… For the unknown robotic system, realizing the dynamic compensation of the uncertain
nonlinear model under the tube-based MPC scheme is still difficult. How to construct a proper …

… as constraints, disturbances, and complex modeling. The importance of control algorithms
capable of negotiating nonlinear systems in … that are prevalent in robotic systems. This study …

… model predictive control (MPC) of linear systems subject to … hard input constraints and joint
state chance constraints in the … for the joint state chance constraints when only the mean and …

… [25] DH van Hessem, “Stochastic inequality constrained closed-loop model predictive
control with application to chemical process operation,” Ph.D. dissertation, Delft Univ. …

… , chance constraints are imposed to restrict the collision probability, and subsequently, a
nonlinear model predictive control problem is constructed with these chance constraints. It is …

… Abstract—Distributed model predictive control methods for uncertain systems often suffer …
predictive control (DSMPC) scheme for dynamically coupled linear discrete-time systems …

… controllers, only consider past and current system behavior (ie they are “reactive” to a deviation).
Predictive controllers use a system … control methods that directly considers constraints. …

… predictive control based on the sum of squares technique is developed for hypersonic vehicles
with state-dependent input constraints. … Then the actual input constraints are converted to …

… 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 …