Optimal Control of Wheeled Mobile Robots: From Simulation to Real World

We study the problem of taking simulations to the real world (RW) for autonomous robotic systems with dynamic uncertainties and unknown disturbances while maintaining the optimal performance and stability of the designed controller designed in simulation. In general, an optimal and robust controller that is designed through simulation often does not perform similarly when deployed in the RW. We focus on using simulations to generate an optimal control policy utilizing the Memetic algorithm (MA) iteratively. The simulation-to-RW performance and stability are realized by using an adaptive fuzzy system to learn the uncertain part of the dynamic model, disturbance and noises. We demonstrate experimentally that this method permits the development of optimal control design in simulations and integrates adaptive learning rules to enable precise and repetitive trajectory tracking for the wheeled mobile robot (WMR) with disturbances and uncertainties.