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Minimum loss optimization of flywheel energy storage systems via distributed adaptive dynamic programming

OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第5期45卷 1950-1970页

作者： Xiao, Feng Ding, Zikang Wei, Bo Zhang, Cong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

In this article, a distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems (FESS). We first formulate a performance function aiming to reduce total losses of FESS in power distribution applications. Then we use the Hamilton-Jacobi-Bellman (HJB) equation to solve this optimal control problem. The solution of the HJB equation is approximated by neural networks. To achieve distributed control, we estimate the global variables in the HJB equation by using the dynamic average consensus algorithm. A barrier Lyapunov function and a saturation function are introduced to handle the issue of state and input constraints, respectively. Then the stability of the system is proved through the Lyapunov stability analysis. Finally the effectiveness of the proposed strategy is verified by simulations. Simulation results show that FESS can track the power command while minimizing total power losses by interacting with neighbors. The proposed algorithm leads to a loss reduction of 1.13%$$ 1.13\% $$ compared to the equal power distribution strategy. A distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems. The speed constraint and current constraint are considered in the systems. The proposed algorithm leads to a loss reduction of 1.13% compared to the equal power distribution strategy. image

关键词： adaptive dynamic programming flywheel energy storage systems loss optimization power distribution state and input constraints

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Model-free adaptive dynamic programming Based Near-optimal Decentralized Tracking Control of Reconfigurable Manipulators

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2018年第2期16卷 478-490页

作者： Zhao, Bo Li, Yuanchun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China

In this paper, a model-free near-optimal decentralized tracking control (DTC) scheme is developed for reconfigurable manipulators via adaptive dynamic programming algorithm. The proposed controller can be divided into two parts, namely local desired controller and local tracking error controller. In order to remove the normboundedness assumption of interconnections, desired states of coupled subsystems are employed to substitute their actual states. Using the local input/output data, the unknown subsystem dynamics of reconfigurable manipulators can be identified by constructing local neural network (NN) identifiers. With the help of the identified dynamics, the local desired control can be derived directly with corresponding desired states. Then, for tracking error subsystems, the local tracking error control is investigated by the approximate improved local cost function via local critic NN and the identified input gain matrix. To overcome the overall error caused by the substitution, identification and critic NN approximation, a robust compensation is added to construct the improved local cost function that reflects the overall error, regulation and control simultaneously. Therefore, the closed-loop tracking system can be guaranteed to be asymptotically stable via Lyapunov stability theorem. Two 2-degree of freedom reconfigurable manipulators with different configurations are employed to demonstrate the effectiveness of the proposed model-free near-optimal DTC scheme.

关键词： adaptive dynamic programming decentralized tracking control model-free near-optimal neural networks reconfigurable manipulators

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Robust tracking control of nonlinear unmatched uncertain systems via event-based adaptive dynamic programming

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NONLINEAR dynamicS 2022年第4期109卷 2831-2850页

作者： Dahal, Raju Kar, Indrani Indian Inst Technol Guwahati Elect & Elect Engn Gauhati 781039 Assam India

In this paper, a novel robust tracking control strategy for nonlinear unmatched uncertain systems is formulated using the event-based adaptive dynamic programming (ADP) approach. First, an augmented system is constructed based on the nonlinear system and the reference trajectory. Then, by forming an auxiliary system and introducing a discounted cost function, the event-based robust tracking control problem is transformed into the event-based optimal control problem of the auxiliary system. The event-based Hamilton-Jacobi-Bellman (HJB) equation associated with the event-based optimal control problem is solved using a single critic neural network (NN) under the ADP framework. A novel weight tuning rule for the critic network is formulated to avoid the necessity of an initial admissible control at the beginning of the weights tuning process. The obtained event-based controller is updated only at the triggering instants decided by the designed triggering condition, which helped in a significant reduction of resources used in computation and communication. Meanwhile, it is demonstrated that the obtained event-based controller can guarantee the tracking error's uniform ultimate boundedness. Furthermore, using the Lyapunov method, it is guaranteed that the established novel event-triggering rule ensures uniform ultimate boundedness of all signals associated with the closed-loop auxiliary system. Finally, the applicability of the proposed control scheme is demonstrated by providing two simulation examples.

关键词： Robust tracking Unmatched uncertainty Event-triggered control adaptive dynamic programming

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Optimal output tracking control of linear discrete-time systems with unknown dynamics by adaptive dynamic programming and output feedback

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2022年第16期53卷 3426-3448页

作者： Cai, Xuan Wang, Chaoli Liu, Shuxin Chen, Guochu Wang, Gang Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai Peoples R China Univ Shanghai Sci & Technol Inst Machine Intelligence Shanghai Peoples R China

This paper discusses the output-feedback-based model-free optimal output tracking control problem of discrete-time systems with completely unknown system models under mild assumptions. The only information that allows utilisation is the system output and the reference output. To overcome these challenges, the paper aims to solve a model-free optimal output regulation problem for achieving optimal output tracking control;solving an optimal output regulation problem is equivalent to solving a linear quadratic regulation (LQR) problem and a constrained static optimisation problem. The state reconstruction is first given to represent the system state in terms of the input and output sequences. A data-driven value iteration (VI) algorithm is then proposed to iteratively approximate the solution to the discrete-time algebraic Riccati equation (ARE) of the corresponding LQR problem on the basis of input and output data. Next, based on the iterative solutions with respect to the ARE, a model-free solution is provided for the corresponding constrained static optimisation problem. Finally, an alternative reference system equivalent to the original reference system is established to avoid the requirement of having the knowledge of the reference system dynamics and the reference state. A numerical example is employed to demonstrate the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming data-driven control optimal tracking control optimal output regulation output-feedback discrete-time systems

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Distributed observer-based hierarchical optimal consensus tracking with dynamic event-triggered adaptive dynamic programming

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NONLINEAR dynamicS 2023年第13期111卷 12319-12337页

作者： Chen, Zitao Chen, Kairui Zhang, Yun Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Seoul Natl Univ Dept Math Sci Seoul 08826 South Korea Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

Constraints on the communication topology bring challenges to optimal consensus protocol design. This paper investigates an observer-based optimal consensus tracking problem with a dynamic event-triggered mechanism for nonlinear multi-agent systems. Followers have access to only part of the states of the leader. The control process is completed hierarchically with two layers. In the distributed observer layer, an extended distributed observer is designed for each follower to reconstruct states of the leader by exchanging local estimated information with its neighbours. Parameters are selected step by step instead of solving linear matrix inequalities, guaranteeing the solvability of the observer. In the decentralized protocol layer, a dynamic event-triggered adaptive dynamic programming algorithm is designed for tracking the observer state, which serves as a reference. Inter-event time slots are enlarged by introducing a positive inner variable with first-order filter dynamics. Asymptotic stability of the tracking error and ultimately uniformly bounded property are proven rigorously. Finally, an illustrative example validates the effectiveness of the proposed design.

关键词： adaptive dynamic programming Multi-agent system Distributed observer dynamic event-triggered control

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A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2023年第8期54卷 1855-1872页

作者： Zhang, Yuhan Zou, Lei Liu, Yang Ding, Derui Hu, Jun Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough England Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai Peoples R China Harbin Univ Sci & Technol Dept Math Harbin Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin Peoples R China

Nonlinear dynamics is frequently encountered in practical applications. adaptive dynamic programming (ADP), which is implemented via actor/critic neural networks with excellent approximation capabilities, is appropriate to be used in finding the solution for the control problem in the presence of known/unknown nonlinear dynamics. The objective of this paper is to introduce state-of-the-art ADP-based algorithms and survey the recent advances in the ADP-based control strategies for nonlinear systems with various engineering-oriented complexities. Firstly, the main motivation of the ADP-based algorithms is thoroughly discussed, and the way of implementing the ADP-based algorithms is highlighted. Then, the latest research results concerning ADP-based control policy design for nonlinear systems are reviewed in detail, Finally, we conclude the survey by outlining the challenges and possible research topics in the future.

关键词： Nonlinear dynamics adaptive dynamic programming nonlinear control neural networks engineering-oriented complexities

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Policy Optimization adaptive dynamic programming for Optimal Control of Input-Affine Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第7期53卷 4339-4350页

作者： Lin, Mingduo Zhao, Bo Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China

In this article, a policy optimization adaptive dynamic programming (POADP) method is developed for optimal control of discrete-time unknown nonlinear systems, where the iterative control policy is parameterized to optimize the iterative Q-function directly. The relaxed condition for the learning rate is given to guarantee the convergence of the present algorithm. Furthermore, the Polyak-Lojasiewicz inequality is introduced to analyze the optimality, i.e., the iterative Q-function converges to the optimum within a given computational threshold under a finite iteration, and the rate of convergence (i.e., the required minimum number of iterations) for the developed POADP method is also illustrated. To ease real implementations, the iterative Q-function and the iterative control policy are approximated by employing an actor-critic structure. Then, an experiment-based method is developed to obtain the initial weights of actor-critic structure. Finally, numerical simulation results of two examples are provided to validate the effectiveness of the POADP algorithm.

关键词： Optimal control Convergence Optimization dynamic programming Nonlinear systems Performance analysis Iterative algorithms adaptive dynamic programming data based optimal control policy optimization (PO) reinforcement learning (RL)

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Optimal group consensus control for the second-order agents in the coopetition networks via adaptive dynamic programming and event-triggered methods

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OPTIMAL CONTROL APPLICATIONS & METHODS 2022年第5期43卷 1546-1567页

作者： Ji, Lianghao Li, Xi Zhang, Cuijuan Xiao, Yunpeng Li, Huaqing Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China

This article investigates the optimal group consensus control problem for the second-order multi-agent systems by using event-triggered and adaptive dynamic programming methods. The proposed method only uses the interaction information among the agents. Comprehensively considering the coopetition coupling interactions among the agents, a novel tracking error function is addressed for meeting the requirement of group consensus. On this basis, we use the Bellman optimality principle to formulate the optimal group consensus problem. To implement the proposed method, the actor-critic neural networks are used to approximate the iterative performance index functions and the control policies in real time. Meanwhile, the event-triggered condition with filter function is installed for each agent to reduce the control cost. Therefore, the weights of actor-critic networks are updated only at the event-triggered instants. The weight estimation errors and the control policy estimation errors are proved uniformly ultimately bounded and the Zeno behavior in the system can be avoided. Finally, the comparative simulation results show that the proposed method can greatly reduce the update times of the control policy of the system.

关键词： adaptive dynamic programming event-triggered control neural networks online policy iteration

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Online Optimization of Gear Shift and Velocity for Eco-Driving Using adaptive dynamic programming

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第1期7卷 123-132页

作者： Li, Guoqiang Goerges, Daniel Wang, Meng Beijing Inst Technol Sch Mech Engn Beijing 10081 Peoples R China Univ Kaiserslautern Inst Electromobil Dept Elect & Comp Engn D-67663 Kaiserslautern Germany Delft Univ Technol Fac Civil Engn & Geosci Stevinweg 1 NL-2600 GA Delft Netherlands

In this paper a learning-based optimization method for online gear shift and velocity control is presented to reduce the fuel consumption and improve the driving comfort in a car-following process. The continuous traction force and the discrete gear shift are optimized jointly to improve both the powertrain operation and the longitudinal motion. The problem is formulated as a nonlinear mixed-integer optimization problem and solved based on adaptive dynamic programming. A major difference compared to existing approaches is that the developed control method is model-free, i.e. it does not rely on vehicle models. It can address system nonlinearities and adapt to changes in engine characteristics (e.g. consumption map) during vehicle driving. The computation is efficient and enables possible real-time implementation. The proposed control method is studied for an urban driving cycle to evaluate the control performance with respect to the fuel economy and the driving comfort. Simulations indicate that the host vehicle can reduce the fuel consumption by 5.03% and 1.12% for two consumption maps in comparison to the preceding while keeping a desired inter-vehicle distance. The results further show a decrease of 1.59% and 2.32% in fuel consumption compared to a linear quadratic controller with the same gear shift schedule.

关键词： Gears Engines Force Biological system modeling Optimization Fuel economy Vehicle dynamics Eco-driving gear shift schedule velocity optimization adaptive cruise control adaptive dynamic programming reinforcement learning

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Output information-based intermittent optimal control for continuous-time nonlinear systems with unmatched uncertainties via adaptive dynamic programming

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ISA TRANSACTIONS 2024年 147卷 163-175页

作者： Wang, Weifeng Gu, Heping Mei, Jun Hu, Junhao South Cent Minzu Univ Sch Math & Stat Wuhan 430074 Peoples R China Sichuan Minzu Coll Dept Math & Stat Kangding City 626001 Peoples R China Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA

Intermittent control stands as a valuable strategy for resource conservation and cost reduction across diverse systems. Nonetheless, prevailing research is intractable to address the challenges posed by robust optimal intermittent control of nonlinear input-affine systems with unmatched uncertainties. This paper aims to fill this gap. Initially, we introduce an enhanced finite-time intermittent control approach to ensure stability within nonlinear dynamic systems harboring bounded errors. A neural networks (NNs) state observer is constructed to estimate system information. Subsequently, an optimal intermittent controller that operates within a finite time span, guaranteeing system stability by employing the Hamilton-Jacobi-Bellman (HJB) methodology. Furthermore, we devise an output information-based event-triggered intermittent (ETI) approach rooted in the robust adaptive dynamic programming (ADP) algorithm, furnishing an optimal intermittent control law. In this process, a critic NNs is introduced to estimate the cost function and optimal intermittent controller. Simulation results show that our proposed method is superior to existing intermittent control strategies.

关键词： adaptive dynamic programming Nonlinear input-affine systems Output information-based intermittent control Unmatched uncertainties Neural networks Finite-time stabilization

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