… GP based MPC are formulated separately for both subsystems. The resulting MPC …
Nonlinear Model Predictive Control (NMPC) algorithm and an existing constrained nonlinear GP …

… Predictive Control: MPC is a widely used closedloop control approach in robotics that
incorporates state and control constraints … for real-time control of robotic systems,” arXiv preprint …

… through constraint satisfaction. However, to apply NMPC to agile robotic systems operating
… despite motion uncertainty and computational constraints. NMPC performance is dependent …

This Chapter is an introduction to the field of MPC. Its basic idea and the rudimentary MPC
optimisation problems are defined, at first for Single-Input Single-Output (SISO) processes …

… Predictive control is an optimal framework for the regulation of constrained processes, with
… this work is the exploitation of model predictive control schemes for linear parameter varying …

… the control objectives in the absence of constraints. Finally, we present a reconfigurable
model predictive controller that achieves the control objective while satisfying constraints. …

… in dealing with constrained multivariable control problems. It is worthwhile to note that the
very first batch of papers include: The first paper on model predictive heuristic control (later …

… index along with a constrained finite horizon optimization … value while the constraints on the
states and control signals are … to be considered as feasible constraints therefore this process …

… a new constrained predictive … system. The developed Particle Swarm Optimization
algorithm is used to determine the predictive controls by minimizing a constrained multivariable …

… robotics systems have gained much attention, where flexibility and fast running time are the
keys used for improvement and approximation of real-robot … the constrained hybrid predictive …