Model Evolutionary Gain-Based Predictive Control (MEGa-PC) for Soft Robotics*

This paper details a reliable control method for highly nonlinear dynamical systems such as soft robots. We call this method model evolutionary gain-based predictive control or MEGa-PC. The method uses an evolutionary algorithm to optimize a set of controller gains via model predictive control. We demonstrate the performance of MEGa-PC in simulation for a single-link inverted pendulum and a three- link inverted pendulum, and on physical hardware for a three- joint continuum soft robot arm with six degrees of freedom. MEGa-PC is compared to prior work that used Nonlinear Evolutionary Model Predictive Control or NEMPC. The new method performs similarly to NEMPC in terms of accumulated cost over the entire trajectory, however, MEGa-PC generalizes better to real-world applications where safety is paramount, the dynamic model is uncertain, the system has significant latency, and where the previous sampling-based method (NEMPC) resulted in significant steady-state error due to model inaccuracy.