Inverse optimal control of general nonlinear systems based on adaptive dynamic programming

Adaptive Compensation and Adaptive Optimal Control of a Single Link Manipulator

This paper presents an online solution to the finite-horizon optimal tracking control problem for continuous-time nonlinear systems with partially unknown dynamics, based on an Adaptive Dynamic Programming (ADP) approach. The method employs a dual-approximation identifier–critic neural network (NN) architecture, with b...

Iterative Learning Control for Unknown Nonlinear Systems Based on Data-Driven Model-Free Feedback Linearization

This paper proposes a novel iterative learning control (ILC) scheme for unknown affine nonlinear systems by integrating model-free feedback linearization with two-dimensional (2D) structure of the controlled dynamics. The approach eliminates the requirement for prior model knowledge by employing model reference adaptiv...

Koopman-based model predictive control for nonlinear systems with bounded model uncertainty

Robust Adaptive Neural Network-Based Backstepping Tracking for Second-Order Euler-Lagrange Systems with Unknown Parameters

A continuous adaptive control law is developed that eliminates chattering typically caused by discontinuous robust terms and proves that all closed-loop signals are uniformly ultimately bounded, achieving asymptotic trajectory tracking with smooth control inputs.

Reinforcement Learning‐Based Optimal Prescribed‐Time Tracking Control for Strict‐Feedback Systems With Unknown Affine Terms

A novel prescribed‐time optimal (PTO) tracking control scheme for nonlinear strict‐feedback systems with unknown affine terms based on radial basis function (RBF) neural networks is proposed and it is demonstrated for the first time that the critic‐actor weights can converge exponentially to the same values.

Model-Free Q-Learning for Infinite-Horizon Stochastic Linear Quadratic Problems with Regime Switching

On-policy and off-policy Q-learning algorithms that learn the optimal controller solely from online state trajectory data are developed, specifically developing on-policy and off-policy Q-learning algorithms that learn the optimal controller solely from online state trajectory data.