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An Anti-Disturbance adaptive control Approach for Automated Vehicles in Mixed Connected Traffic Environment

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第12期24卷 15274-15287页

作者： Liu, Can Zheng, Fangfang Li, Ruijie Liu, Xiaobo Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data Appl Western Hitech Zone Chengdu 611756 Sichuan Peoples R China

In the foreseeable future, a coexistence of human-driven vehicles (HVs) and connected automated vehicles (CAVs) is expected in traffic flow systems. Effectively controlling CAVs to improve overall traffic performance and stability is a crucial yet challenging issue, especially when considering the uncertain behavior of HVs. This study proposes an optimal CAV controller for mixed connected traffic based on the adaptive model predictive control (AMPC) method. To mitigate the instability of the mixed platoon, the concept of loose disturbance string stability (LDSS) is introduced and integrated into the controller. Instead of using a fixed platoon size, a platoon formation criterion is established to dynamically calculate the number of vehicles in a platoon, taking into account the uncertain behavior of HVs, such as sudden deceleration and lane changes. An adaptive parameter tuning approach is incorporated into the MPC-based control framework to enhance the control performance in terms of stability, accuracy and disturbance recovery ability. The effects of the proposed CAV controller on overall traffic performance are investigated through two simulation experiments. In the first experiment, the performance of the proposed controller and LDSS are verified under three scenarios: sudden deceleration, vehicle leaving, and vehicle cut-in. In the second experiment, the proposed AMPC method is compared with two existing models: the linear quadratic regulation -based and acceleration-based connected cruise control models. The comparison results demonstrate that the proposed control model ensures LDSS and outperforms the other two approaches in terms of disturbance mitigation, albeit with a slight sacrifice of traffic efficiency.

关键词： Mixed connected traffic dynamic platoon size adaptive model predictive control disturbance string stability

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Constrained robust model predictive control embedded with a new data-driven technique

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IET control THEORY AND APPLICATIONS 2020年第16期14卷 2395-2405页

作者： Yang, L. Lu, J. Xu, Y. Li, D. Xi, Y. Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

In the control field, the adaptive model predictive control (AMPC) has the capability of taking effective control actions on unknown-but-bounded time-independent or slowly time-varying systems coupling with constraints. In essence, AMPC estimates the uncertain parameters or uncertainty set online by utilising historian data to extract model information. The model estimation procedure imposes some specific conditions on data and these extra conditions have restricted its practical use. To overcome these problems, a new data-driven control methodology is presented that integrates the data-driven concept into robust model predictive control (RMPC) architecture for unknown-but-bounded time-independent or slowly time-varying plant. The key novelty is to employ historian data to derive control policy and make a prediction in replacement with the complicated procedure of utilising data to estimate model parameters. A data-driven RMPC algorithm is developed within the robust model predictive control framework with the fulfilment of recursive feasibility and stability. The authors display the highlights of the data-driven model predictive control controller through two simulation examples. The resulting controller is verified to reduce conservativeness and increase the closed-loop performance of the system.

关键词： closed loop systems robust control control system synthesis predictive control time-varying systems uncertain systems constrained robust model predictive control data-driven technique control field adaptive model predictive control AMPC effective control actions unknown-but-bounded time-independent time-varying systems historian data model information model estimation procedure data-driven control methodology data-driven concept robust model predictive control architecture time-varying plant control policy utilising data model parameters data-driven RMPC algorithm robust model predictive control framework data-driven model predictive control controller

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Polygonal constrained adaptive MPC dynamic obstacle avoidance based on safety trigger mechanism 36

Polygonal constrained adaptive MPC dynamic obstacle avoidanc...

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36th Chinese control and Decision Conference (CCDC)

作者： Zheng, Ranran Li, Runmei Beijing Jiaotong Univ Sch Automat & Intelligence Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In order to improve the adaptability of autonomous vehicles in dynamic obstacle environments, this paper designs a novel constrained obstacle avoidance strategy based on adaptive model predictive control (MPC) algorithm. Firstly, an adaptive MPC prediction model is established based on the vehicle kinematics model. Secondly, a safety trigger mechanism is defined to update the polygonal constraints established in this paper, which is introduced into the MPC optimization problem and solved by a quadratic programming algorithm. Finally, the results of the integrated obstacle avoidance framework established in this paper are tested in several scenarios. The results show that the algorithm can effectively avoid collisions in both static and dynamic obstacle environments, which lays the foundation for the subsequent development of real scenarios.

关键词： adaptive model predictive control dynamic obstacle polygonal constraint obstacle avoidance integration

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Integrating Demand Response Technique with Effective control Algorithm for Energy Management System in a Renewable Energy Based-Micro-grid 32

Integrating Demand Response Technique with Effective Control...

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32nd Southern African Universities Power Engineering Conference (SAUPEC)

作者： Gbadega, Peter Anuoluwapo Sun, Yanxia Akindeji, Kayode Timothy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa Durban Univ Technol Smart Grid Res Ctr Dept Elect Power Engn ZA-4001 Durban South Africa

ISBN: (纸本)9798350371345

This paper addresses the challenges of demand response (DR) in energy management, especially within microgrids. It emphasizes the need for effective control strategies and market structures to maximize DR resource potential. The intermittent, stochastic, and distributed nature of generation and consumption patterns requires novel control algorithms. Therefore, this paper investigates the demand response technique for the energy management system in a micro-grid based on adaptive model predictive control (AMPC). More so, the objective of the DR technique in this paper is to use the available renewable energy resources optimally, maximize the economic benefit, reduce the peak load demand, and manage load consumption patterns, improving micro-grid operation. The simulation results from the MATLAB/Simulink environment have shown that implementing the DR technique for energy management in microgrids reduces the peak load demand and, consequently, minimizes the operation costs of the system.

关键词： adaptive model predictive control demand response demand-side management energy management system and optimization problem

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adaptive MPC of a class of Switched Linear Systems with Unknown System Matrices 43

Adaptive MPC of a class of Switched Linear Systems with Unkn...

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43rd Chinese control Conference (CCC)

作者： Chen, Hui Sun, Licheng Ma, Hongbin Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper presents an adaptive model predictive control for a class of switched linear stochastic systems with unknown system matrices. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of on-line estimated models, which are built on a switching recursive weighted least-squares (WLS) algorithm together with a random regularization method. By incorporating an attenuating excitation signal into adaptive MPC, the switched system satisfies the state constraints and control constraints after finite steps.

关键词： adaptive model predictive control Weighted Least-Squares Switched Linear Systems

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adaptive TS fuzzy MPC based on Particle Swarm Optimization-Cuckoo Search algorithm

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ISA TRANSACTIONS 2022年 131卷 598-609页

作者： Taieb, Adel Salhi, Hichem Chaari, Abdelkader Univ Tunis Natl High Sch Engineers Tunis Lab Ind Syst & Renewable Energies Engn 05 Ave Taha Hussein Tunis Tunisia Univ Tunis Manar Fac Sci Tunis Lab Anal Design & Control Syst Tunis 1002 Tunisia

A novel technique to building an adaptable fuzzy model predictive control (AFMPC) is suggested in this study, based on the algorithm Particle Swarm Optimization-Cuckoo Search (PSOCS). This technique combines the particle swarm optimization (PSO) algorithm's iterative scheme with the Cuckoo Search (CS) algorithm's searching approach. To identify the system parameters at each time step, an on-line adaptive fuzzy identification is used. Based on a predictive technique, these factors are utilized to generate the goal function. The PSOCS method is then used to solve the optimization issue and select the best control signal. The suggested controller's utility is demonstrated using an experimental communicating three-tank system, in which the proposed approach-based PSOCS algorithm outperforms both the approach-based CS and the approach-based PSO algorithms.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： adaptive model predictive control Takagi-Sugeno Cuckoo Search Particle Swarm Optimization Nonlinear system

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Linear parameter-varying-based transition flight control design for a tilt-rotor aircraft

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2022年第16期236卷 3354-3369页

作者： Qu, Shen Zhu, Guoming Su, Weihua Swei, Sean Shan-Min Michigan State Univ Dept Mech Engn 1497 Engn Res CourtRoom E148 E Lansing MI 48824 USA Univ Alabama Dept Aerosp Engn & Mech Tuscaloosa AL USA Khalifa Univ Dept Aerosp Engn Abu Dhabi U Arab Emirates

This paper presents the development of novel transition flight controllers for a class of urban air mobility aircrafts configured with a fixed-wing and six distributed electric rotor assemblies. Only the two tilt-rotors are utilized for thrust vectoring during transition flight from hovering to steady-level flight, while the four lift-rotors are modulated with aerodynamic lift induced by fixed-wing to maintain stable altitude-hold. Three tractable tilt-rotor articulation profiles are proposed by taking into account of various aircraft and hardware constraints. Given a predefined nominal tilting profile, a family of linear models is obtained by linearizing the nonlinear aircraft model at multiple tilt-rotor angular positions along the tilting profile. Using tilt-rotor angular position as a scheduling parameter, a discrete-time linear parameter-varying model can be constructed, which is then used to develop a novel transition flight control architecture that integrates the adaptive model predictive control law with feedforward effect of the dynamic reference compensation. The simulation results demonstrate the effectiveness of proposed transition flight controllers and its robustness subject to external disturbance.

关键词： Electric vertical takeoff and landing tilting optimization linear parameter-varying electric vertical takeoff and landing model model predictive control with dynamic reference compensation adaptive model predictive control tilt-rotor aircraft

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Improved grey wolf optimizer based on opposition and quasi learning approaches for optimization: case study autonomous vehicle including vision system

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第7期55卷 5597-5620页

作者： Elsisi, M. Natl Taiwan Univ Sci & Technol Ind 4 0 Implementat Ctr Ctr Cyber Phys Syst Innovat Taipei 10607 Taiwan Benha Univ Fac Engn Shoubra Dept Elect Engn 108 Shoubra StBO Box 11241 Cairo Egypt

The adapting of lateral deviation during the change of road curvature with less error, system settling time, and overshoot is the main challenge against the steering angle control of autonomous vehicles (AVs). In this regard, this paper introduces new learning techniques defined opposition-based learning (OBL) and quasi OBL (QOBL) to improve the exploration as well as exploitation manner of the grey wolf optimizer (GWO). The involved approach can enhance the searching behavior of the original GWO against the trapping in local optima. The proposed modified GWO (MGWO) is applied to detect the optimal factors of the adaptive model predictive control (AMPC) for AVs. The suggested MGWO-based AMPC is evaluated with the classical MPC and the adaptive fuzzy logic controller. Furthermore, the inspired MGWO is compared with the original GWO, neural network algorithm (NNA), heap-based optimizer, and equilibrium optimizer in literature. The performance of the introduced technique is tested to follow different road curvatures. Moreover, the presented method is approved against the time delay of the vision system and the produced uncertainty of system variables from the change of vehicle speed and look-ahead distance. Furthermore, the introduced MGWO-based AMPC can tackle the system settling time and overshoot to be less than 1 s and 1.696% respectively for the response of lateral deviation compared to other techniques. The attained results emphasize that the proposed MGWO-based AMPC controller has high damped and effective performance evaluated with other controllers.

关键词： adaptive model predictive control Autonomous vehicles Grey wolf optimizer Vision system

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predictive control of adaptive Micro-Grid Energy Management System Considering Electric Vehicles Integration

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2022年第1期59卷 175-204页

作者： Peter Anuoluwapo, Gbadega Akshay Kumar, Saha Univ KwaZulu Natal Sch Engn Discipline Elect Elect & Comp Engn Howard Coll CampusKing George V Ave ZA-4041 Durban South Africa

This paper addresses the problems of control and energy management in micro-grid with the incorporation of renewable energy generation, hybrid storage technologies, and the integration of the electric vehicles (EVs) with vehicle to grid (V2G) technology. The adaptive model predictive control (AMPC) technique is used to optimize the charge/discharge of the EVs in a receding horizon manner in order to reduce operational cost in a renewable energy-based micro-grid. V2G systems integration can be a crucial element in the assurance of network reliability against variability in loads. In this context, the paper presents an AMPC algorithm for the optimization of a micro-grid coupled with a V2G system consisting of six electric vehicle charging stations. The proposed algorithm effectively manages the use of renewable energy sources, vehicles charge, energy storage units, and the purchase and sale of electric power to the external network. Two scenarios are investigated in this paper to examine the performance of the proposed controller to manage the renewable energy sources in the micro-grid system. The first case uses a load shifting mechanism to solve the charge management problem during a known interval of parking time. The second case introduces the EVs with V2G capabilities when connected with the micro-grid. In this case, the vehicle battery collaborates with the ESS of the micro-grid to maximize costs benefits and mitigate the intermittency of renewable generation. Furthermore, other benefits of V2G concepts, such as voltage and frequency control for the micro-grid stability, are investigated. Therefore, it is evident from the obtained results that the proposed control algorithm was able to effectively manage the renewable energy sources, energy storage units, vehicles charge, and the purchase and sale of electric power with the grid.

关键词： adaptive model predictive control Energy management system Electric vehicles Vehicle to grid technology Grid reliability Load shifting Optimization problem and MATLAB/Simulink

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Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay 27

Comparison of artificial neural network adaptive control tec...

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27th International Conference on Methods and models in Automation and Robotics (MMAR)

作者： Gus, Bartlomiej Mozaryn, Jakub Warsaw Univ Technol Fac Mechatron Inst Automat Control & Robot Warsaw Poland

ISBN: (纸本)9798350311075

This research paper compares two neural-networkbased adaptive controllers, namely the Hybrid Deep Learning Neural Network controller (HDLNNC) and the adaptive model predictive control with Nonlinear Prediction and Linearization along the Predicted Trajectory (AMPC-NPLPT), for controlling a nonlinear object with delay. Specifically, the study investigates the effect of delay on the accuracy of the two controllers. The experimental results demonstrate that the AMPC-NPLPT approach outperforms HDLNNC regarding control accuracy for the given nonlinear object control problem.

关键词： Artificial Neural Network Nonlinear object Hybrid Deep Learning Neural Network controller adaptive model predictive control Time-Delay Object

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