Abstract
In past robotics applications, Model Predictive Control (MPC) has often been limited to linear models and relatively short time horizons. In recent years however, research in optimization, optimal control, and simulation has enabled some forms of nonlinear model predictive control which find locally optimal solutions. The limiting factor for applying nonlinear MPC for robotics remains the computation necessary to solve the optimization, especially for complex systems and for long time horizons. This letter presents a new solution method which addresses computational concerns related to nonlinear MPC called nonlinear Evolutionary MPC (NEMPC), and then we compare it to several existing methods. These comparisons include simulations on torque-limited robots performing a swing-up task and demonstrate that NEMPC is able to discover complex behaviors to accomplish the task. Comparisons with state-of-the-art nonlinear MPC algorithms show that NEMPC finds high quality control solutions very quickly using a global, instead of local, optimization method. Finally, an application in hardware (a 24 state pneumatically actuated continuum soft robot) demonstrates that this method is tractable for real-time control of high degree of freedom systems.
Published Version
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