In this note the Infinite Horizon (IH) optimality property of Nonlinear Model Predictive Control (MPC) is analysed. In particular it is shown with a contra example that the conjecture that the IH cost of the closedloop system controlled with a stabilizing MPC controller is a monotonic decreasing function of the optimization horizon is fallacius.

Abstract. The principle of the energy saving algorithm in buildings is based on lowering the night-time temperature and increasing (preheating) it in the morning. One important problem of this strategy consists in keeping peek demand of energy below the maximum power of the central energy source. A model-reference nonlinear predictive control algorithm is studied and the effects of peak power d...

This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear sys...

Tracking control of constrained nonlinear systems is a challenging problem which has recently attracted considerable attention. In particular, a successful approach is based on the so called Reference Governor (RG) [1]-[4]. The RG is essentially a nonlinear device which manipulates on-line a command input to the suitably pre-compensated closed-loop system so as to impose constraint satisfaction...

Predictive control of nonlinear systems is addressed by embedding the dynamics into an LPV system and by computing robust invariant sets. This mitigates the on-line computational burden by transferring most of the computations off-line. Benefits and conservatism of this approach are discussed in relation with the control of a critical mechanical system. keywords: Predictive control, constraints...

As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied ...

Nonlinear predictive split vector quantization (NPSVQ) and classiied NPSVQ (CNPSVQ) are introduced to exploit the correlation among the speech spectral parameters from two adjacent analysis frames. By interleaving intraframe SVQ with forward predictive SVQ, error propagation is limited to at most one adjacent frame. At an overall bit rate of about 21 bits/frame, NPSVQ can provide similar coding...

In this thesis a nonlinear model predictive control (NMPC) scheme is presented that is based on the concept of passivity. The proposed NMPC scheme takes advantage of the relationship between optimal control and passivity and the relationship between optimal control and model predictive control. This novel NMPC scheme, guaranteeing closedloop stability through the incorporation of a passivity-ba...

Model predictive control is a very interesting research area for its practical applications in the context of process control of industrial plants. The book, object of the review, covers a specific branch of Model Predictive Control, that is Nonlinear Model Predictive Control (NMPC). This review is organized in two sections: the Overview where the contents of the book are examined and the Concl...

Nonlinear Model Predictive Control (NMPC) is a powerful control framework, which strongly relies on a good model of the system dynamics. In the case, such a model is not available apriori, non-parametric regression using Bayesian regression or Gaussian Processes (GPs) have been shown promising in inferring the dynamics from collected data. An advantage of Bayesian methods and GPs over other reg...