Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net

TL;DR

This paper introduces a novel adaptive control framework combining meta-reinforcement learning and conditional generative adversarial networks to achieve fast, optimal, and data-driven control of nonlinear systems with uncertainties, demonstrated on robotic and musculoskeletal systems.

Contribution

It proposes a new control architecture that integrates CGANs and meta-reinforcement learning for rapid adaptation without system identifiers, improving control efficiency and robustness.

Findings

Faster adaptation and reduced control effort compared to standard methods.

Effective uncertainty modeling with low-dimensional latent space.

Successful application on robotic and large-scale systems with disturbances.

Abstract

The control of nonlinear systems with unknown dynamics has been a significant field of research for many years. This paper presents a novel data-driven optimal adaptive control structure with less control effort and faster adaptation than standard adaptive control counterparts. The proposed control structure utilizes the system's recorded data to increase the speed of adaptation and performance dramatically. In this study, we employ a conditional generative adversarial net (CGAN) as a novel central pattern generator to reproduce the steady-state harmonic pattern of the control signals matching the system's uncertainties over a wide range. We can also use the CGAN architecture as a fault detector. The CGAN provides a low-dimensional latent space of uncertainties. It enables rapid and convenient adaptation when there are many parametric uncertainties, especially for large-scale systems. Then, we introduce a novel meta-reinforcement learning framework to adapt the latent space of CGAN to the system's uncertainties as an optimal direct adaptive controller without any system identifier. Another part of the control structure is a regulator that achieves semi-global asymptotic tracking using the Lyapunov stability analysis. Finally, via some simulations, we evaluate the capabilities of the proposed designs on two dynamical systems, a robot manipulator and a large-scale musculoskeletal structure, in the presence of disturbance and perturbation.