Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems

TL;DR

This paper introduces an online adaptive learning control method for dynamical systems that optimizes tracking performance without requiring prior system models, using reinforcement learning and real-time updates.

Contribution

It presents a novel model-independent adaptive tracking mechanism employing reinforcement learning and adaptive critics for real-time control in uncertain environments.

Findings

Successfully applied to control a flexible wing aircraft.

Minimized tracking errors effectively in simulation.

Operates without prior knowledge of system dynamics.

Abstract

The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical environments with complete or partial model-based control structures, complexity and integrity in discrete-time environments, and scalability in complex coupled dynamical systems. An online adaptive learning mechanism is developed to tackle the above limitations and provide a generalized solution platform for a class of tracking control problems. This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneous linear feedback control strategies. Reinforcement learning approaches based on value iteration processes are adopted to solve the underlying Bellman optimality equations. The resulting control strategies are updated in real time in an interactive manner without requiring any information about the dynamics of the underlying systems. Means of adaptive critics are employed to approximate the optimal solving value functions and the associated control strategies in real time. The proposed adaptive tracking mechanism is illustrated in simulation to control a flexible wing aircraft under uncertain aerodynamic learning environment.