Journal articles on the topic 'Hierarchical quadratic programming'
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Kumar, Suchet, and Madhuchanda Rakshit. "A Solution of Fuzzy Multilevel Quadratic Fractional Programming Problem through Interactive Fuzzy Goal Programming Approach." International Journal of Fuzzy Mathematical Archive 13, no. 01 (2017): 83–97. http://dx.doi.org/10.22457/ijfma.v13n1a9.
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The purpose of this paper is to study the fuzzy multilevel quadratic fractional programming problem through fuzzy goal programming procedure. A fuzzy multilevel quadratic fractional programming problem is a type of hierarchical programming problem which contains fuzzy parameters as coefficients of cost in objective function, the resources and the technological coefficients. Here, we are considering those fuzzy parameters as the triangular fuzzy numbers. Firstly, we are transferring the fuzzy multilevel quadratic fractional programming problem into a deterministic multilevel multiobjective quadratic fractional programming problem by using Zadeh extension principle. Then, an interactive fuzzy goal programming procedure is used to solve this equivalent deterministic multiobjective multilevel quadratic fractional programming problem by using respective membership functions. An illustrative numerical example for fuzzy four level quadratic fractional programming problem is provided to reveal the practicability of the proposed method.
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Pérez-Villeda, Héctor M., Gustavo Arechavaleta, and América Morales-Díaz. "Multi-vehicle coordination based on hierarchical quadratic programming." Control Engineering Practice 94 (January 2020): 104206. http://dx.doi.org/10.1016/j.conengprac.2019.104206.
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Escande, Adrien, Nicolas Mansard, and Pierre-Brice Wieber. "Hierarchical quadratic programming: Fast online humanoid-robot motion generation." International Journal of Robotics Research 33, no. 7 (May 2014): 1006–28. http://dx.doi.org/10.1177/0278364914521306.
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Shi, Xuanyang, Junyao Gao, Yizhou Lu, Dingkui Tian, and Yi Liu. "Biped Walking Based on Stiffness Optimization and Hierarchical Quadratic Programming." Sensors 21, no. 5 (March 2, 2021): 1696. http://dx.doi.org/10.3390/s21051696.
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The spring-loaded inverted pendulum model is similar to human walking in terms of the center of mass (CoM) trajectory and the ground reaction force. It is thus widely used in humanoid robot motion planning. A method that uses a velocity feedback controller to adjust the landing point of a robot leg is inaccurate in the presence of disturbances and a nonlinear optimization method with multiple variables is complicated and thus unsuitable for real-time control. In this paper, to achieve real-time optimization, a CoM-velocity feedback controller is used to calculate the virtual landing point. We construct a touchdown return map based on a virtual landing point and use nonlinear least squares to optimize spring stiffness. For robot whole-body control, hierarchical quadratic programming optimization is used to achieve strict task priority. The dynamic equation is given the highest priority and inverse dynamics are directly used to solve it, reducing the number of optimizations. Simulation and experimental results show that a force-controlled biped robot with the proposed method can stably walk on unknown uneven ground with a maximum obstacle height of 5 cm. The robot can recover from a 5 Nm disturbance during walking without falling.
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Koung, Daravuth, Olivier Kermorgant, Isabelle Fantoni, and Lamia Belouaer. "Cooperative Multi-Robot Object Transportation System Based on Hierarchical Quadratic Programming." IEEE Robotics and Automation Letters 6, no. 4 (October 2021): 6466–72. http://dx.doi.org/10.1109/lra.2021.3092305.
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Kim, Sanghyun, Keunwoo Jang, Suhan Park, Yisoo Lee, Sang Yup Lee, and Jaeheung Park. "Continuous Task Transition Approach for Robot Controller Based on Hierarchical Quadratic Programming." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 1603–10. http://dx.doi.org/10.1109/lra.2019.2896769.
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Tian, Dingkui, Junyao Gao, Xuanyang Shi, Yizhou Lu, and Chuzhao Liu. "Vertical Jumping for Legged Robot Based on Quadratic Programming." Sensors 21, no. 11 (May 25, 2021): 3679. http://dx.doi.org/10.3390/s21113679.
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The highly dynamic legged jumping motion is a challenging research topic because of the lack of established control schemes that handle over-constrained control objectives well in the stance phase, which are coupled and affect each other, and control robot’s posture in the flight phase, in which the robot is underactuated owing to the foot leaving the ground. This paper introduces an approach of realizing the cyclic vertical jumping motion of a planar simplified legged robot that formulates the jump problem within a quadratic-programming (QP)-based framework. Unlike prior works, which have added different weights in front of control tasks to express the relative hierarchy of tasks, in our framework, the hierarchical quadratic programming (HQP) control strategy is used to guarantee the strict prioritization of the center of mass (CoM) in the stance phase while split dynamic equations are incorporated into the unified quadratic-programming framework to restrict the robot’s posture to be near a desired constant value in the flight phase. The controller is tested in two simulation environments with and without the flight phase controller, the results validate the flight phase controller, with the HQP controller having a maximum error of the CoM in the x direction and y direction of 0.47 and 0.82 cm and thus enabling the strict prioritization of the CoM.
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Abohany, A. A., Rizk Masoud Rizk-Allah, Diana T. Mosa, and Aboul Ella Hassanien. "A Novel Approach for Solving a Fully Rough Multi-Level Quadratic Programming Problem and Its Application." International Journal of Service Science, Management, Engineering, and Technology 11, no. 4 (October 2020): 137–65. http://dx.doi.org/10.4018/ijssmet.2020100109.
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The most widely used actions and decisions of the real-world tasks frequently appear as hierarchical systems. To deal with these systems, the multi-level programming problem presents the most flourished technique. However, practical situations involve some the impreciseness regarding some decisions and performances; RST provides a vital role by considering the lower and upper bounds of any aspect of uncertain decision. By preserving the advantages of it, in the present study, solving fully rough multi-level quadratic programming problems over the variables, parameters of the objective functions, and the constraints such as rough intervals are focused on. The proposed approach incorporates the interval method, slice-sum method, Frank and Wolfe algorithm, and the decomposition algorithm to reach optimal values as rough intervals. The proposed is validated by an illustrative example, and also environmental-economic power dispatch is investigated as a real application. Finally, the proposed approach is capable of handling the fully rough multi-level quadratic programming models.
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Li, Gang, Hai Lan Han, Chao Wang, and Gao Feng Ma. "Study on Fuzzy PI Control of Vehicle Yaw Moment Based on Optimal Allocation of Braking Forces." Applied Mechanics and Materials 556-562 (May 2014): 2293–96. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2293.
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For vehicle direct yaw moment control (DYC) ,the additional yaw moment decision method based on the fuzzy PI control and optimal allocation method of yaw moment based on quadratic programming are studied. Yaw moment control adopts hierarchical control method.The fuzzy PI controller and brake force optimization distributor are designed. The control method is verified through the Matlab/Simulink and CarSim co-simulation experiment.The results show that the control method can make the vehicle track the expected value better and improve the driving stability effectively.
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Wang, Pengcheng, Weile Xu, Hao Zhu, Hui Tian, and Guobiao Cai. "An Application of Analytical Target Cascading for a Hierarchical Multidisciplinary System: The Preliminary Design of a Launch Vehicle Powered by Hybrid Rocket Motors." Aerospace 9, no. 12 (December 1, 2022): 778. http://dx.doi.org/10.3390/aerospace9120778.
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Analytical target cascading (ATC) is a method for coordinating hierarchical system design optimization with a decomposition-based framework. Since a launch vehicle (LV) is usually powered by two or more stages of rocket motors, the overall design of the LV clearly has a hierarchical structure, including system level (conducted by the general design department) and subsystem level (conducted by the motor stage design department). In particular, the subsystem level contains stage-divided elements rather than discipline-divided elements. Therefore, ATC is inherently suitable for the overall design of the LV. This paper presents an ATC decomposition framework for LV design according to practical engineering. The feasibility of the multi-island genetic algorithm (MIGA) used in the ATC decomposition is verified by a mathematical programming test, in which non-linear programming with the quadratic Lagrangian (NLPQL) algorithm is set as a comparison. The multi-disciplinary analysis modules of a hybrid rocket motor (HRM) propelled LV, including propulsion, structure, aerodynamics and trajectory, are established. A hierarchical decomposition is proposed for this multi-level design with a multi-disciplinary model. The application and optimization results verify the feasibility of the ATC decomposition framework with MIGA in the preliminary design of the LV and the final orbit accuracy is better than that of the MDF method. In addition, the final design schemes also prove that HRMs can be considered as a feasible choice of propulsion system for a small payload at low earth orbit.
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Zhang, Wenjun, Zhuxing Liu, and Qingzhang Chen. "Electronic Differential System Based on Adaptive SMC Combined with QP for 4WID Electric Vehicles." World Electric Vehicle Journal 12, no. 3 (August 20, 2021): 126. http://dx.doi.org/10.3390/wevj12030126.
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This study investigates an adaptive differential control system for 4WID (4-wheel-independent-drive) electric vehicles. The novel adaptive system will maneuver the independently operating hub motors without the help of any conventional steering mechanism. The control system consists of a hierarchical structure to confront the vehicle stability condition, which includes a novel SMC (sliding mode control) with a fuzzy algorithm parameter modification to achieve the required virtual control signal at the top level, and a quadratic programming-based torque allocation algorithm at the bottom-level controller. The proposed controller was tested through Simulink/Carsim simulation and experiments. All the test cases showed the advantages of the proposed method over some of the currently existing 4WID control strategies.
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Ai, Chao, Guangling Zhou, Yalun Wang, Wei Gao, and Xiangdong Kong. "Active Power Control of Hydraulic Wind Turbines during Low Voltage Ride-Through (LVRT) Based on Hierarchical Control." Energies 12, no. 7 (March 29, 2019): 1224. http://dx.doi.org/10.3390/en12071224.
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To improve the power grid adaptability and low voltage ride-through (LVRT) capability of hydraulic wind turbines (HWT), an LVRT control method based on hierarchical control is proposed for the energy regulation of HWT. The method includes a top-level machine-controlled paddle, mid-level control based on variable motor swash plate angle, and an underlying control based on throttle opening. To achieve multivariable coordinated control of the HWT via the control process, the minimum wind, maximum inertial energy storage, and minimum energy consumption of the throttle valve of the wind turbine are optimized. The multiobjective control law is computed by a quadratic programming algorithm, and the optimal control law is obtained. The multitarget control strategy is simulated and analyzed by AMESim14 and MATLAB/Simulink R2014a software, and the control law is verified by a semiphysical test platform of an HWT. The results show that the proposed control method can effectively reduce the residual energy of the HWT during LVRT, reduce the impact on the generator, and improve the adaptability of the HWT.
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Wenwei, Wang, Zhang Wei, Zhang Hanyu, and Cao Wanke. "Yaw stability control through independent driving torque control of mid and rear wheels of an articulated bus." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 13 (June 3, 2020): 2947–60. http://dx.doi.org/10.1177/0954407020919539.
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This paper describes a novel yaw stability control strategy for a four-wheel-independent-drive electric articulated bus with four motors at the middle and rear wheels. The proposed control strategy uses a hierarchical control architecture. In the upper layer, a 3 degree-of-freedom reference model is established to obtain the desired vehicle states and the desired yaw moments of the front and rear compartments are determined by means of sliding mode control, respectively. The lower layer distributes differential longitudinal forces according to the desired yaw moments based on quadratic programming theory. The tire utilization rate is used as the optimization goal considering the actual constraints. To verify performance, three test cases are designed on the dSPACE-ASM simulation platform. The test results show the proposed control strategy can improve the yaw stability and the trajectory following performance of the bus under different driving conditions.
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Zou, Teng’an, Yulong You, Hao Meng, and Yukang Chang. "Research on Six-Wheel Distributed Unmanned Vehicle Path Tracking Strategy Based on Hierarchical Control." Biomimetics 7, no. 4 (December 12, 2022): 238. http://dx.doi.org/10.3390/biomimetics7040238.
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For the multi-objective control problem of tracking effect and vehicle stability in the path tracking process of six-wheel distributed unmanned vehicles, a control strategy based on hierarchical control (HC) theory is proposed. A hierarchical kinematic model is designed considering the structural advantages of independent steering and independent driving of the unmanned vehicle, and this model is applied to the path tracking strategy. The strategy is divided into two levels of control. The upper level of control is to use the upper-level kinematic model as the prediction model of model predictive control (MPC), and to convert the solution problem of future control increments into the optimal solution problem of quadratic programming by setting the optimal objective function and constraints. The lower level of control is to map the optimal control quantities obtained from the upper level control to the six-wheel speeds and the four-wheel turning angles through the lower-level kinematics, and to design the six-wheel torque distribution rules based on deterministic torque and stability-based slip rate control for executing the control requirements of the upper level controller to prevent the unmanned vehicle from generating sideslip and precisely generating transverse moment to ensure the stable driving of the unmanned vehicle. Experiments were conducted on the Trucksim/Simulink simulation platform for a variety of road conditions, and the results showed that hierarchical control improved the accuracy of tracking the desired path and the driving stability on complex road surfaces more than MPC.
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Zhao, Lu, and Zhang. "Game-Based Hierarchical Cooperative Control for Electric Vehicle Lateral Stability via Active Four-Wheel Steering and Direct Yaw-Moment Control." Energies 12, no. 17 (August 29, 2019): 3339. http://dx.doi.org/10.3390/en12173339.
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A Stackelberg game-based cooperative control strategy is proposed for enhancing the lateral stability of a four-wheel independently driving electric vehicle (FWID-EV). An upper‒lower double-layer hierarchical control structure is adopted for the design of a stability control strategy. The leader‒follower-based Stackelberg game theory (SGT) is introduced to model the interaction between two unequal active chassis control subsystems in the upper layer. In this model, the direct yaw-moment control (DYC) and the active four-wheel steering (AFWS) are treated as the leader and the follower, respectively, based on their natural characteristics. Then, in order to guarantee the efficiency and convergence of the proposed control strategy, a sequential quadratic programming (SQP) algorithm is employed to solve the task allocation problem among the distributed actuators in the lower layer. Also, a double-mode adaptive weight (DMAW)- adjusting mechanism is designed, considering the negative effect of DYC. The results of cosimulation with CarSim and Matlab/Simulink demonstrate that the proposed control strategy can effectively improve the lateral stability by properly coordinating the actions of AFWS and DYC.
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Wang, Bowen, Cheng Lin, Sheng Liang, Xinle Gong, and Zhenyi Tao. "Hierarchical Model Predictive Control for Autonomous Collision Avoidance of Distributed Electric Drive Vehicle with Lateral Stability Analysis in Extreme Scenarios." World Electric Vehicle Journal 12, no. 4 (October 15, 2021): 192. http://dx.doi.org/10.3390/wevj12040192.
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This paper proposes an active collision avoidance controller based on a hierarchical model predictive control framework for distributed electric drive vehicles (4IDEV) considering extreme conditions. In this framework, a two-layer strategy is developed. The upper layer is the path replanning controller based on nonlinear MPC (nMPC), from which a collision-free path including the optimal lateral displacement and yaw angle can be obtained in real-time while encountering the obstacles. The lower layer is the path tracking controller based on hybrid MPC (hMPC), and the coordinated control inputs (yaw moment and the front wheel steering angle) are solved by a Mixed-Integer Quadratic Programming (MIQP) with the piecewise affine (PWA) tire model considering tire saturation region. Moreover, to improve the lateral stability when tracking, the stable zone of lateral stability in the high-risk condition is analyzed based on the phase portrait method, by which the constraints of vehicle states and inputs are derived. The verification is carried out on the MATLAB and CarSim co-simulation platform, and the simulation results show that the proposed active collision avoidance controller can track the reference path accurately and prevent vehicle instability in extreme scenarios.
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Wang, Zixu, Yong Li, Chuyo Kaku, and Hongyu Zheng. "Trajectory Tracking Control of Intelligent X-by-Wire Vehicles." World Electric Vehicle Journal 13, no. 11 (November 1, 2022): 205. http://dx.doi.org/10.3390/wevj13110205.
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Vehicle intelligence is an effective way to improve driving safety and comfort and reduce traffic accidents. The trajectory tracking control of unmanned vehicles is the core module of intelligent vehicles. As a redundant system, the X-by-wire electric vehicle has the advantage that the turning angles and driving torque of the four wheels can be precisely controlled and it has a higher degree of controllability and flexibility. In this paper, a trajectory tracking control algorithm based on a hierarchical control architecture is designed based on x-by-wire vehicles. The hierarchical control algorithm architecture includes the trajectory tracking layer, tire force distribution layer, and actuator control layer. The trajectory tracking layer uses the longitudinal force, lateral force, and yaw moment as the control variables; the model predictive control algorithm controls the vehicle to follow the desired trajectory. The tire force distribution layer is solved by transforming the tire force distribution problem into a quadratic programming problem with constraints. Based on the expected resultant force and resultant moment, the longitudinal force and lateral force of each tire in the vehicle coordinate system are obtained. The actuator control layer converts the coordinate system to obtain the longitudinal force and lateral force in the tire coordinate system, which uses the arctangent function tire model to solve the desired tire slip angle, and then obtains the vehicle steer angle and driving torque. To verify the effectiveness of the trajectory tracking control algorithm of the hierarchical control architecture, the proposed trajectory tracking control algorithm is simulated and verified through the variable speed double line change condition and the low road friction coefficient double line change condition. The simulation results show that the control algorithm proposed in this paper has the accuracy to follow the desired trajectory.Definition:
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Dash, Pratik S., D. N. Prasad, Santosh K. Sriramoju, R. K. Lingam, A. Suresh, P. K. Banerjee, and S. Ganguly. "Maximizing Demineralization during Chemical Leaching of Coal through Optimal Reagent Addition Policy." Chemical Product and Process Modeling 10, no. 1 (March 1, 2015): 1–9. http://dx.doi.org/10.1515/cppm-2014-0019.
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Abstract The main objective of the optimal reagent addition was to maximize the quantity of product with minimal quantity of feed. In the present study, the optimal addition of reagents during the chemical leaching of coal was computed. Chemical leaching of coal was carried out using aqueous solution of caustic to dissolve and remove the mineral matter. Simulation studies were carried out using the optimal reagent addition for chemical leaching of coal in batch reactors. This was experimentally validated, using the bench-scale reactor setup with hierarchical optimization architecture. Chemical leaching experiments were conducted using West Bokaro coal. Samples collected at various time intervals during the experiment were analyzed. Variations in silica (SiO2) and alumina (Al2O3) concentrations, which were main constituents present in coal ash, were evaluated with respect to time for different concentrations of caustic. The simulation studies for optimal addition were carried out at 6, 8 and 10 intervals. An objective function, required for maximum ash removal, was solved, using sequential quadratic programming (SQP) algorithm to find out the optimum sequence for reagent dosing. An improvement of about 1% (wt) ash reduction on an average was observed with implementation of optimal reagent addition.
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Liu, Yang, Dong Zhang, Timothy Gordon, Guiyuan Li, and Changfu Zong. "Approach of Coordinated Control Method for Over-Actuated Vehicle Platoon based on Reference Vector Field." Applied Sciences 9, no. 2 (January 15, 2019): 297. http://dx.doi.org/10.3390/app9020297.
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Collaborative vehicle platoon control with full drive-by-wire vehicles with four-wheel independent driving and steering (FWIDSV) has attracted broader research interests. However, the problem of cooperative vehicle platoon control in two-dimensional driving scenes remains to be solved. This paper proposes a coupling control method for path tracking and spacing-maintaining based on the reference vector field (RVF). An integrated hierarchical control structure, including the following control layer, tire force allocator layer, and an actuator controlling layer for FWIDSV is presented. Inside, the next control layer was designed according to the spacing control strategy and RVF within the limitation of the friction circle. For verifying the effectiveness of this control method, sufficient conditions for error convergence are analyzed when considering the influence of the critical parameters on the particle dynamics model. The tire force allocator layer is designed based on linear quadratic programming (LQP), which is used to distribute the total forces and yaw moment. The sliding mode control (SMC) is employed to track the desired tire forces in the actuator controlling layer. The proposed control methods are validated through simulation in intelligent cruise control (ICC) and platoon merging scenarios. The results demonstrate an effective FWIDSV platoon control approach that is based on the RVF in the 2-D driving scenes.
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Reyes Dreke, Victor Daniel, and Mircea Lazar. "Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters." Energies 15, no. 4 (February 14, 2022): 1376. http://dx.doi.org/10.3390/en15041376.
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Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach.
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Wang, Deqing, Zheng Chang, and Fengyu Cong. "Sparse nonnegative tensor decomposition using proximal algorithm and inexact block coordinate descent scheme." Neural Computing and Applications 33, no. 24 (October 4, 2021): 17369–87. http://dx.doi.org/10.1007/s00521-021-06325-8.
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AbstractNonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using $$l_1$$ l 1 -norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP with the proximal algorithm. The subproblems in the new model are strongly convex in the block coordinate descent (BCD) framework. Therefore, the new sparse NCP provides a full column rank condition and guarantees to converge to a stationary point. In addition, we proposed an inexact BCD scheme for sparse NCP, where each subproblem is updated multiple times to speed up the computation. In order to prove the effectiveness and efficiency of the sparse NCP with the proximal algorithm, we employed two optimization algorithms to solve the model, including inexact alternating nonnegative quadratic programming and inexact hierarchical alternating least squares. We evaluated the proposed sparse NCP methods by experiments on synthetic, real-world, small-scale, and large-scale tensor data. The experimental results demonstrate that our proposed algorithms can efficiently impose sparsity on factor matrices, extract meaningful sparse components, and outperform state-of-the-art methods.
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Zhang, Ze, Brock C. Christensen, and Lucas A. Salas. "Abstract 1212: ExTIME: Extended tumor immune micro-environment cell mixture deconvolution using DNA methylation and a novel tumor-site-specific hierarchical approach." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1212. http://dx.doi.org/10.1158/1538-7445.am2022-1212.
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Abstract Background: The solid tumor microenvironment is heterogeneous and varies in composition by tumor type. Previous gene expression and DNA methylation deconvolution approaches for tumor micro-environment have had some success for major cell types. However, existing methods lack specificity to tumor type and detailed cell types. We developed 21 tumor-specific DNA methylation-based libraries. We employed a novel hierarchical approach in 3 major tumor microenvironment components (tumor, angiogenic, immune) to profile 17 cell types (see methods below). Methods: DNA methylation data on tumor samples (n=6183) and normal control samples (n=689) for 21 tumor sites were downloaded from GEO and TCGA to develop tumor-type-specific libraries. The top 1000 most informative differentially methylated CpG (DMC) sites were identified using InfiniumPurify for 21 tumor types to project tumor cell proportion. Epithelial, endothelial, and stromal cell samples were used to identify DMCs to profile the cells in the angiogenic environment. Basophil, eosinophil, neutrophil, dendritic cell, monocyte, B naïve, B memory, CD4T naïve, CD4T memory, CD8T naïve, CD8T memory, T regulatory, and natural killer cells were used to identify DMCs to deconvolve the immune environment. In conjunction with the constrained projection/quadratic programming approach, a novel hierarchical approach was employed with six layers and 12 libraries per tumor type to project cell proportions in first, tumor, second, angiogenic, and third, immune micro-environments. The method was validated using purified samples and experimental artificial mixtures. Results: 12 libraries were developed per tumor site to deconvolve 17 cell types in 21 tumors. A preliminary application of the method on TCGA data investigating the association between angiogenic cells and survival revealed worse survival outcomes with a higher proportion of angiogenic cell proportions in BLCA (p<0.01) and HNSC (p=0.02), a higher endothelial cell proportion in CESC (p=0.04), a higher epithelial cell proportion in COAD (p=0.02), a lower endothelial proportion in KIRC (p<0.01), and a lower epithelial proportion in LUAD (p=0.04). Further analyses will be done to investigate the angiogenic and immune microenvironments with prognosis across tumor sites. Conclusion: We developed a DNA methylation-based algorithm, ExTIME, to estimate cell proportions in the tumor microenvironments. This novel approach increased the specificity and accuracy of cell projection by employing a tumor-site-specific hierarchical model. Furthermore, the ExTIME profiles the tumor microenvironment to the most granular level compared to the existing methods. ExTIME’s capability of depicting the cellular composition in tumors promises a better understanding of the cell heterogeneity and its relationship with prognosis across cancers. Citation Format: Ze Zhang, Brock C. Christensen, Lucas A. Salas. ExTIME: Extended tumor immune micro-environment cell mixture deconvolution using DNA methylation and a novel tumor-site-specific hierarchical approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1212.
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Jang, Keunwoo, Sanghyun Kim, and Jaeheung Park. "Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator." Sensors 21, no. 3 (January 28, 2021): 890. http://dx.doi.org/10.3390/s21030890.
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This paper introduces a reactive self-collision avoidance algorithm for differentially driven mobile manipulators. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, which means the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. In this respect, the proposed algorithm has a distinct advantage over existing avoidance methods that do not consider the non-holonomic constraint of the mobile robot and push links away from each other without considering the workspace. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot. The avoidance task is smoothly inserted with a top priority into the controller based on hierarchical quadratic programming. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.
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Biggar, Oliver, Mohammad Zamani, and Iman Shames. "On Modularity in Reactive Control Architectures, with an Application to Formal Verification." ACM Transactions on Cyber-Physical Systems 6, no. 2 (April 30, 2022): 1–36. http://dx.doi.org/10.1145/3511606.
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Modularity is a central principle throughout the design process for cyber-physical systems. Modularity reduces complexity and increases reuse of behavior. In this article we pose and answer the following question: how can we identify independent “modules” within the structure of reactive control architectures? To this end, we propose a graph-structured control architecture we call a decision structure and show how it generalizes some reactive control architectures that are popular in Artificial Intelligence (AI) and robotics, specifically Teleo-Reactive programs (TRs), Decision Trees (DTs), Behavior Trees (BTs), and Generalised Behavior Trees ( k -BTs). Inspired by the definition of a module in graph theory [ 16 ] we define modules in decision structures and show how each decision structure possesses a canonical decomposition into its modules, which can be found in polynomial time. We establish intuitive connections between our proposed modularity and modularity in structured programming. In BTs, k -BTs, and DTs the modules we propose are in a one-to-one correspondence with their subtrees. We show we can naturally characterize each of the BTs, k -BTs, DTs, and TRs by properties of their module decomposition. This allows us to recognize which decision structures are equivalent to each of these architectures in quadratic time. Following McCabe [ 26 ], we define a complexity measure called essential complexity on decision structures, which measures the degree to which they can be decomposed into simpler modules. We characterize the k -BTs as the decision structures of unit-essential complexity. Our proposed concept of modules extends to formal verification, under any verification scheme capable of verifying a decision structure. Namely, we prove that a modification to a module within a decision structure has no greater flow-on effects than a modification to an individual action within that structure. This enables verification on modules to be done locally and hierarchically, where structures can be verified and then repeatedly locally modified, with modules replaced by modules while preserving correctness. To illustrate the findings, we present an example of a solar-powered drone completing a reconnaissance-based mission using a decision structure. We use a Linear Temporal Logic-based verification scheme to verify the correctness of this structure and then show how one can repeatedly modify modules while preserving its correctness, and this can be verified by considering only those modules that have been modified.
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Zhang, Yao, Jianxue Wang, and Tianhui Zhao. "Using Quadratic Programming to Optimally Adjust Hierarchical Load Forecasting." IEEE Transactions on Power Systems, 2018, 1. http://dx.doi.org/10.1109/tpwrs.2018.2857628.
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Wu, Fangyu, and Alexandre M. Bayen. "A Hierarchical MPC Approach to Car-Following via Linearly Constrained Quadratic Programming." IEEE Control Systems Letters, 2022, 1. http://dx.doi.org/10.1109/lcsys.2022.3201162.
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Jang, Keunwoo, Sanghyun Kim, Suhan Park, Junhyung Kim, and Jaeheung Park. "Weighted hierarchical quadratic programming: assigning individual joint weights for each task priority." Intelligent Service Robotics, July 5, 2022. http://dx.doi.org/10.1007/s11370-022-00431-9.
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Hosseini, Mohammad Mehdi, Luis Rodriguez-Garcia, and Masood Parvania. "Hierarchical Combination of Deep Reinforcement Learning and Quadratic Programming for Distribution System Restoration." IEEE Transactions on Sustainable Energy, 2023, 1–11. http://dx.doi.org/10.1109/tste.2023.3245090.
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Prasad, Rajan, Yue Ma, Yu Wang, and Huimin Zhang. "Hierarchical coordinated control distribution and experimental verification for six-wheeled unmanned ground vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, July 21, 2020, 095440702094082. http://dx.doi.org/10.1177/0954407020940823.
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In recent years, the all-wheel independent drive has been the most promising form of drive configuration in unmanned ground vehicles. Considering the difficulties in the control allocation for this kind of vehicle, this paper presents a hierarchical control coordination strategy with three layers to distribute control in real time effectively and accurately. In the upper layer, a hybrid instruction parsing method is proposed, which converts commands of the control panel into driving force requirement and target steering yaw rate, respectively, to prioritize steering command to maintain the trajectory based on the motor properties. Subsequently, a sliding mode controller is employed to convert the target yaw rate into the required yaw moment. The state estimation layer receives data from the sensors and estimates different properties/parameters required in other layers. The lower-level control layer receives commands from the upper layer and allocates respective control to wheels. The control allocation problem has been formulated as an optimization problem and later has been converted into a quadratic programming problem, in which a novel modified barrier method with the combination of reduced equation dimension has been adopted to minimize the computational effort and complexity for implementation on the embedded platform. Computer simulation and field experiment have been conducted, which verify the performance of the proposed strategy.
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Carreira-Perpinan, Miguel, and Weiran Wang. "LASS: A Simple Assignment Model with Laplacian Smoothing." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 1 (June 21, 2014). http://dx.doi.org/10.1609/aaai.v28i1.8969.
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We consider the problem of learning soft assignments of N items to K categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity matrix, which encourages similar items to have similar assignments. We propose a simple quadratic programming model that captures this intuition. We give necessary conditions for its solution to be unique, define an out-of-sample mapping, and derive a simple, effective training algorithm based on the alternating direction method of multipliers. The model predicts reasonable assignments from even a few similarity values, and can be seen as a generalization of semisupervised learning. It is particularly useful when items naturally belong to multiple categories, as for example when annotating documents with keywords or pictures with tags, with partially tagged items, or when the categories have complex interrelations (e.g. hierarchical) that are unknown.
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Fawcett, Randall T., Abhishek Pandala, Jeeseop Kim, and Kaveh Akbari Hamed. "Real-Time Planning and Nonlinear Control for Quadrupedal Locomotion With Articulated Tails." Journal of Dynamic Systems, Measurement, and Control 143, no. 7 (February 8, 2021). http://dx.doi.org/10.1115/1.4049555.
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Abstract The primary goal of this paper is to develop a formal foundation to design nonlinear feedback control algorithms that intrinsically couple legged robots with bio-inspired tails for robust locomotion in the presence of external disturbances. We present a hierarchical control scheme in which a high-level and real-time path planner, based on an event-based model predictive control (MPC), computes the optimal motion of the center of mass (COM) and tail trajectories. The MPC framework is developed for an innovative reduced-order linear inverted pendulum (LIP) model that is augmented with the tail dynamics. At the lower level of the control scheme, a nonlinear controller is implemented through the use of quadratic programming (QP) and virtual constraints to force the full-order dynamical model to track the prescribed optimal trajectories of the COM and tail while maintaining feasible ground reaction forces at the leg ends. The potential of the analytical results is numerically verified on a full-order simulation model of a quadrupedal robot augmented with a tail with a total of 20 degrees-of-freedom. The numerical studies demonstrate that the proposed control scheme coupled with the tail dynamics can significantly reduce the effect of external disturbances during quadrupedal locomotion.
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Liu, Hui, Baoshuai Liu, Ziyong Han, Yechen Qin, Xiaolei Ren, and Lijin Han. "Attitude control strategy for unmanned wheel-legged hybrid vehicles considering the contact of the wheels and ground." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, November 14, 2021, 095440702110583. http://dx.doi.org/10.1177/09544070211058382.
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During patrol and surveillance tasks, attitude control is crucial for improving the terrain adaptability of unmanned wheel-legged hybrid vehicles. This paper proposes an attitude control strategy for unmanned wheel-legged hybrid vehicles, considering the contact of the wheels and ground. The proposed method can naturally achieve torque control efficiently of each joint actuator and wheel-side actuator and avoid the discrepancy between off-road terrain and stability. First, an inverse kinematics model is established to resolve the body and each joint rotation angle, and the dynamic model is built based on the multi rigid body theory, considering the contact points planning of wheel and ground. Considering the nonholonomic constraint of the structure scheme, a hierarchical real-time attitude controller for a wheel-legged vehicle is proposed. The upper layer calculates the contact points of each wheel and the ground through the quadratic programming algorithm, and the lower layer is divided into a legged motion generator and a wheel motion generator by a mathematical analysis method. Finally, the proposed method is applied to achieve the tracking and control of the whole-body trajectory. The proposed strategy can achieve the decoupling of wheeled motion generator and legged motion generator, and improve control efficiency.
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Kleinert, Thomas, and Martin Schmidt. "Computing Feasible Points of Bilevel Problems with a Penalty Alternating Direction Method." INFORMS Journal on Computing, June 22, 2020. http://dx.doi.org/10.1287/ijoc.2019.0945.
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Bilevel problems are highly challenging optimization problems that appear in many applications of energy market design, critical infrastructure defense, transportation, pricing, and so on. Often these bilevel models are equipped with integer decisions, which makes the problems even harder to solve. Typically, in such a setting in mathematical optimization, one develops primal heuristics in order to obtain feasible points of good quality quickly or to enhance the search process of exact global methods. However, there are comparably few heuristics for bilevel problems. In this paper, we develop such a primal heuristic for bilevel problems with a mixed-integer linear or quadratic upper level and a linear or quadratic lower level. The heuristic is based on a penalty alternating direction method, which allows for a theoretical analysis. We derive a convergence theory stating that the method converges to a stationary point of an equivalent single-level reformulation of the bilevel problem and extensively test the method on a test set of more than 2,800 instances—which is one of the largest computational test sets ever used in bilevel programming. The study illustrates the very good performance of the proposed method in terms of both running times and solution quality. This renders the method a suitable subroutine in global bilevel solvers as well as a reasonable standalone approach. Summary of Contribution: Bilevel optimization problems form a very important class of optimization problems in the field of operations research, which is mainly due to their capability of modeling hierarchical decision processes. However, real-world bilevel problems are usually very hard to solve—especially in the case in which additional mixed-integer aspects are included in the modeling. Hence, the development of fast and reliable primal heuristics for this class of problems is very important. This paper presents such a method.
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González-Alemán, Roy, Daniel Platero-Rochart, Alejandro Rodríguez-Serradet, Erix W. Hernández-Rodríguez, Julio Caballero, Fabrice Leclerc, and Luis Montero-Cabrera. "MDSCAN: RMSD-Based HDBSCAN Clustering of Long Molecular Dynamics." Bioinformatics, October 7, 2022. http://dx.doi.org/10.1093/bioinformatics/btac666.
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Abstract Motivation The term clustering designates a comprehensive family of unsupervised learning methods allowing to group similar elements into sets called clusters. Geometrical clustering of Molecular Dynamics (MD) trajectories is awell-established analysis to gain insights into the conformational behavior of simulated systems. However, popular variants collapse when processing relatively long trajectories because of their quadratic memory or time complexity. From the arsenal of clustering algorithms, HDBSCAN stands out as a hierarchical density-based alternative that provides robust differentiation of intimately related elements from noise data. Although a very efficient implementation of this algorithm is available for programming-skilled users (HDBSCAN*), it cannot treat long trajectories under the de facto molecular similarity metric RMSD. Results Here, we propose MDSCAN, an HDBSCAN-inspired software specifically conceived for non-programmers users to perform memory-efficient RMSD-based clustering of long MD trajectories. Methodological improvements over the original version include the encoding of trajectories as a particular class of vantage-point tree (decreasing time complexity), and a dual-heap approach to construct a quasi-minimum spanning tree (reducing memory complexity). MDSCAN was able to process a trajectory of one-million frames using the RMSD metric in about 21 hours with less than 8 GB of RAM, a task that would have taken a similar time but more than 32 TB of RAM with the accelerated HDBSCAN* implementation generally used. Availability and implementation The source code and documentation of MDSCAN are free and publicly available on GitHub (https://github.com/LQCT/MDScan.git) and as a PyPI package (https://pypi.org/project/mdscan/). Supplementary information Supplementary data are available at Bioinformatics online.