Evolving behaviors for bounded-flow tracking control of second-order dynamical systems

摘要

A two-stage methodology for the development of nonlinear analytical controllers for tracking control in second-order dynamical systems subject to flow variable constraints is proposed. It extends the concepts of behavior based control to describe the system as the summation of its unforced, forced, and learned behaviors. While the unforced behavior is characterized by its analytical dynamical model, the forced and learned behaviors are introduced in the system by means of a Control-Theory-based controller and an evolutionary learning process based in the Genetic Programming paradigm. The integration of both approaches in a unified framework allows the system to exhibit a good tracking performance while keeping the flow variable bounded to a desired value, parametrized as a boundary interval. A set of 180993 learned behaviors, which preserves asymptotic convergence to the desired behavior while achieving a bounded flow variable, were discovered by the evolutionary process. Simulation results show the effectiveness of the found nonlinear tracking controllers with the highest fitness value, as well as the one with the lower structural complexity. A performance comparison between numerical simulations and real-time experiments for a mechatronic prototype is also provided to illustrate the feasibility of the proposed method in real-world applications.