A Novel Infinite-Time Optimal Tracking Control Scheme for a Class of Discrete-Time Nonlinear Systems via the Greedy HDP Iteration Algorithm

By Huaguang Zhang, Qinglai Wei, Yanhong Luo. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2008

478 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f1f5cc153a932bdb22a8c34cc06c745c0f81fc57

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Citations in the corpus

Adaptive Dynamic Programming: An Introduction (2009, background): “In [82], Si et al summarizes the cross-disciplinary theoretical developments of ADP and overviews DP and ADP; and discusses their relations to artificial intelligence, approximation theory, control theory, operations research, and statistics.”
Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone (2016, methodology): “In [20], the optimal NN control problem was considered by using the greedy heuristic dynamic programming (HDP) for nonlinear discrete-time affine systems, and a new type of performance index is introduced to solve the tracking problem.”
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems (2016, methodology): “in [47], [52], [53], [55], and [59] can easily be justified by our developed value iteration algorithm.”
Data-Driven Optimal Consensus Control for Discrete-Time Multi-Agent Systems With Unknown Dynamics Using Reinforcement Learning Method (2017, methodology): “Among these classical RL methods, ADP is regarded as one of the core methodologies to achieve optimal control policies for diversities of optimal control issues, such as optimal control with constrained control input [16]–[19], optimal tracking control [20]–[24], optimal control with time delays [25”
Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning (2014, methodology): “The existing RL solutions to the optimal tracking [17]–[22] employs the dynamic inversion concept to obtain the feedforward control term a priori and then find the optimal feedback control term using RL techniques.”
Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems (2014, methodology): “[40] applied value iteration ADP to solve optimal tracking control problems for nonlinear systems.”
Model-Free Optimal Tracking Control via Critic-Only Q-Learning (2016, methodology): “Hence, the optimal tracking control problem is then reformulated as the optimal regulation problem of the error system with respect to the performance index, and then some RL approaches were proposed, such as, heuristic dynamic programming algorithm for nonlinear discrete-time systems [53] and with ”
An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games (2011, unknown): “Zhang et al. / Automatica 47 (2011) 207–214 209 of optimality, when V (x) ≥ 0 we have the following Hamilton– Jacobi–Bellman (HJB) equation HJB(V (x), w) := min w {Vt(x) + Vxf (x, u, w) +l(x, w)}”
Adaptive Dynamic Programming and Adaptive Optimal Output Regulation of Linear Systems (2016, background): “The ADP-based optimal trackers are also designed in [16], [45], [46], wherein the feedforward controllers are obtained by dynamic inversion, which requires not only the invertibility of input matrix but knowing or firstly identifying the complete system dynamics.”