Journal articles: 'Off line Model Predictive Control'

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Hayasaki, Yuji, Shingo Nakashima, Yuji Wakasa, Yoshiki Mizukami, and Kanya Tanaka. "Application of off-line model predictive control to pneumatic systems." IFAC Proceedings Volumes 37, no. 12 (August 2004): 813–18. http://dx.doi.org/10.1016/s1474-6670(17)31570-7.

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Kim, Sung Hyun. "Model Predictive Control Algorithm Based on Off-Line Region Dependency." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6308598.

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This paper presents an efficient MPC algorithm for uncertain time-varying systems with input constraints. The main advantage of this algorithm with respect to other published algorithms is to significantly enlarge the size of the stabilization set without regard to computational burdens. Specially, we introduce an off-line region-dependent MPC scheme to avoid the size limitation of the control horizon caused by huge on-line computational burdens. A numerical example is included to illustrate the validity of the result.

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Stoica, C., P. Rodríguez-Ayerbe, and D. Dumur. "Off-line Robustification of Model Predictive Control for Uncertain Multivariable Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 7832–37. http://dx.doi.org/10.3182/20080706-5-kr-1001.01324.

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Ping, Xubin, and Baocang Ding. "Off-line approach to dynamic output feedback robust model predictive control." Systems & Control Letters 62, no. 11 (November 2013): 1038–48. http://dx.doi.org/10.1016/j.sysconle.2013.07.011.

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Pan, Xia, Xiaowei Chen, Qingyu Zhang, and Nannan Li. "Model Predictive Control : A Reinforcement Learning-based Approach." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012058. http://dx.doi.org/10.1088/1742-6596/2203/1/012058.

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Abstract This article proposes a method of model predictive control, which combine the excellent data-driven optimization ability of reinforcement learning and model predictive control to design the controller. Different from the off-line design of MPC, reinforcement learning is based on the adaptation of on-line data to achieve the purpose of control strategy optimization. The reinforcement learning-based model predictive control can improve the control performance effectively. And the numerical simulations are given to demonstrate the effectiveness of the proposed approach.

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Wang, Jing, and Qilun Wang. "Intelligent explicit model predictive control based on machine learning for microbial desalination cells." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 7 (December 11, 2018): 751–63. http://dx.doi.org/10.1177/0959651818816845.

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Aiming at the online control problem of microbial fuel cells, this article presents a class of explicit model-predictive control methods based on the machine learning data model. The proposed method is divided into two stages: off-line design and on-line control. In the off-line design stage, (1) a feasible data set is collected by sampling the admissible state in the feasible region and solving the optimal model predictive control law for each sampling data point off-line, (2) a feasible sample discriminator is constructed based on the support vector machine–based binary classification in order to judge the whether the real sampling state is feasible, and (3) according to the feasible samples and the corresponding optimal control law, the control surface of explicit model predictive controller is constructed based on the machine learning methods. In the on-line control stage, the process data are collected in real time and the feasible control output is calculated by using the trained explicit predictive control surface. Extensive testing and comparison among the different machine learning algorithms, such as artificial neural network, extreme learning machine, Gaussian process regression, and relevance vector machine, are performed on the benchmark model of a class of microbial desalination fuel cells. These results demonstrate that the proposed explicit model predictive control method can avoid the exhausting optimization computing and is easy to realize on-line with good control performance.

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PING, Xu-Bin, and Bao-Cang DING. "An Off-line Approach to Dynamic Output Feedback Robust Model Predictive Control." Acta Automatica Sinica 39, no. 6 (March 25, 2014): 790–98. http://dx.doi.org/10.3724/sp.j.1004.2013.00790.

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Asemani, M. H., V. J. Majd, and M. H. Zibaee Nejad. "An improved off-line approach for output feedback robust model predictive control." IFAC Proceedings Volumes 41, no. 2 (2008): 10886–91. http://dx.doi.org/10.3182/20080706-5-kr-1001.01844.

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Cychowski, Marcin T., and Thomas O'Mahony. "EFFICIENT OFF-LINE SOLUTIONS TO ROBUST MODEL PREDICTIVE CONTROL USING ORTHOGONAL PARTITIONING." IFAC Proceedings Volumes 38, no. 1 (2005): 129–34. http://dx.doi.org/10.3182/20050703-6-cz-1902.00882.

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Wan, Zhaoyang, and Mayuresh V. Kothare. "Robust output feedback model predictive control using off-line linear matrix inequalities." Journal of Process Control 12, no. 7 (October 2002): 763–74. http://dx.doi.org/10.1016/s0959-1524(02)00003-3.

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Zanma, Tadanao, Nobuhiro Asano, and Muneaki Ishida. "Off-line model predictive control of DCDC converter and its experimental verification." IEEJ Transactions on Electrical and Electronic Engineering 4, no. 2 (March 2009): 269–77. http://dx.doi.org/10.1002/tee.20403.

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Ma, Xianghua, Hanqiu Bao, and Ning Zhang. "A New Approach to Off-line Robust Model Predictive Control for Polytopic Uncertain Models." Designs 2, no. 3 (August 20, 2018): 31. http://dx.doi.org/10.3390/designs2030031.

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Concerning the robust model predictive control (MPC) for constrained systems with polytopic model characterization, some approaches have already been given in the literature. One famous approach is an off-line MPC, which off-line finds a state-feedback law sequence with corresponding ellipsoidal domains of attraction. Originally, each law in the sequence was calculated by fixing the infinite horizon control moves as a single state feedback law. This paper optimizes the feedback law in the larger ellipsoid, foreseeing that, if it is applied at the current instant, then better feedback laws in the smaller ellipsoids will be applied at the following time. In this way, the new approach achieves a larger domain of attraction and better control performance. A simulation example shows the effectiveness of the new technique.

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Ettefagh, Massoud Hemmasian, Mahyar Naraghi, and Farzad Towhidkhah. "Position Control of a Flexible Joint via Explicit Model Predictive Control: An Experimental Implementation." Emerging Science Journal 3, no. 3 (June 3, 2019): 146–56. http://dx.doi.org/10.28991/esj-2019-01177.

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This paper experimentally controls a flexible joint via explicit model predictive control (Explicit MPC) method. The scheme divides the state space into different partitions, then solves the associated multi parametric optimization in off-line computations. The result stores in a look-up table to be used in on-line algorithm. First, the state space equations of the flexible joint are derived and linearized around the working point. Then, in order to meet the plant’s specifications, desired performance and the limitation of processor/memory, the constraints, weights, sampling time and prediction horizon are determined for the system. Finally, the algorithm is applied on the experimental plant. Numerous simulations, the result of the experiment and comparison with other methods confirmed that the method was able to control the vibrations of the constrained flexible joint.

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Liberati, Francesco, Chiara Maria Francesca Cirino, and Andrea Tortorelli. "Energy-Aware Model Predictive Control of Assembly Lines." Actuators 11, no. 6 (June 20, 2022): 172. http://dx.doi.org/10.3390/act11060172.

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This paper presents a model predictive approach to the energy-aware control of tasks’ execution in an assembly line. The proposed algorithm takes into account both the need for optimizing the assembly line operations (in terms of the minimization of the total cycle time) and that of optimizing the energy consumption deriving from the operations, by exploiting the flexibility added by the presence of a local source of renewable energy (a common scenario of industries that are often equipped, e.g., with photovoltaic plants) and, possibly, also exploiting an energy storage plant. The energy-related objectives we take into account refer to the minimization of the energy bill and the minimization of the peaks in the power injected and absorbed from the grid (which is desirable also from the perspective of the network operator). We propose a mixed-integer linear formulation of the optimization problem, through the use of H-infinite norms, instead of the quadratic ones. Simulation results show the effectiveness of the proposed algorithm in finding a trade-off that allows keeping at a minimum the cycle time, while saving on the energy bill and reducing peak powers.

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Zhao, Min, and Yan Xia Jiang. "Off-Line Robust MPC Algorithm for VAV Air-Conditioning Systems." Advanced Materials Research 846-847 (November 2013): 293–96. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.293.

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The efficient temperature control of variable air volume (VAV) air-conditioning system can greatly reduce the energy consumption. In order to due with system constraints and uncertainties, and guarantee the closed-loop stability, an off-line min-max robust model predictive control algorithm is presented for the temperature control of a VAV system. As the first-order-plus-time-delay model of system is described by a polyhedral described uncertain model, the off-line ellipsoidal invariant set based robust MPC algorithm is employed for the controller design. Simulation results show efficiency of the proposed control algorithm implemented in the temperature control of a VAV system, which can enhance robustness, and satisfy the constraints.

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Yan, Yan, and Longge Zhang. "Robust Model Predictive Control with Almost Zero Online Computation." Mathematics 9, no. 3 (January 26, 2021): 242. http://dx.doi.org/10.3390/math9030242.

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This paper provides a strategy for the problem of robust model predictive control of constrained, discrete-time systems with state and output disturbances. Using the linear matrix inequality (LMI) method, the nested geometric proportion asymptotically stable ellipsoid (GPASE) strategy is designed off-line, and then the designed shrinking ellipsoids strategy assures the system converges on the equivalent with an exponential convergence velocity. The biggest advantage of this method is the online computation is almost reduced to zero, which makes it possible to apply the designed control scheme not only to plants with slowly varying parameters, but also to fast ones. Finally, a simulation example shows the validity of the proposed technique.

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Chen, Minghao, Zuhua Xu, and Jun Zhao. "Triple-Mode Model Predictive Control Using Future Target Information." Processes 8, no. 1 (January 2, 2020): 54. http://dx.doi.org/10.3390/pr8010054.

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In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) the future target information feedforward, (ii) the output feedback, and (iii) the extra degrees of freedom for constraint satisfaction. The first two parts of the control law are off-line designed through unconstrained MPC, and the optimal future trajectory horizon is obtained by golden section search based on the integral of squared error (ISE) criterion. The final part is calculated by the on-line MPC algorithm aiming to satisfy constraints. Furthermore, we analyze the feasibility and convergence properties of the proposed algorithm. The method is demonstrated by the simulation of the shell fundamental control problem and also tested on the coordinated control problem in the power plant. The test results show that the proposed algorithm can increase tracking performance dramatically due to the proper selection of future trajectory horizon.

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Wan, Zhaoyang, and Mayuresh V. Kothare. "An efficient off-line formulation of robust model predictive control using linear matrix inequalities." Automatica 39, no. 5 (May 2003): 837–46. http://dx.doi.org/10.1016/s0005-1098(02)00174-7.

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Cembrano, G., J. Quevedo, V. Puig, R. Pérez, J. Figueras, J. M. Verdejo, I. Escaler, et al. "PLIO: a generic tool for real-time operational predictive optimal control of water networks." Water Science and Technology 64, no. 2 (July 1, 2011): 448–59. http://dx.doi.org/10.2166/wst.2011.431.

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This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation).

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Zarzycki, Krzysztof, and Maciej Ławryńczuk. "Fast Real-Time Model Predictive Control for a Ball-on-Plate Process." Sensors 21, no. 12 (June 8, 2021): 3959. http://dx.doi.org/10.3390/s21123959.

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This work is concerned with an original ball-on-plate laboratory process. First, a simplified process model based on state–space process description is derived. Next, a fast state–space MPC algorithm is discussed. Its main advantage is computational simplicity: the manipulated variables are found on-line using explicit formulas with parameters calculated off-line; no real-time optimization is necessary. Software and hardware implementation details of the considered MPC algorithm using the STM32 microcontroller are presented. Tuning of the fast MPC algorithm is discussed. To show the efficacy of the MPC algorithm, it is compared with the classical PID and LQR controllers.

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Tang, Xiaoming, Hongchun Qu, Ping Wang, and Meng Zhao. "Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays." ISA Transactions 55 (March 2015): 135–44. http://dx.doi.org/10.1016/j.isatra.2014.11.007.

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Hyun Kim, Chang, and Houng Kun Joung. "Model-based predictive control of dc-dc converter for EV applications." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 308. http://dx.doi.org/10.14419/ijet.v7i2.12.11312.

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Background/Objectives: The power performance of electric vehicle chargers depends on the control efficiency of the power converters with on-board and off-board types. In this paper, a new control method is proposed for power converter of fast electric vehicle chargers in order to improve the power efficiency.Methods/Statistical analysis: The proposed control method is the optimal control to minimize the performance objectives from the predicted output, based on the system model. The discretized model of DC-DC converter with sampling time is derived by using lifting operation for taking into account with the desired prediction time.Findings: The existing conventional controllers are obtained by off-line optimal solution and applied to the systems. Once the control gain is determined, the controller is able to reflect the system response at the real-time.Improvements/Applications: The proposed control method has advantages to deal with system performances at real-time and the control actuation is updated every sampling time via the derived mathematical model. It can be directly applicable to real electric vehicle charger systems in industry.

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Bumroongsri, P., and S. Kheawhom. "An ellipsoidal off-line robust model predictive control strategy for uncertain polytopic discrete-time systems." IFAC Proceedings Volumes 45, no. 25 (2012): 268–73. http://dx.doi.org/10.3182/20120913-4-it-4027.00018.

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Bumroongsri, P., and S. Kheawhom. "The Polyhedral Off-line Robust Model Predictive Control Strategy for Uncertain Polytopic Discrete-time Systems." IFAC Proceedings Volumes 45, no. 15 (2012): 655–60. http://dx.doi.org/10.3182/20120710-4-sg-2026.00017.

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Bumroongsri, P., and S. Kheawhom. "Off-Line Robust Constrained MPC for Linear Time-Varying Systems with Persistent Disturbances." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/936093.

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An off-line robust constrained model predictive control (MPC) algorithm for linear time-varying (LTV) systems is developed. A novel feature is the fact that both model uncertainty and bounded additive disturbance are explicitly taken into account in the off-line formulation of MPC. In order to reduce the on-line computational burdens, a sequence of explicit control laws corresponding to a sequence of positively invariant sets is computed off-line. At each sampling time, the smallest positively invariant set containing the measured state is determined and the corresponding control law is implemented in the process. The proposed MPC algorithm can guarantee robust stability while ensuring the satisfaction of input and output constraints. The effectiveness of the proposed MPC algorithm is illustrated by two examples.

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Li, Donglin, Tongwen Chen, Horacio J. Marquez, and R. Kent Gooden. "VARIANCE CONSTRAINED MODEL PREDICTIVE CONTROL AND APPLICATION IN LIFE EXTENDING CONTROL." Transactions of the Canadian Society for Mechanical Engineering 29, no. 2 (June 2005): 297–314. http://dx.doi.org/10.1139/tcsme-2005-0018.

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The objective of life extending control (LEC), also known as damage mitigating control, is to design a controller to achieve a good trade-off between structural durability and dynamic performance in a system. In this paper, continuum fatigue damage theory for a boiler-turbine system is discussed. To reduce the accumulated damage, a variance constrained model predictive control (VCMPC) problem is developed and an algorithm via linear matrix inequalities (LMIs) is derived. This algorithm is simpler than previous ones. The controller obtained by this algorithm can assign the resultant closed-loop poles in a prescribed region. Finally, we apply the algorithm in a boiler-turbine system.

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Chatrattanawet, Narissara, Soorathep Kheawhom, Yong-Song Chen, and Amornchai Arpornwichanop. "Design and Implementation of the Off-Line Robust Model Predictive Control for Solid Oxide Fuel Cells." Processes 7, no. 12 (December 3, 2019): 918. http://dx.doi.org/10.3390/pr7120918.

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An off-line robust linear model predictive control (MPC) using an ellipsoidal invariant set is synthesized based on an uncertain polytopic approach and then implemented to control the temperature and fuel in a direct internal reforming solid oxide fuel cell (SOFC). The state feedback control is derived by minimizing an upper bound on the worst-case performance cost. The simulation results indicate that the synthesized robust MPC algorithm can control and guarantee the stability of the SOFC; although there are uncertainties in some model parameters, it can keep both the temperature and fuel at their setpoints.

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Chu, Zhenzhong, Da Wang, and Fei Meng. "An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles." Machines 9, no. 5 (May 20, 2021): 105. http://dx.doi.org/10.3390/machines9050105.

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An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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Peng, Haijun, Yuzhen Chen, Erfang Li, Sheng Zhang, and Biaosong Chen. "Explicit expression-based practical model predictive control implementation for large-scale structures with multi-input delays." Journal of Vibration and Control 24, no. 12 (January 30, 2017): 2605–20. http://dx.doi.org/10.1177/1077546316689341.

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In this paper, two practical model predictive control (MPC) implementation algorithms with multi-input delay (NFMPCMID1 and NFMPCMID2) are developed in discrete-time formulation for vibration control of large-scale structures. By introducing a particular augmented state vector, the controlled dynamic equation with multi-input delay is transformed into the standard form without any explicit time delay. Because of no approximation for multi-input delay involved, the system performance and stability are easily guaranteed. In order to solve the computation efficiency and memory requirement for large-scale structure, a novel explicit expression form of Newmark-β method is derived, from which the future states can be easily predicted without computing matrix exponential and its integration. By applying this explicit expression form into MPC, the control input of NFMPCMID1 method can be computed by some matrix–matrix multiplications, and also the control input of NFMPCMID2 method can be computed just by two off-line transient analyses and one on-line transient analysis at every sampling instant on the structure. For no computation of matrix exponential and its integration in NFMPCMID1 and NFMPCMID2 methods, the off-line computation efficiency is greatly improved, and the memory requirement is greatly reduced, especially for the NFMPCMID2 method. In additional, due to the small amount of on-line computation, the on-line computation efficiency is also guaranteed. At last, the stability, feasibility and efficiency of the proposed methods are verified by several typical numerical examples.

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Qi, Haitao, Gary M. Bone, and Yile Zhang. "Position Control of Pneumatic Actuators Using Three-Mode Discrete-Valued Model Predictive Control." Actuators 8, no. 3 (July 19, 2019): 56. http://dx.doi.org/10.3390/act8030056.

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A novel discrete-valued model-predictive control (DVMPC) algorithm termed DVMPC2 for the position control of pneumatic actuators using inexpensive on/off valves is presented. DVMPC2 includes a more flexible cost function, an improved prediction strategy, and other improvements. The actuator is a double-acting cylinder with two on/off solenoid poppet valves connected to each chamber. To reduce the switching frequency and prolong the valve life, DVMPC2 directly switches the valves when necessary, instead of using relatively high-frequency pulse-width modulation. Experimental comparisons are made with the state-of-the-art sliding-mode control (SMC) algorithm and the previous DVMPC algorithm. The comparisons are based on the five performance metrics: integral of time-weighted absolute error (ITAE), root mean square error (RMSE), overshoot (OS), steady-state error (SSE), and valve switches per second (SPS). The robustness is evaluated by increasing and decreasing the total mass of the moving components while keeping the controller parameters constant. The experimental results show that the proposed algorithm is superior to the previous DVMPC and outperformed SMC by a wide margin. Specifically, DVMPC2 reduced ITAE by 80%, RMSE by 52%, OS by 43%, and SPS by 20% relative to SMC. There was no clear winner in terms of SSE.

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Bai, Guoxing, Chen Liang, Yu Meng, Li Liu, Weidong Luo, and Qing Gu. "Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control." World Electric Vehicle Journal 10, no. 4 (November 1, 2019): 72. http://dx.doi.org/10.3390/wevj10040072.

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Obstacle avoidance is a core part of the autonomous driving of off-road vehicles, such as semi-trailers. Due to the long length of semi-trailers, the traditional obstacle avoidance controller based on the circumcircle model can ensure that there is no collision between the semi-trailer and the obstacle, but it also greatly reduces the passable area. To solve this problem, we propose a new obstacle avoidance model. In this model, the distance between the obstacle and the middle line of semi-trailers is used as the indicator of obstacle avoidance. Based on this model, we design a new obstacle avoidance controller for semi-trailers. The simulation results show that the proposed controller can ensure that no collision occurs between the semi-trailer and the obstacle. The minimum distance between the obstacle center and the semi-trailer body trajectory is greater than the sum of the obstacle radius and the safety margin. Compared with the traditional obstacle avoidance controller based on the circumcircle model, the proposed controller greatly reduces the error between the semi-trailer and the reference path during obstacle avoidance.

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Zhang, Ri-dong, and Shu-qing Wang. "Predictive control of a class of bilinear systems based on global off-line models." Journal of Zhejiang University-SCIENCE A 7, no. 12 (December 2006): 1984–88. http://dx.doi.org/10.1631/jzus.2006.a1984.

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Wang, S. X., C. H. Yang, and Y. Dong. "Multi-Objective Optimization Nonlinear Predictive Control Based on Small-World Optimization." Advanced Materials Research 562-564 (August 2012): 2116–19. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2116.

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Due to fast receding horizon speed and global optimization of small-world optimization algorithm with real-coding (RSW), a new nonlinear multi-objective predictive controller was presented based on RSW and neural network (NN) identification trained by BP. NN model which was obtained by off-line identification was used to predict the present and future output of the plant, and RSW was applied to receding horizon control. Finally, an application to 500MW unit load control system with multi-objective optimization was given, and simulation results indicated the effectiveness of this new approach.

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Lv, Ruixin, Zhongyuan Yuan, Bo Lei, Jiacheng Zheng, and Xiujing Luo. "Model Predictive Control with Adaptive Building Model for Heating Using the Hybrid Air-Conditioning System in a Railway Station." Energies 14, no. 7 (April 5, 2021): 1996. http://dx.doi.org/10.3390/en14071996.

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A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings.

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Wang, Xiwen, and Tongli Lu. "Offline model predictive control approach to micro-slip control in gearshifts of dual clutch transmission." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 236, no. 1 (November 3, 2021): 84–98. http://dx.doi.org/10.1177/14644193211052136.

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Dual clutch transmission can avoid some noise vibration and harshness issues caused by other transmissions with single clutch. And applying micro-slip control on clutches can further improve the gearshift performance of transmission compared to the lock-up control. Considering the real-time characteristic of vehicle control, an offline model predictive controller designed by multi-parameter quadratic programming was creatively applied in dual clutch transmission to obtain both clutch torque at the same time with optimal control algorithm. In this way, while realizing the micro-slip state of the clutches, the fast response speed can be realized through the off-line controller, which makes it more feasible and practical for transmission control. A six degrees of freedom vehicle powertrain system model was built in MATLAB/Simulink to simulate the proposed control algorithm. The simulation results show that the micro-slip control avoid the negative torque compared to the lock-up control, which leads to a smoother shift process. In addition, compared with the proportional–integral–derivative micro-slip controller, the offline model predictive controller can achieve more stable control effects with less output torque fluctuation and shorter gearshift time.

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Wang, Wei, Gaoshuai Shen, Run Min, Qiaoling Tong, Qiao Zhang, and Zhenglin Liu. "State Switched Discrete-Time Model and Digital Predictive Voltage Programmed Control for Buck Converters." Energies 13, no. 13 (July 3, 2020): 3451. http://dx.doi.org/10.3390/en13133451.

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Switched mode power converters are nonlinear systems, and it is a constant challenge to improve their modeling accuracy and control performance. In this paper, a State Switched Discrete-time Model (SSDM) is proposed, which achieves a higher accuracy at a high frequency than that of conventional state averaged models. Instead of averaging the converter states for approximation, the states within each switching cycle are considered in the modeling. Based on total differential equations of switching-ON and switching-OFF durations, the inductor current and output voltage within a cycle are accurately calculated, which derives the SSDM. Furthermore, a Digital Predictive Voltage Programmed (DPVP) control strategy is derived through the SSDM. Through voltage prediction, a suitable duty ratio is calculated that regulates the output voltage to its reference value in the minimum switching cycles. In this way, the converter achieves a very fast load/line transient response and reference tracking speed, and it exhibits a high stability under deviated inductance. Finally, the accuracy of SSDM and the system stability are proved by frequency response analyses and experiments.

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Rosa, Fabiano C., and Edson Bim. "A Constrained Non-Linear Model Predictive Controller for the Rotor Flux-Oriented Control of an Induction Motor Drive." Energies 13, no. 15 (July 31, 2020): 3899. http://dx.doi.org/10.3390/en13153899.

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Predictive controllers have been extensively studied and applied to electrical drives, mainly because they provide fast dynamic responses and are suitable for multi-variable control and non-linear systems. Many approaches perform the prediction and optimization process on-line, which requires a high computational capacity for fast dynamics, such as, for example, the control of AC electric motors. Due to the complexity of embedding constraints in controller design, which demands a high computational capacity to solve the optimization problem, off-line approaches are one of the choices to overcome this problem. However, these strategies do not deal with the inherent constraints of the drive system, which significantly simplifies the design of the controller. This paper proposes a non-linear and multi-variable predictive controller to control the speed and rotor flux of an induction motor, where the constraints are treated after the controller design. Besides dealing with the constraints of the electric drive system, our proposal allows increasing the stability of the system when the model does not incorporate disturbances and when parameter incompatibilities occur. Several computer simulations and experimental tests were performed to evaluate the behavior of the proposed controller, showing good performance to track the controlled variables under normal operating conditions, under load disturbances, parametric incompatibility, and at a very low rotor speed.

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Luciani, Sara, Angelo Bonfitto, Nicola Amati, and Andrea Tonoli. "Model predictive control for comfort optimization in assisted and driverless vehicles." Advances in Mechanical Engineering 12, no. 11 (November 2020): 168781402097453. http://dx.doi.org/10.1177/1687814020974532.

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This paper presents a method to design a Model Predictive Control to maximize the passengers’ comfort in assisted and self-driving vehicles by achieving lateral and longitudinal dynamic. The weighting parameters of the MPC are tuned off-line using a Genetic Algorithm to simultaneously maximize the control performance in the tracking of speed profile, lateral deviation and relative yaw angle and to optimize the comfort perceived by the passengers. To this end, two comfort evaluation indexes extracted by ISO 2631 are used to evaluate the amount of vibration transmitted to the passengers and the probability to experience motion sickness. The effectiveness of the method is demonstrated using simulated experiments conducted on a subcompact crossover vehicle. The control tracking performance produces errors lower than 0.1 m for lateral deviation, 0.5° for relative yaw angle and 1.5 km/h for the vehicle speed. The comfort maximization results in a low percentage of people who may experience nausea (below 5%) and in a low value of equivalent acceleration perceived by the passenger (below 0.315 [Formula: see text]“not uncomfortable” by ISO 2631). The robustness at variations of vehicle parameters, namely vehicle mass, front and rear cornering stiffness and mass distribution, is evaluated through a sensitivity analysis.

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Ma, Yu, and Yuanli Cai. "Scheduled Composite Off-Line Output Feedback Model Predictive Control for a Constrained Hypersonic Vehicle Using Polyhedral Invariant Sets." Journal of Aerospace Engineering 31, no. 4 (July 2018): 04018035. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000856.

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Bumroongsri, P., and S. Kheawhom. "An ellipsoidal off-line model predictive control strategy for linear parameter varying systems with applications in chemical processes." Systems & Control Letters 61, no. 3 (March 2012): 435–42. http://dx.doi.org/10.1016/j.sysconle.2012.01.003.

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Manimala, B. J., D. J. Walker, G. D. Padfield, M. Voskuijl, and A. W. Gubbels. "Rotorcraft simulation modelling and validation for control law design." Aeronautical Journal 111, no. 1116 (February 2007): 77–88. http://dx.doi.org/10.1017/s0001924000001780.

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AbstractThis paper describes the development and validation of a high fidelity simulation model of the Bell 412 helicopter for handling qualities and flight control investigations. The base-line model features a rigid, articulated blade-element formulation of the main rotor, with flap and lag degrees of freedom. The Bell 412 HP engine/governor dynamics are represented by a second-order system. Other key features of the base-line model include a finite-state dynamic inflow model and lag damper dynamics. The base-line model gives excellent agreement with flight-test data over the speed range 15-120kt for on-axis responses. Prediction of off-axis responses is less accurate. Several model enhancement options were introduced to obtain an improved off-axis response. It is shown that the pitch/roll off-axis responses in transient manoeuvres can be improved significantly by including wake geometry distortion effects in the Peters-He finite-state dynamic inflow model.

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Gautam, A., and Y. C. Soh. "Constraint-softening in model predictive control with off-line-optimized admissible sets for systems with additive and multiplicative disturbances." Systems & Control Letters 69 (July 2014): 65–72. http://dx.doi.org/10.1016/j.sysconle.2014.04.006.

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Zamudio Lara, Jesús Miguel, Laurent Dewasme, Héctor Hernández Escoto, and Alain Vande Wouwer. "Parameter Estimation of Dynamic Beer Fermentation Models." Foods 11, no. 22 (November 11, 2022): 3602. http://dx.doi.org/10.3390/foods11223602.

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In this study, two dynamic models of beer fermentation are proposed, and their parameters are estimated using experimental data collected during several batch experiments initiated with different sugar concentrations. Biomass, sugar, ethanol, and vicinal diketone concentrations are measured off-line with an analytical system while two on-line immersed probes deliver temperature, ethanol concentration, and carbon dioxide exhaust rate measurements. Before proceeding to the estimation of the unknown model parameters, a structural identifiability analysis is carried out to investigate the measurement configuration and the kinetic model structure. The model predictive capability is investigated in cross-validation, in view of opening up new perspectives for monitoring and control purposes. For instance, the dynamic model could be used as a predictor in receding-horizon observers and controllers.

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Li, Jianhua, Jianfeng Sun, Liqun Liu, and Jiasheng Xu. "Model predictive control for the tracking of autonomous mobile robot combined with a local path planning." Measurement and Control 54, no. 9-10 (October 24, 2021): 1319–25. http://dx.doi.org/10.1177/00202940211043070.

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This article presents a model predictive control (MPC) coupled with an artificial potential field (APF) to resolve the trajectory tracking while considering the obstacle avoidance. In this article, the obstacle avoidance problem is solved by a local path planning based on the artificial potential field by constructing a virtual goal. A virtual goal is generated to produce an attractive force to guide the mobile robot to a collision-free space. The planned path is controlled by a proportional–integral–derivative (PID) controller to avoid collision. After arriving at the virtual goal, an off-line explicit MPC is calculated to obtain the optimal control inputs to track the reference trajectory. The simulation results show that the proposed method can be applied to control the mobile robot in the environment with one obstacle.

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Meyer, Stefan P., Christian J. Bernauer, Sophie Grabmann, and Michael F. Zaeh. "Design, evaluation, and implementation of a model-predictive control approach for a force control in friction stir welding processes." Production Engineering 14, no. 4 (June 30, 2020): 473–89. http://dx.doi.org/10.1007/s11740-020-00969-6.

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Abstract Friction press joining is an innovative joining process for bonding plastics and metals without additives in an overlap configuration. A model-based approach for the design of an axial force controller for friction press joining is presented in this paper. A closed-loop control was set up on the machining center, in which the plunge depth was used as the controlling variable. In order to support the controller development, a nonparametric dynamic process model was developed via a data-based system identification. Subsequently, various control concepts were designed off-line and verified on the actual system. The most promising ones, a proportional controller, a controller created with the pole placement method, and a model predictive controller, were selected for further investigations. The three controllers were re-evaluated and compared by means of a defined input of disturbance variables and reference variables. The model predictive control (MPC) approach as well as the proportional controller were also tested for model uncertainties. For this purpose, different material combinations were joined using the different controllers. Thereby, it was shown that the MPC controller resulted in smaller standard deviations when encountering large model uncertainties. The investigations demonstrated the high potential of friction press joining of plastic components with metals. The results form the basis for future research, whereby the force can be specified as an additional input parameter instead of the plunge depth.

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Assegie, Tsehay Admassu, Thulasi Karpagam, Radha Mothukuri, Ravulapalli Lakshmi Tulasi, and Minychil Fentahun Engidaye. "Extraction of human understandable insight from machine learning model for diabetes prediction." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 1126–33. http://dx.doi.org/10.11591/eei.v11i2.3391.

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Explaining the reason for model’s output as diabetes positive or negative is crucial for diabetes diagnosis. Because, reasoning the predictive outcome of model helps to understand why the model predicted an instance into diabetes positive or negative class. In recent years, highest predictive accuracy and promising result is achieved with simple linear model to complex deep neural network. However, the use of complex model such as ensemble and deep learning have trade-off between accuracy and interpretability. In response to the problem of interpretability, different approaches have been proposed to explain the predictive outcome of complex model. However, the relationship between the proposed approaches and the preferred approach for diabetes prediction is not clear. To address this problem, the authors aimed to implement and compare existing model interpretation approaches, local interpretable model agnostic explanation (LIME), shapely additive explanation (SHAP) and permutation feature importance by employing extreme boosting (XGBoost). Experiment is conducted on diabetes dataset with the aim of investigating the most influencing feature on model output. Overall, experimental result evidently appears to reveal that blood glucose has the highest impact on model prediction outcome.

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Bahmani, Hamidreza, Farhad Bayat, and Mohamadjavad Golchin. "Wind turbines power regulation using a low-complexity linear parameter varying-model predictive control approach." Transactions of the Institute of Measurement and Control 42, no. 1 (July 18, 2019): 81–93. http://dx.doi.org/10.1177/0142331219862078.

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In this paper, the model predictive control (MPC) approach is utilized to stabilize the output power of the wind turbines at the region above the rated wind speed. The controller is designed based on two different approaches and results have been compared. First, by putting the advantages of the MPC approach into practice, the optimal output power regulation of the wind turbine is obtained using a control oriented linear parameter varying (LPV) model of the wind turbine. However, this method inherently requires high computational cost and thus powerful hardware and processors. To cope with this limitation, an efficient suboptimal approach is proposed that significantly reduces the online computational complexity of the controller. In this approach, the main part of the controller design procedure is done off-line prior to the closed-loop wind turbine power generation and a set of optimal controllers were designed using the MPC scheme. Then, a convex combination of the calculated controllers is used for online power regulation of the wind turbine. It is noted that the selected wind turbine is a horizontal axis wind turbine operating at various speeds ranging from 10-25 m/s. Finally, using a set of simulation results we investigate the performance of the proposed approach.

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Zavvar Sabegh, Mohammad Reza, and Chris Bingham. "Model Predictive Control with Binary Quadratic Programming for the Scheduled Operation of Domestic Refrigerators." Energies 12, no. 24 (December 7, 2019): 4649. http://dx.doi.org/10.3390/en12244649.

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The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to schedule the operation of widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, the paper proposes the real-time estimation of the thermal mass of each refrigerator in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands commensurate with accommodating food safety standards. A custom model predictive control (MPC) scheme is devised using binary quadratic programming to realize the scheduling methodology which is implemented through IoT hardware (based on a NodeMCU). Benefits afforded by the proposed scheme are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can i) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e., peak load levelling), ii) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and iii) contribute to a widely distributed aggregated load shed for demand side response purposes in order to aid grid stability. Importantly, the number of compressor starts per hour for each refrigerator is also bounded as an inherent design feature of the algorithm so as not to operationally overstress the compressors and reduce their lifetime. Experimental trials show that such co-ordinated operation of refrigerators can reduce energy consumption by ~30% whilst also providing peak load levelling, thereby affording benefits to both individual consumers as well as electrical network suppliers.

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Peng, Kai, Ding Fan, Ran Ran Wu, and Yu Qiang Teng. "Active Predictive Control of Turbine Tip Clearance for Aero-Engine." Applied Mechanics and Materials 672-674 (October 2014): 1531–34. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1531.

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Active control of turbine blade tip clearance continues to be a concern in design and control of gas turbines. Ever increasing demands for improved efficiency and higher operating temperatures require more stringent tolerances on turbine tip clearance. In this paper, a turbine tip clearance control apparatus and a model of turbine tip clearance are proposed. The active clearance control (ACC) of aero-engine turbine tip clearance is evaluated in a lapse-rate take-off transient, along with the comparative and quantitative analysis. The results show that the resultant active tip clearance control system has favorable steady-state and dynamic performance and benefits of increased efficiency, reduced specific fuel consumption, and additional service life.

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Nassourou, Mohamadou, Joaquim Blesa, and Vicenç Puig. "Robust Economic Model Predictive Control Based on a Zonotope and Local Feedback Controller for Energy Dispatch in Smart-Grids Considering Demand Uncertainty." Energies 13, no. 3 (February 5, 2020): 696. http://dx.doi.org/10.3390/en13030696.

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Electrical smart grids are complex MIMO systems whose operation can be noticeably affected by the presence of uncertainties such as load demand uncertainty. In this paper, based on a restricted representation of the demand uncertainty, we propose a robust economic model predictive control method that guarantees an optimal energy dispatch in a smart micro-grid. Load demands are uncertain, but viewed as bounded. The proposed method first decomposes control inputs into dependent and independent components, and then tackles the effect of demand uncertainty by tightening the system constraints as the uncertainty propagates along the prediction horizon using interval arithmetic and local state feedback control law. The tightened constraints’ upper and lower limits are computed off-line. The proposed method guarantees stability through a periodic terminal state constraint. The method is faster and simpler compared to other approaches based on Closed-loop min–max techniques. The applicability of the proposed approach is demonstrated using a smart micro-grid that comprises a wind generator, some photovoltaic (PV) panels, a diesel generator, a hydroelectric generator and some storage devices linked via two DC-buses, from which load demands can be adequately satisfied.

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Journal articles on the topic 'Off line Model Predictive Control'

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