In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can redu...

In this paper we investigate the stability of hybrid systems in closed-loop with Model Predictive Controllers (MPC) and we derive a priori sufficient conditions for Lyapunov asymptotic stability and exponential stability. A general theory is presented which proves that Lyapunov stability is achieved for both terminal cost and constraint set and terminal equality constraint hybrid MPC, even thou...

In this paper a robust MPC scheme using a time-varying terminal constraint set for input-constrained systems with a polytopic uncertainty description is proposed. The new scheme is a refinement to the algorithm published in “Efficient robust constrained model predictive control with a time-varying terminal constraint set” [1]. The original result contains an error in the stability proof which i...

The main objective in this work is to compare different convex relaxations for Model Predictive Control (MPC) problems with mixed real valued and binary valued control signals. In the problem description considered, the objective function is quadratic, the dynamics are linear, and the inequality constraints on states and control signals are all linear. The relaxations are related theoretically ...

This paper investigates the combination of model predictive control (MPC) concepts and posterior sampling techniques proposes a simple constraint tightening technique to introduce cautiousness during explorative learning episodes. The provided theoretical analysis in terms cumulative regret focuses on previously stated sufficient conditions resulting ‘Cautious Bayesian MPC’ algorithm shows Lips...

We present a technique for approximate robust dynamic programming that is suitable for linearly constrained polytopic systems with piecewise affine cost functions. The approximation method uses polyhedral representations of the cost-to-go function and feasible set, and can considerably reduce the computational burden compared to recently proposed methods for exact robust dynamic programming [Be...

This paper proposes a controller design approach that integrates RTO and MPC for the control of constrained uncertain nonlinear systems. Assuming that the economic function is a known function of constrained system’s states, parameterized by unknown parameters and time-varying, the controller design objective is to simultaneously identify and regulate the system to the optimal operating point. ...

This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate under combined parametric and additive uncertainties. The proposed SMPCap enables the quantification, manipulation, of both mean covariance system states inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared con...

This thesis extends Model Predictive Control (MPC) for constrained linear systems subject to uncertainty, including persistent disturbances, estimation error and the effects of delay. Previous work has shown that feasibility and constraint satisfaction can be guaranteed by tightening the constraints in a suitable, monotonic sequence. This thesis extends that work in several ways, including more...

The technological advances reached in the last years released portable devices with high computational capabilities and able to overcome relevant hardware limitations of past artificial pancreas (AP) applications. In view this, choice an unconstrained saturated model predictive control (S-MPC) can be reconsidered. A constrained MPC (C-MPC) is formulated here as a finite-horizon optimal problem ...