… A robust, adaptive Model Predictive Control (MPC) approach for asymptotically stable,
constrained linear time-varying (LTV) systems with multiple inputs and outputs is proposed. The …

… predictive control to include step distance to the optimization objective function, while made
footsteps variable to generate both CoM and CoP trajectories under the CoP constraints. …

… that satisfy the chance constraints. By tuning the algorithm parameters appropriately, we
show that the resulting constrainttightening parameters satisfy the chance constraints up to an …

… in practice, constraints on … predictive control (MPC) through which the control actions that
respect actuator limits can be achieved by considering input constraints in the predictive control …

… This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) …
In practice, we usually consider some constraints put on process variables. The typical …

… constraint tightening for the hard constraints on the input uk , we propose a stochastic constraint
… to optimal feed-forward instead of feedback control. In other words, we take advantage …

… approximate explicit model predictive control (MPC) law using neural networks. The optimal
MPC control law for constrained linear quadratic regulator (LQR) systems is piecewise affine …

… model predictive control with … constraints may only be enforced by a controllable system.
This motivates the following assumption, which imposes only little restriction as robotic systems …

… robots.ox.ac.uk) Abstract: This paper presents stabilizing Model Predictive Controllers (MPC)
to be applied to blackbox systems subject to constraints in the inputs and the outputs. The …

… In Chapter 5, multivariable constrained predictive control algorithms are presented with
DMC as an illustration example, focusing on the description of online optimization and the …