The envelope approach for multiobjective optimization problems
IEEE Transactions on Systems, Man and Cybernetics
A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization
Journal of Computational and Applied Mathematics
IEEE Transactions on Neural Networks
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling, identification, and control of a class of nonlinear systems
IEEE Transactions on Fuzzy Systems
Adaptive differential dynamic programming for multiobjective optimal control
Automatica (Journal of IFAC)
Adaptive Learning and Control for MIMO System Based on Adaptive Dynamic Programming
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks - Part 1
IEEE Transactions on Neural Networks - Part 2
Data-Based Controllability and Observability Analysis of Linear Discrete-Time Systems
IEEE Transactions on Neural Networks - Part 2
Iterative Learning Control With Unknown Control Direction: A Novel Data-Based Approach
IEEE Transactions on Neural Networks - Part 2
Data-Based Identification and Control of Nonlinear Systems via Piecewise Affine Approximation
IEEE Transactions on Neural Networks - Part 2
Automatica (Journal of IFAC)
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In this paper, an optimal control method for a class of unknown discrete-time nonlinear systems with general multi-objective performance indices is proposed. In the design of the optimal controller, only available input-output data are required instead of known system dynamics, and the data-based identifier is established with stability proof. By the weighted sum technology, the multi-objective optimal control problem is transformed into the single objective optimization. To obtain the solution of the HJB equation, the novel finite-approximation-error adaptive dynamic programming (ADP) algorithm is presented with convergence proof. The detailed theoretic analyses for the relationship of the approximation accuracy and the algorithm convergence are given. It is shown that, as convergence conditions are satisfied, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the proposed method.