Relevant bibliographies by topics / Model predictive controller (MPC)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Model predictive controller (MPC)'

Author: Grafiati
Published: 25 May 2024
Last updated: 25 July 2025

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Journal articles on the topic "Model predictive controller (MPC)"

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Rezaee, Alireza. "Model predictive Controller for Mobile Robot." Transactions on Environment and Electrical Engineering 2, no. 2 (2017): 18. http://dx.doi.org/10.22149/teee.v2i2.96.

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This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC
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Tesfaye, Alamirew, Balaji V., and Gabbeye Nigus. "Comparison of PID Controller with Model Predictive Controller for Milk Pasteurization Process." Bulletin of Electrical Engineering and Informatics 6, no. 1 (2017): 24–35. https://doi.org/10.11591/eei.v6i1.575.

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Proportional-Integral-Derivative (PID) controllers are used in many of the Industries for various process control applications. PID controller yields a long settling time and overshoot which is not good for the process control applications. PID is not suitable for many of the complex process control applications. This research paper is about developing a better type of controller, known as MPC (Model Predictive Control). The aim of the paper is to design MPC and PID for a pasteurization process. In this manuscript comparison of PID controller with MPC is made and the responses are presented. M
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Alasali, Feras, Stephen Haben, Husam Foudeh, and William Holderbaum. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads." Energies 13, no. 10 (2020): 2596. http://dx.doi.org/10.3390/en13102596.

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This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impa
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Wahid, Abdul, and Richi Adi. "MODELING AND CONTROL OF MULTIVARIABLE DISTILLATION COLUMN USING MODEL PREDICTIVE CONTROL USING UNISIM." SINERGI 20, no. 1 (2016): 14. http://dx.doi.org/10.22441/sinergi.2016.1.003.

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Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation
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Xu, Ying, Wentao Tang, Biyun Chen, Li Qiu, and Rong Yang. "A Model Predictive Control with Preview-Follower Theory Algorithm for Trajectory Tracking Control in Autonomous Vehicles." Symmetry 13, no. 3 (2021): 381. http://dx.doi.org/10.3390/sym13030381.

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Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the
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Kümpel, Alexander, Phillip Stoffel, and Dirk Müller. "Self-adjusting model predictive control for modular subsystems in HVAC systems." Journal of Physics: Conference Series 2042, no. 1 (2021): 012037. http://dx.doi.org/10.1088/1742-6596/2042/1/012037.

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Abstract In order to reduce the energy consumption and CO2 emissions in the building sector, an efficient control strategy, such as model predictive control (MPC) is required. However, MPC is rarely applied in buildings since the implementation and modeling is complex, time consuming and costly. To bring MPC into practice, controllers and models are needed, that automatically adapt their behavior to the controlled system. In this work, such a self-adjusting MPC applicable to heating, ventilation and air-conditioning (HVAC) systems is developed. The MPC is based on a simple grey-box model that
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Srikshana, Sasidaran, R. Adithya, Raja V. Haris, and M.P.Anbarasi. "Recent Trends in Model Predictive Control." International Journal of Innovative Science and Research Technology 7, no. 2 (2022): 249–54. https://doi.org/10.5281/zenodo.6323081.

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In this paper we are going to present the recent trends of model predictive control (MPC) and its techniques are used in modern world. MPC forecasts plant output behavior using a plant model. The MPC controller solves the optimization problem across the prediction horizon while adhering to the constraints at the current phase. This can be used in non-linear problems and it is more precise when compare to the linear controller such as PID.
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Kumavat, Mayur, and Sushil Thale. "Analysis of CSTR Temperature Control with PID, MPC & Hybrid MPC-PID Controller." ITM Web of Conferences 44 (2022): 01001. http://dx.doi.org/10.1051/itmconf/20224401001.

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This paper presents an analysis of the continuous stirred tank reactor (CSTR) temperature control with the Proportional-Integral-Derivative (PID) Controller, Model Predictive Controller (MPC) and Hybrid-Model Predictive Controller-Proportional Integral Derivative Controller (MPC-PID). It is the main goal of this project to find a suitable improvement strategy for the system’s stability and accuracy to be more stable. By creating a model, the control system is implemented for all the above mentioned control methods and so comparative analysis is carried out to find the best control method for C
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Chrif, Labane, and Zemalache Meguenni Kadda. "Aircraft Control System Using Model Predictive Controller." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 259. http://dx.doi.org/10.11591/tijee.v15i2.1538.

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This paper concerns the application of model-based predictive control to the longitudinal and lateral mode of an aircraft in a terrain following task. The predictive control approach was based on a quadratic cost function and a linear state space prediction model with input and state constraints. The optimal control was obtained as the solution of a quadratic programming problem defined over a receding horizon. Closed-loop simulations were carried out by using the linear aircraft model. This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industria
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Vrečko, D., N. Hvala, and M. Stražar. "The application of model predictive control of ammonia nitrogen in an activated sludge process." Water Science and Technology 64, no. 5 (2011): 1115–21. http://dx.doi.org/10.2166/wst.2011.477.

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In this paper a model predictive controller (MPC) for ammonia nitrogen is presented and evaluated in a real activated sludge process. A reduced nonlinear mathematical model based on mass balances is used to model the ammonia nitrogen in the activated sludge plant. An MPC algorithm that minimises only the control error at the end of the prediction interval is applied. The results of the ammonia MPC were compared with the results of the ammonia feedforward-PI and ammonia PI controllers from our previous study. The ammonia MPC and ammonia feedforward-PI controller give better results in terms of
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Dissertations / Theses on the topic "Model predictive controller (MPC)"

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Bangalore, Narendranath Rao Amith Kaushal. "Online Message Delay Prediction for Model Predictive Control over Controller Area Network." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.

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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive
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Mattsson, Mathias, and Rasmus Mehler. "Optimal Vehicle Speed Control Using a Model Predictive Controller for an Overactuated Vehicle." Thesis, Linköpings universitet, Fordonssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119480.

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To control the speed of an overactuated vehicle there may be many possible ways to use the actuators of the car achieving the same outcome. The actuators in an ordinary car is a combustion engine and a friction brake. In some cases it is trivial how to coordinate actuators for the optimal result, but in many cases it is not. The goal with the thesis is to investigate if it is possible to achieve the same or improved performance with a more sophisticated control structure than today's, using a model predictive controller. A model predictive controller combines the possibility to predict the out
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Claro, Érica Rejane Pereira. "Localização de canais afetando o desempenho de controladores preditivos baseados em modelos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/149927.

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O escopo desta dissertação é o desenvolvimento de um método para detectar os modelos da matriz dinâmica que estejam degradando o desempenho de controladores preditivos baseados em modelos. O método proposto se baseia na análise de correlação cruzada entre o erro nominal do controlador em malha fechada e a uma estimativa da contribuição de cada canal para o cálculo da saída, filtrada pela função de sensibilidade do controlador. Esse método pode ser empregado na auditoria de controladores com variáveis controladas em setpoints e/ou com variáveis que operem entre faixas, como é usual de se encont
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Paula, Neander Alessandro da Silva. "MPC adaptativo - multimodelos para controle de sistemas não-lineares." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-14052009-000836/.

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Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do
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Venieri, Giulia. "Development and testing of Model Predictive Controllers for an automotive organic Rankine cycle unit." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Two-thirds of the energy produced by an internal combustion engine (ICE) is lost into waste heat through the coolant and the exhaust gas; hence, studying Waste Heat Recovery (WHR) systems is of vital importance. The organic Rankine cycle (ORC) is a powerful system to recover low-grade heat and transform it into electrical energy. This thesis aimed at developing and testing a Model Predictive Control (MPC) system that ensures a safe operation of a system that constitutes an ICE bottomed by an ORC unit. The experimentation was carried out at the DTU Mekanik laboratories and was divided into dif
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Andina, Elisa. "Complexity and Conservatism in Linear Robust Adaptive Model Predictive Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Questa tesi presenta uno schema di controllo robusto adattativo basato sulla tecnica di controllo avanzato model predictive control (MPC) per sistemi lineari soggetti a disturbi additivi e incertezze parametriche, costanti e variabili. L'approccio proposto fornisce uno schema di controllo efficiente dal punto di vista computazionale con stima dei parametri online per ottenere un aumento delle prestazioni e una diminuzione progressiva del conservatismo. L'insieme dei parametri è estimato usando una tecnica di identificazione a finestra mobile per ottenere un insieme con complessità limitata. Il
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Rokebrand, Luke Lambertus. "Towards an access economy model for industrial process control." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/79650.

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With the ongoing trend in moving the upper levels of the automation hierarchy to the cloud, there has been investigation into supplying industrial automation as a cloud based service. There are many practical considerations which pose limitations on the feasibility of the idea. This research investigates some of the requirements which would be needed to implement a platform which would facilitate competition between different controllers which would compete to control a process in real-time. This work considers only the issues relating to implementation of the philosophy from a control theoret
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Pitta, Renato Neves. "Aplicação industrial de re-identificação de modelos de MPC em malha fechada." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-10042012-115001/.

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A identificação de modelos é usualmente a tarefa mais significativa e demorada no trabalho de implementação e manutenção de sistemas de controle que usam Controle Preditivo baseado em Modelos (MPC) tendo em vista a complexidade da tarefa e a importância que o modelo possui para um bom desempenho do controlador. Após a implementação, o controlador tende a permanecer com o modelo original mesmo que mudanças de processo tenham ocorrido levando a uma degradação das ações do controlador. Este trabalho apresenta uma aplicação industrial de re-identificação em malha fechada. A metodologia de excitaçã
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Jansson, Lovisa, and Amanda Nilsson. "Evaluation of Model-Based Design Using Rapid Control Prototyping on Forklifts." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158715.

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The purpose of this thesis is to evaluate Rapid Control Prototyping which is apart of the Model-Based Design concept that makes it possible to convenientlytest prototype control algorithms directly on the real system. The evaluation ishere done by designing two different controllers, a gain-scheduled P controllerand a linear Model Predictive Controller (mpc), for the lowering of the forks of aforklift.The two controllers are first tested in a simulation environment. The thesis con-tains two different simulation models: one physical where only minor parameteradjustments are done and one estimat
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Cruz, Diego Déda Gonçalves Brito. "Detecção de erros planta-modelo em sistemas de controle preditivo (MPC) utilizando técnicas de informação mútua." Universidade Federal de Sergipe, 2017. https://ri.ufs.br/handle/riufs/5028.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES<br>Model predictive control (MPC) strategies have become the standard for advanced control applications in the process industry. Significant benefits are generated from the MPC's capacity to ensure that the plant operates within its constraints more profitably. However, like any controller, after some time under operation, MPCs rarely function as when they were initially designed. A large percentage of performance degradation of MPC is associated with the deterioration of model that controller uses to predict process out
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Books on the topic "Model predictive controller (MPC)"

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Takács, Gergely. Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. Springer London, 2012.

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Khaled, Nassim, and Bibin Pattel. Practical Design and Application of Model Predictive Control: MPC for MATLAB and Simulink Users. Butterworth-Heinemann Limited, 2018.

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Takács, Gergely, and Boris Rohaľ-Ilkiv. Model Predictive Vibration Control: Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures. Springer, 2014.

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Model Predictive Vibration Control Efficient Constrained Mpc Vibration Control For Lightly Damped Mechanical Structures. Springer, 2012.

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Khaled, Nassim, and Bibin Pattel. Practical Design and Application of Model Predictive Control: MPC for MATLAB® and Simulink® Users. Elsevier Science & Technology Books, 2018.

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Vaez-Zadeh, Sadegh. Predictive, Deadbeat, and Combined Controls. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0005.

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In this chapter, three control methods recently developed for or applied to electric motors in general and to permanent magnet synchronous (PMS) motors, in particular, are presented. The methods include model predictive control (MPC), deadbeat control (DBC), and combined vector and direct torque control (CC). The fundamental principles of the methods are explained, the machine models appropriate to the methods are derived, and the control systems are explained. The PMS motor performances under the control systems are also investigated. It is elaborated that MPC is capable of controlling the mo
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Book chapters on the topic "Model predictive controller (MPC)"

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Mehmood, Usama, Shouvik Roy, Radu Grosu, Scott A. Smolka, Scott D. Stoller, and Ashish Tiwari. "Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45231-5_1.

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AbstractWe show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking con
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Pierini, Matteo, Paolo Fusco, Rodrigo Senofieni, Matteo Corno, Giulio Panzani, and Sergio Matteo Savaresi. "Trajectory Tracking for High-Performance Autonomous Vehicles with Real-Time Model Predictive Control." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_2.

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AbstractThis work is the development of a Model Predictive Controller (MPC) for the integrated control of lateral and longitudinal dynamics of a high-performance autonomous car, which follows a given trajectory on a racetrack. The MPC model is based on an Affine-Force-Input single-track nonlinear bicycle model that accounts for actuation dynamics and delays. The MPC problem is formulated as a quadratic problem, enabling efficient real-time solution with a specific quadratic programming (QP) solver. The controller is implemented in "Image missing" and tested in a real-time hardware-in-the-loop
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Klaučo, Martin, and Michal Kvasnica. "Inner Loops with Model Predictive Control Controllers." In MPC-Based Reference Governors. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17405-7_6.

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Seong, Junyeong, Sungjun Park, and Kunsoo Huh. "Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_39.

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AbstractThis paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness
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Gao, Haoyu, Chang Liu, Yingxi Piao, Sen Yang, Beiyan Jiang, and Shengbo Eben Li. "Design of Explicit and Lateral-Longitudinal Integrated Motion Controller with Safety Guarantee for Autonomous Vehicles." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_133.

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AbstractModel predictive control (MPC) is an effective method in lateral-longitudinal integrated control with safety guarantee for autonomous vehicles. But its computational burden is significant, making it challenging to meet real-time requirements. The contribution of this paper is to propose an explicitly solvable autonomous vehicle motion controller with lateral-longitudinal integrated characteristics and safety guarantee, achieved by integrating input-output controllers from exponential control Lyapunov function (ECLF) and exponential control barrier function (ECBF). We performed simulati
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Sathyamangalam Imran, Mohammed Irshadh Ismaaeel, Satyesh Shanker Awasthi, Michael Khayyat, Stefano Arrigoni, and Francesco Braghin. "A Rule-Defined Adaptive MPC Based Motion Planner for Autonomous Driving Applications." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_77.

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AbstractIn autonomous driving systems, motion planning to reach a given destination while avoiding obstacles becomes a task entirely managed by the on-board unit. In this work, we present a rule-defined motion planning algorithm for autonomous driving applications based on an adaptive Model Predictive Controller (MPC) framework. The motion planning task is first formulated as an Optimal Control Problem (OCP) subject to time-varying Control Barrier Function (CBF) constraints. It is then integrated within an MPC framework with adaptive weights settings, enabling the algorithm to dynamically adju
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Favelli, Stefano, Luis M. Castellanos Molina, Alessandro Mancarella, et al. "Fuel Economy Assessment of MPC-ACC on Powertrain Testbed." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_117.

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AbstractThe development and testing of Advanced Driver Assistance Systems (ADAS) is one of the most active fields in the automotive industry towards Automated Driving (AD). This work presents the deployment and testing of an Adaptive Cruise Control (ACC) based on Model Predictive Control (MPC). The goal is to design and validate through the experimental campaign a computationally efficient longitudinal dynamics controller and assess its fuel economy potential. The development of the control structure as well as the definition of the testing method for energy efficiency assessment are central a
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Bertipaglia, Alberto, Mohsen Alirezaei, Riender Happee, and Barys Shyrokau. "A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_89.

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AbstractThis paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the pre
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Sharma, Veena, Vineet Kumar, R. Naresh, and V. Kumar. "MPA Optimized Model Predictive Controller for Optimal Control of an AVR System." In Intelligent Data Engineering and Analytics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7524-0_6.

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Camacho, Eduardo F., and Carlos Bordons. "Multivariable MPC." In Model Predictive Control. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_6.

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Conference papers on the topic "Model predictive controller (MPC)"

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Kolinský, Jan, Pavel Jonáš, Jiří Hanzlík, Jakub Královec, and Petr Horáček. "PlantPAx MPC: Model Predictive Control Embedded in Programmable Controller and its Application." In 2025 25th International Conference on Process Control (PC). IEEE, 2025. https://doi.org/10.1109/pc65047.2025.11047370.

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Agrawal, Vivek, Richa Kapoor, and Avnish Singh. "The design and Implementation of a Model Predictive Controller (MPC) for a Plug Flow Reactor." In 2025 International Conference on Intelligent Control, Computing and Communications (IC3). IEEE, 2025. https://doi.org/10.1109/ic363308.2025.10956803.

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Desbiens, Andre, Michel Nadeau-Beaulieu, and Vincent Myrand-Lapierre. "Flight Trajectory Tracking for Training Simulator Qualification Using Model Predictive Control Strategy: A Case Study." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1049.

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A high-fidelity flight simulator is used to effectively train and evaluate pilots. The simulator must, however, be previously qualified by authorities by comparing the responses of the simulator to those of flight tests for several maneuvers. The use of a controller is permitted to make corrections to the simulator input signals. The simulator inputs and outputs must, however, be within tolerance bands defined by the authorities, compared to flight test data. This article presents a model predictive controller (MPC) and its evaluation on a takeoff and landing case. The method is already promis
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Kumar, Abhishek, and Om Prakash. "Analysis of Hybrid Airship Longitudinal Dynamics With Model Predictive Controller." In 2024 Second International Conference on Microwave, Antenna and Communication (MAC). IEEE, 2024. https://doi.org/10.1109/mac61551.2024.10837547.

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Zambrano, Consuelo Del Pilar Vega, Nikolaos A. Diangelakis, and Vassilis M. Charitopoulos. "Closed-Loop Data-Driven Model Predictive Control For A Wet Granulation Process Of Continuous Pharmaceutical Tablet Production." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.192802.

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In 2023, the International Council for Harmonisation (ICH) guideline for the development, implementation, and lifecycle management of pharmaceutical continuous manufacturing (PCM), was implemented in Europe. It promotes quality-by-design (QbD) and quality by control (QbC) strategies as well as the appropriate use of mathematical modelling. This development urges a harmonizing understanding across academia and industry for adoption of interpretable models instead of black-box models for advanced control strategies such as model predictive control (MPC), especially when applied in Good Manufactu
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Piper, Matthew, Pranav Bhounsule, and Krystel K. Castillo-Villar. "How to Beat Flappy Bird: A Mixed-Integer Model Predictive Control Approach." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5285.

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Flappy Bird is a mobile game that involves tapping the screen to navigate a bird through a gap between pairs of vertical pipes. When the bird passes through the gap, the score increments by one and the game ends when the bird hits the floor or a pipe. Surprisingly, Flappy Bird is a very difficult game and scores in single digits are not uncommon even after extensive practice. In this paper, we create three controllers to play the game autonomously. The controllers are: (1) a manually tuned controller that flaps the bird based on a vertical set point condition; (2) an optimization-based control
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Lenssen, Daan, Alberto Bertipaglia, Felipe Santafe, and Barys Shyrokau. "Combined Path Following and Vehicle Stability Control using Model Predictive Control." In WCX SAE World Congress Experience. SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0645.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This paper presents an innovative combined control using Model Predictive Control (MPC) to enhance the stability of automated vehicles. It integrates path tracking and vehicle stability control into a single controller to satisfy both objectives. The stability enhancement is achieved by computing two expected yaw rates based on the steering wheel angle and on lateral acceleration into the MPC model. The vehicle's stability is determined by comparing the two reference yaw rates to the actual one. Thus, the MPC controller
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Parry, Adam, Brandon Hencey, and Jon Zumberge. "Model Predictive Control for a Synchronous Machine With a Pulsed, Constant-Power Load." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3110.

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Abstract In this paper, the problem of controlling synchronous machines driving high pulsed, constant-power loads (CPLs) with fast ramp rates is investigated. Using a PI controller to provide offset-free tracking of the generator voltage in steady state, we design controllers using Model Predictive Control which act as a reference governor to ensure power quality constraints are met during transients. However, it is shown that a standard linear MPC algorithm creates a steady state offset due to model mismatch at off-nominal power levels resulting in loss of power quality. This problem is corre
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Vroemen, B. G., H. A. van Essen, A. A. van Steenhoven, and J. J. Kok. "Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation." In ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/98-gt-100.

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The feasibility of Model Predictive Control (MPC) applied to a laboratory gas turbine installation is investigated. MPC explicitly incorporates (input- and output-) constraints in its optimizations, which explains the choice for this computationally demanding control strategy. Strong nonlinearities, displayed by the gas turbine installation, cannot always be handled adequately by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combination of these. We implement these methods, using a nonlinear model of th
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Chen, Xiao, and Qian Wang. "A Data-Driven Thermal Sensation Model Based Predictive Controller for Indoor Thermal Comfort and Energy Optimization." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6131.

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This paper proposes a model predictive controller (MPC) using a data-driven thermal sensation model for indoor thermal comfort and energy optimization. The uniqueness of this empirical thermal sensation model lies in that it uses feedback from occupants (occupant actual votes) to improve the accuracy of model prediction. We evaluated the performance of our controller by comparing it with other MPC controllers developed using the Predicted Mean Vote (PMV) model as thermal comfort index. The simulation results demonstrate that in general our controller achieves a comparable level of energy consu
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Reports on the topic "Model predictive controller (MPC)"

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Bryson, Joshua, and Benjamin Gruenwald. Linear Parameter Varying (LPV) Model Predictive Control (MPC) of a High-Speed Projectile. DEVCOM Army Research Laboratory, 2021. http://dx.doi.org/10.21236/ad1150280.

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Yang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2240.

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California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered
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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0741.

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for th
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