Data-based robust optimal control of continuous-time affine nonlinear systems with matched uncertainties

Wang, Ding; Li, Chao; Liu, Derong; Mu, Chaoxu

doi:10.1016/j.ins.2016.05.034

Cited by 62 publications

(16 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the existence of unknown attacks, it is difficult or even impossible to investigate the systems 3and 4directly. Inspired by the idea of classical works [23][24][25][26], we convert this robust control issue of the systems 3and 4into the optimal control problem of the nominal system (2). e main idea is that, with the system data and models, we can first attain the optimal control policy through ADP algorithms.…”

Section: Problem Statement For Power Systemmentioning

confidence: 99%

A Brief Overview of Optimal Robust Control Strategies for a Benchmark Power System with Different Cyberphysical Attacks

Wang

Zhou

et al. 2021

Complexity

View full text Add to dashboard Cite

Security issue against different attacks is the core topic of cyberphysical systems (CPSs). In this paper, optimal control theory, reinforcement learning (RL), and neural networks (NNs) are integrated to provide a brief overview of optimal robust control strategies for a benchmark power system. First, the benchmark power system models with actuator and sensor attacks are considered. Second, we investigate the optimal control issue for the nominal system and review the state-of-the-art RL methods along with the NN implementation. Third, we propose several robust control strategies for different types of cyberphysical attacks via the optimal control design, and stability proofs are derived through Lyapunov theory. Furthermore, the stability analysis with the NN approximation error, which is rarely discussed in the previous works, is studied in this paper. Finally, two different simulation examples demonstrate the effectiveness of our proposed methods.

show abstract

Section: Problem Statement For Power Systemmentioning

confidence: 99%

A Brief Overview of Optimal Robust Control Strategies for a Benchmark Power System with Different Cyberphysical Attacks

Wang

Zhou

et al. 2021

Complexity

View full text Add to dashboard Cite

show abstract

“…In this subsection, the data of the PV power is measured by specific sensors and is stored in the control unit. Recently, intelligent algorithms have been developed for obtaining information from data [26][27][28][29][30][31]. Therefore, these data are available to design an observer to forecast the future PV power.…”

Section: Neural Network Observermentioning

confidence: 99%

“…The gradient descent method is used to update the weights of the neural network observer in the backpropagation [26][27][28]32]. Define the input-to-hidden weight vector w 1 t and the hidden-to-output weight vector w 2 t as…”

Section: Neural Network Observermentioning

confidence: 99%

Observer-Based Load Frequency Control for Island Microgrid with Photovoltaic Power

Liu

et al. 2017

International Journal of Photoenergy

Self Cite

View full text Add to dashboard Cite

As renewable energy is widely integrated into the power system, the stochastic and intermittent power generation from renewable energy may cause system frequency deviating from the prescribed level, especially for a microgrid. In this paper, the load frequency control (LFC) of an island microgrid with photovoltaic (PV) power and electric vehicles (EVs) is investigated, where the EVs can be treated as distributed energy storages. Considering the disturbances from load change and PV power, an observer-based integral sliding mode (OISM) controller is designed to regulate the frequency back to the prescribed value, where the neural network observer is used to online estimate the PV power. Simulation studies on a benchmark microgrid system are presented to illustrate the effectiveness of OISM controller, and comparative results also demonstrate that the proposed method has a superior performance for stabilizing the frequency over the PID control.

show abstract

“…In recent years, Lewis, Jagannathan, Murray, Powell and Liu have made great contribution to the development of the ADP algorithms. According to Werbos, ADP approaches were classified into several schemes including heuristic dynamic programming (HDP), action dependent HDP, dual heuristic programming (DHP), action dependent DHP, globalized DHP (GDHP), and ADGDHP.…”

Section: Introductionmentioning

confidence: 99%

“…There are some synonyms of ADP including approximate dynamic programming, 8,9 adaptive dynamic programming, [10][11][12] adaptive critic designs, 13,14 neural dynamic programming, 15,16 neurodynamic programming, 17 and reinforcement learning. 18,19 In recent years, Lewis,[20][21][22] Jagannathan, 23, 24 Murray, 25 Powell 26 and Liu [27][28][29][30] have made great contribution to the development of the ADP algorithms. According to Werbos,5 ADP approaches were classified into several schemes including heuristic dynamic programming (HDP), action dependent HDP, dual heuristic programming (DHP), action dependent DHP, globalized DHP (GDHP), and ADGDHP.…”

Section: Introductionmentioning

confidence: 99%

Optimal control for discrete‐time systems with actuator saturation

Qiao

Wei

Zhao

2017

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary In this study, we use generalized policy iteration approximate dynamic programming (ADP) algorithm to design an optimal controller for a class of discrete‐time systems with actuator saturation. A integral function is proposed to manage the saturation nonlinearity in actuators and then the generalized policy iteration ADP algorithm is developed to deal with the optimal control problem. Compared with other algorithm, the developed ADP algorithm includes 2 iteration procedures. In the present control scheme, 2 neural networks are introduced to approximate the control law and performance index function. Furthermore, numerical simulations illustrate the convergence and feasibility of the developed method.

show abstract

Data-based robust optimal control of continuous-time affine nonlinear systems with matched uncertainties

Cited by 62 publications

References 51 publications

A Brief Overview of Optimal Robust Control Strategies for a Benchmark Power System with Different Cyberphysical Attacks

A Brief Overview of Optimal Robust Control Strategies for a Benchmark Power System with Different Cyberphysical Attacks

Observer-Based Load Frequency Control for Island Microgrid with Photovoltaic Power

Optimal control for discrete‐time systems with actuator saturation

Contact Info

Product

Resources

About