Data-driven Robust Fixed-iteration Learning Control

Reinforcement-Learning Robust Tracking Control of Discrete-Time Systems with Diagonal Scaling

This paper addresses a robust tracking problem for linear discrete-time systems by proposing a reinforcement learning (RL) control method based on a diagonal-scaling strategy, offering a solution tailored to the demands of enhanced reliability. To overcome a common limitation in policy-iteration-based RL design, namely...

Data-driven preview Q-learning for H∞ tracking control of unknown linear discrete-time systems.

Conventional tracking controllers relying only on the current tracking error may suffer from tracking lag when the reference trajectory changes rapidly. To address this problem, a data-driven preview Q-learning algorithm is presented for H∞ tracking control of unknown linear discrete-time systems. The algorithm integra...

Robust data-induced learning control for uncertain nonlinear systems via dual-timescale adaptation.

In addressing the challenge of achieving precise tracking control for uncertain nonlinear systems under complex disturbances, this paper proposes a Robust Data-induced Learning Control (R-DiLC) framework. The R-DiLC introduces a dual-timescale adaptation mechanism that effectively compensates for both iteration-invaria...

Model-Free Adaptive Learning Control with Prescribed Performance of Nonlinear Systems

This paper proposes the data-driven prescribed performance adaptive learning control (DPPALC) algorithm to accomplish the prescribed performance control for nonlinear systems, which can ensure that the tracking error converges to the prescribed region in the prescribed time. Considering that system model is unavailable...

Pure Feedback Control for Nonlinear Systems Based on Predefined-Time Performance Function

This paper investigates the predefined-time adaptive neural tracking control problem for a class of nonlinear pure feedback systems with full state constraints, and proves that all closed loop signals are predefined-time-bounded and the tracking error meets the preset performance specifications.

Adaptive finite-time command filter backstepping control with error compensation for uncertain strict-feedback nonlinear systems