Fast nonlinear model predictive control for teleoperation systems using computationally efficient optimization techniques

Abstract

The online computational load associated with Nonlinear Model Predictive Control (NMPC) is a serious barrier for its application to systems with fast dynamics. This paper addresses numerical approaches for fast and real-time feasible NMPC. The applicability of these methods to fast systems with sampling times in order of milliseconds is investigated through a nonlinear teleoperation system. The main ideas of the "real-time iteration" (RTI) approach for real-time optimization are addressed. Condensing approach to efficient structure exploitation of the described RTI scheme is then presented. The described method ensures a considerable reduction of the computational effort as required in real-time implementations. The NMPC algorithms based on these methods can therefore be carried out at higher sampling rates. The results show promising performance of the nonlinear teleoperation system using fast NMPC controller.

Nonlinear model predictive control for aggressive maneuvers in a variable pitch quadrotor based on the extended modal series method

“…There are many NMPC implementations that differ mainly in the nonlinear optimization algorithm: Recursive Neural Networks [12] Differential Evolution [14], Sequential Quadratic Programming [11], Particle Swarm Optimization [11], Dynamic Particle Swarm Optimization [15], Optimistic Optimization [16], Neighboring External Updates [17], Extended Modal Series Method [18], among others.…”