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The optimal tracking problem is addressed in the robotics literature by usinga variety of robust and adaptive control approaches. However, these schemes areassociated with implementation limitations such as applicability in uncertaindynamical environments with complete or partial model-based control structures,complexity and integrity in discrete-time environments, and scalability incomplex coupled dynamical systems. An online adaptive learning mechanism isdeveloped to tackle the above limitations and provide a generalized solutionplatform for a class of tracking control problems. This scheme minimizes thetracking errors and optimizes the overall dynamical behavior using simultaneouslinear feedback control strategies. Reinforcement learning approaches based onvalue iteration processes are adopted to solve the underlying Bellmanoptimality equations. The resulting control strategies are updated in real timein an interactive manner without requiring any information about the dynamicsof the underlying systems. Means of adaptive critics are employed toapproximate the optimal solving value functions and the associated controlstrategies in real time. The proposed adaptive tracking mechanism isillustrated in simulation to control a flexible wing aircraft under uncertainaerodynamic learning environment.

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