Position and Orientation Tunnel-Following NMPC of Robot Manipulators Based on Symbolic Linearization in Sequential Convex Quadratic Programming

The tunnel-following nonlinear model predictive control (NMPC) scheme allows to exploit acceptable deviations around a path reference. This is done by using convex-over-nonlinear functions as objective and constraints in the underlying optimal problem (OCP). structure exploited algorithms such generalized Gauss-Newton (GGN) method or sequential convex quadratic programming (SCQP) reduce computational complexity of OCP solution. However, modeling effort engineering time required implement these methods high. We address reducing implementation SCQP, focusing on standard (SQP) where symbolic linearization applied part constraints. novelty this letter twofold. It introduces novel operator that applies transparent easy way solve nonconvex OCPs with SCQP method, meaningful representation an orientation-tunnel for robotic applications means constraint, which preserves convexity exploitation method. proposed technique demonstrated task 7-degrees-of-freedom manipulator.