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Soft constrained model predictive control (MPC) is frequently applied in practice in order to ensure feasibility of the optimization during online operation. Standard techniques offer global feasibility by relaxing state or output constraints, but cannot e ...
Institute of Electrical and Electronics Engineers2014
In this paper, stability analysis of traffic control for two-region urban cities is treated. It is known in control theory that optimality does not imply stability. If the optimal control is applied in a heavily congested system with high demand, traffic c ...
In Model Predictive Control, the enforcement of hard state constraints can be overly conservative or even infeasible, especially in the presence of disturbances. This work presents a soft constrained MPC approach that provides closed- loop stability even f ...
We consider the control of a large-scale system composed of state-coupled linear subsystems that can be added or removed offline. In this paper we present Plug-and-Play (PnP) design methods based on Model Predictive Control (MPC) meaning that (i) the desig ...
A short technical subsection for Gaussian Process learning and Uncertainty propagation is presented as required in applications like Model Predictive Control, machine learning and optimization. ...
This paper describes synthesis of controllers involving Quadratic Programming (QP) optimization problems for control of nonlinear systems. The QP structure allows an implementation of the controller as a piecewise affine function, pre-computed offline, whi ...
A probabilistic interpretation of model predictive control is presented, enabling extensions to multiple coordinate systems. The resulting controller follows a minimal intervention principle, by learning and retrieving movements through the coordination of ...
A novel decomposition scheme to solve parametric nonconvex programs as they arise in Nonlinear Model Predictive Control (NMPC) is presented. It consists of a fixed number of alternating proximal gradient steps and a dual update per time step. Hence, the pr ...
In this chapter an algorithm for nonlinear explicit model predictive control is presented. A low complexity receding horizon control law is obtained by approximating the optimal control law using multiscale basis function approximation. Simultaneously, fea ...
This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ve ...
