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Optimal Adaptive Ultra-Local Model-Free Control Based-Extended State Observer for PMSM Driven Single-Axis Servo Mechanism System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第5期60卷 7728-7745页

作者： El-Sousy, Fayez F. M. Amin, Mahmoud M. Soliman, Ahmed S. Mohammed, Osama A. Prince Sattam bin Abdulaziz Univ Dept Elect Engn Al Kharj 16273 Saudi Arabia Manhattan Coll Dept Elect & Comp Engn 4513 Manhattan Coll Pkwy Riverdale NY 10471 USA Florida Int Univ Dept Elect & Comp Engn Miami FL 33174 USA

This paper proposes an optimal adaptive ultra-local model-free control (OAULMFC) scheme for servo-mechanism actuated through a permanent-magnet synchronous motor drive. Owing to the uncertainties in real-time applications, the model-based controller cannot achieve good dynamic performance. Therefore, an OAULMFC based on ultra-local model is developed. The proposed control scheme requires estimating the total disturbance and the control input gains. Therefore, the composite control structure of the OAULMFC combines an ultra-local model-free controller (ULMFC), a robust adaptive extended state observer (ESO) and an optimal ${{\mathcal{H}}_\infty }$ controller. The ESO is developed to estimate the total disturbance whereas the optimal ${{\mathcal{H}}_\infty }$ controller is adopted as the auxiliary controller to provide the optimal control performance of the servo mechanism. For enhancing the estimation accuracy, an online direct heuristic dynamic programming (DHDP) strategy is utilized for adaptation the ESO parameters and control input gains of the ULMFC. Accordingly, an actor-critic neural-networks with DHDP is adopted to solve the Hamilton-Jacobi-Bellman (HJB) equation online. Concurrently, the actor neural-network provides the optimal control performance. Furthermore, the system stability and convergence conditions via Lyapunov theory is proved. The experimental results confirmed the robustness of the proposed OAULMFC in case of model perturbations and external disturbances compared with the conventional ULMFC.

关键词： Adaptation models Estimation Uncertainty Robustness dynamic programming Adaptive systems Servomechanisms Ultra-local model free-control extended state observer (ESO) PMSM servo-mechanism neural-network heuristic dynamic programming Hamilton-Jacobi-Bellman Lyapunov stability Lyapunov stability

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Adaptive learning control for triggered switched systems based on unknown direction control gain function

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INTERNATIONAL JOURNAL OF CONTROL 2024年第2期97卷 175-186页

作者： Qi, Yiwen Geng, Honglin Xing, Ning Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China

This paper investigates event-triggered switched heuristic dynamic programming (HDP) control for switched systems based on an unknown direction control gain function (UDCGF). First, the Nussbaum gain function is introduced to describe the unknown control direction, which makes the control behaviour of switched systems more flexible. The proposed variable threshold event-triggering (VTET) condition can significantly reduce computation while maintaining a certain system performance. Then, a detailed Lyapunov stability analysis based on the new tight bound is given, which reflects the asymptotic stability of the switched systems under the proposed switched HDP control method. The system mode-matched action-critic neural networks are employed to approximate the optimal triggered control law and the optimal switching value function, respectively. Subsequently, the boundedness analysis of neural network (NN) weights and the convergence analysis of HDP algorithm are given. At last, a numerical example is provided to verify the effectiveness of the proposed method.

关键词： Switched systems heuristic dynamic programming unknown direction control gain function event-triggering method

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Online Optimal DLQR-DFIG Control System Design via Recursive Least-Square Approach and State heuristic dynamic programming for Approximate Solution of the HJB Equation

Online Optimal DLQR-DFIG Control System Design via Recursive...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： da Fonseca Neto, Joao Viana Ferreira, Ernesto F. M. Moraes Rego, Patricia H. Fed Univ Maranhao UFMA Dept Elect Engn BR-65080040 Sao Luis MA Brazil

ISBN: (纸本)9781479906529

Our aim in this paper is to present a novel method for online optimal control system design via state heuristic dynamic programming (HDP) to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation by means of the recursive least-square (RLS) approach. Because the randomness nature associated to primary energy sources, the control of eolic and solar energy systems demands methods and technics that are suitable with the high degree of the environment uncertainties. The reinforcement learning (RL) and approximate dynamic programming (ADP) approaches furnish the key ideas and the mathematical formulations to develop optimal control system methods and strategies for alternative energy systems. We are proposing a online design method to establish control strategies for the the main unit of a eolic system that is the doubly fed induction generator (DFIG). The performance of proposed method is evaluated via computational experiments for discrete time HDP algorithms that map eigenstructure assignments in the stable Z-plane.

关键词： heuristic dynamic programming Multivariable Control dynamic programming Optimal Control Tuning Convergence Discrete Linear Quadratic Regulator Digital Control DFIG wind turbines FACTS Devices Doubly Fed Induction Generator

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On Distributed Implementation of Switch-Based Adaptive dynamic programming

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 7218-7224页

作者： Liu, Di Baldi, Simone Yu, Wenwu Chen, Guanrong Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli NL-9747 AG Groningen Netherlands Southeast Univ Sch Math Nanjing 210096 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases.

关键词： Switches Neural networks Games dynamic programming Switched systems Cybernetics Consensus distributed adaptive dynamic programming (ADP) heuristic dynamic programming optimal switching

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HDP Algorithms for Trajectory Tracking and Formation Control of Multi-Agent Systems

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IEEE ACCESS 2022年 10卷 27136-27146页

作者： Ferreira, Ernesto F. M. Da Fonseca Neto, Joao Viana Selmic, Rastko R. Fed Inst Maranhao Educ Dept BR-65930000 Acailandia Maranhao Brazil Univ Fed Maranhao Elect Engn Dept BR-65080805 Sao Luis Maranhao Brazil Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

A heuristic dynamic programming (HDP) algorithm for trajectory tracking and formation control of multi-agent systems (MAS) is presented in this paper. The selected HDP method allows for an online optimal control design. The multi-agent control problem is formulated as a leader-follower, where it is necessary for followers to maintain the assigned formation, while the leader follows the specified trajectory. Developed QR-Solver and RLS mu-QR-HDP-DLQR algorithms provide a new methodology to obtain the solution of the Hamilton-Jacobi-Bellman (HJB) equation. These proposed solutions are based on QR factorization to decrease the computational cost and avoid issues associated with numerical stability of conventional least squares (LS) method. The algorithms' performances such as convergence, numerical stability, and computational metrics, were experimentally evaluated for two control systems: aerial altitude control (one degree of freedom) and terrestrial robot (two degrees of freedom).

关键词： heuristic dynamic programming multi-agent systems leader-followers robots

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Aperiodic Sampling Artificial-Actual H∞ Optimal Control for Interconnected Constrained Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年 1-11页

作者： Song, Ruizhuo Liu, Lu Hu, Bo Univ Sci & Technol Beijing Beijing Engn Res Ctr Ind Spectrum Imaging Sch Automat & Elect Engn Beijing 100083 Peoples R China Nankai Univ Inst Robot & Automatic Informat Syst IRAIS Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot tjKLIR Tianjin 300350 Peoples R China

This paper analyzes decentralized/centralized event/ self-triggered schemes respectively according to a large-scale interconnected system with state limitation and uncertain interference, and devises a set of parallel controllers for the control input and the worst disturbance. Firstly, the system with asymmetric state constraints is transformed into an auxiliary system by means of coordinate transformation. And then, the physical and artificial systems are integrated to establish a parallel control structure to improve the performance of the system. Next, an aperiodic sampling robust H-infinity optimization method to save communication resources is proposed through zero-sum game (ZSG) theory, and the control pair sequence is obtained by solving the event-triggered Hamilton-Jacobi-Isaacs equation (ET-HJIE) via heuristic dynamic programming (HDP). Based on decentralized ET control (DETC), centralized ETC (CETC), decentralized self-triggered control (DSTC) and centralized STC (CSTC) are further proposed. In addition, the stability of the system under these four modes is proved, and the Zeno phenomenon is discussed. In the end, a simulation model is used to verify the control sequences within four trigger schemes, and satisfactory results are obtained.

关键词： Control systems Optimal control Game theory Mathematical models Interconnected systems dynamic programming Adaptive systems Artificial-actual interaction decentralized/centralized control event/self-triggered mode heuristic dynamic programming

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Learning Control of a Bioreactor System Using Kernel-based heuristic dynamic programming

Learning Control of a Bioreactor System Using Kernel-based H...

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10th World Congress on Intelligent Control and Automation (WCICA)

作者： Lian, Chuanqiang Xu, Xin Zuo, Lei Huang, Zhenhua Natl Univ Def Technol Inst Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467313988

To solve the learning control problem of a bioreactor system, a novel framework of heuristic dynamic programming (HDP) with sparse kernel machines is presented, which integrates kernel methods into critic learning of HDP. As a class of adaptive critic designs (ACDs), HDP has been used to realize online learning control of dynamical systems, where neural networks are commonly employed to approximate the value functions or policies. However, there are still some difficulties in the design and implementation of HDP such as that the learning efficiency and convergence of HDP greatly rely on the empirical design of the critic and so on. In this paper, by using the sparse kernel machines, Kernel HDP (KHDP) is proposed and its performance is analyzed both theoretically and empirically. Due to the representation learning and nonlinear approximation ability of sparse kernel machines, KHDP can obtain better performance than previous HDP method with manually designed neural networks. Simulation results demonstrate the effectiveness of the proposed method.

关键词： heuristic dynamic programming reinforcement learning kernel machines bioreactor Markov decision processes

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Adaptive critic design-based reinforcement learning approach in controlling virtual inertia-based grid-connected inverters

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 127卷 106657-106657页

作者： Saadatmand, Sepehr Shamsi, Pourya Ferdowsi, Mehdi Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

In this paper, an adaptive critic design (ACD) approach is proposed to control the phase and voltage of a grid-connected virtual synchronous generator (VSG). The penetration of fast responding inertia-less power converters significantly affect the stability of the power system, especially weak systems such as micro grids. The concept of virtual inertia addresses this concern by virtually emulating the behavior of a synchronous generator. However, the conventional VSG is designed based on two conditions: (i) fixed operating point and (ii) inductive grid connections. The performance of VSGs in low-voltage semi-resistive microgrids is far from optimal. To overcome the aforementioned concerns, a heuristic dynamic programing (HDP) approach is proposed to optimally control grid-connected VSGs. The neural-network-based inherence of the HDP enables the proposed technique to adapt to any impedance angle. The HDP controller includes two subnetworks: (i) the action network that controls the system optimally and (ii) the critic network, which evaluates the effectiveness of the action network. The simulation and experimental results are provided to evaluate the effectiveness of the proposed technique. As shown, the HDP-based approach illustrates a better performance in comparison with the conventional PI-based VSG in various operating conditions.

关键词： Adaptive critic design Approximate dynamic programming DC/AC converters heuristic dynamic programming Machine learning Reinforcement learning Virtual inertia Synchronverter

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Approximate dynamic programming with Gaussian Processes for Optimal Control of Continuous-Time Nonlinear Systems 21st

Approximate Dynamic Programming with Gaussian Processes for ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Beppu, Hirofumi Maruta, Ichiro Fujimoto, Kenji Kyoto Univ Grad Sch Engn Dept Aeronaut & Astronaut Kyoto 6158540 Japan

In this paper, a new algorithm for realization of approximate dynamic programming (ADP) with Gaussian processes (GPs) for continuous-time (CT) nonlinear input-affine systems is proposed to infinite horizon optimal control problems. The convergence for the ADP algorithm is proven based on the assumption of an exact approximation, where both the cost function and the control input converge to their optimal values, that is, the solution to the Hamilton-Jacobi-Bellman (HJB) equation. The approximation errors, however, are unavoidable in almost every case of applications. In order to tackle the problem, the proposed algorithm is derived with the proof of convergence, where the cost function and the control input, which are both approximated, converge to those of the ADP as the number of data points for GPs approaches infinity. A numerical simulation demonstrates the effectiveness of the proposed algorithm. Copyright (C) 2020 The Authors.

关键词： Approximate dynamic programming heuristic dynamic programming value iteration optimal control Gaussian processes nonparametric models

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Output Tracking Control Based on Adaptive dynamic programming With Multistep Policy Evaluation

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年第10期49卷 2155-2165页

作者： Luo, Biao Liu, Derong Huang, Tingwen Liu, Jiangjiang Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Texas A&M Univ Qatar Dept Sci Doha Qatar Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, the optimal output tracking control problem of discrete-time nonlinear systems is considered. First, the augmented system is derived and the tracking control problem is converted to the regulation problem with a discounted performance index, which relies on the solution of the Bellman equation. It is known that policy iteration and value iteration are two classical algorithms for solving the Bellman equation. Through analysis of the two algorithms, it is found that policy iteration converges fast while requires an initial admissible control policy, and value iteration avoids the requirement of an initial admissible control policy but converges slowly. To achieve the tradeoff between policy iteration and value iteration, the multistep heuristic dynamic programming (MsHDP) is proposed by using multistep policy evaluation scheme. The convergence of MsHDP algorithm is proved by demonstrating that it converges to the solution of the Bellman equation. Subsequently, neural network-based actor-critic structure is developed to implement the MsHDP algorithm. The effectiveness and advantages of the developed MsHDP method are validated through comparative simulation studies.

关键词： Adaptive dynamic programming (ADP) Bellman equation heuristic dynamic programming neural networks (NNs) output tracking control

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