Speed-Up of Nonlinear Model Predictive Control for Robot Manipulators Using Task and Data Parallelism

The repetitive evaluation of computationally expensive functions in the objective and constraints represents a bottleneck in the solution of the underlying optimal control problem ( OCP) of nonlinear model predictive controllers (MPC) for robot manipulators. We address this problem by exploiting the parallel evaluation of such functions within the execution of a first-order and a second-order OCP solution algorithm, such as the proximal averaged Newton-type method for optimal control (PANOC) and the sequential convex quadratic programming (SCQP) method, respectively. The use of task parallelism with multicore executions and data parallelism with single-instruction- multiple-data (SIMD) instructions is shown to effectively reduce the solution time of the underlying OCP so that the satisfaction of real-time constraints in the deployment of MPC for robot manipulators can be achieved. Index Terms-Robot manipulator, model