Robust constrained model predictive control based on parameter-dependent Lyapunov functions

The problem of robust constrained model predictive control (MPC) of systems with polytopic uncertainties is considered in this paper. New sufficient conditions for the existence of parameter-dependent Lyapunov functions are proposed in terms of linear matrix inequalities (LMIs), which will reduce the conservativeness resulting from using a single Lyapunov function. At each sampling instant, the corresponding parameter-dependent Lyapunov function is an upper bound for a worst-case objective function, which can be minimized using the LMI convex optimization approach. Based on the solution of optimization at each sampling instant, the corresponding state feedback controller is designed, which can guarantee that the resulting closed-loop system is robustly asymptotically stable. In addition, the feedback controller will meet the specifications for systems with input or output constraints, for all admissible time-varying parameter uncertainties. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques

Multiobjective economic MPC of constrained non‐linear systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166264/1/cth2bf00058.pd

Predictive feedback control using a multiple model approach

A new method of designing predictive controllers for SISO systems is presented. The controller selects the model used in the design of the control law from a given set of models according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a feedback control law that ensures robust stability of the closed–loop system and gives better performance for the current operating point. The overall multiple model predictive control scheme quickly identifies the closest linear model to the dynamics of the current operating point, and carries out an automatic reconfiguration of the control system to achieve a better performance. The results are illustrated with simulations of a continuous stirred tank reactor

Receding horizon climate control in metal mine extraction rooms

International audienceThis papers proposes a novel climate control strategy for mine extraction rooms based on the receding horizon optimal control scheme. Being a model-based procedure, the development of a pertinent prediction model is one of the keystones. According to recent technological advances, we consider that distributed measurements are available and provided by a wireless network. An enhanced modeling approach, based on stratification and sigmoid description of concentrations in the extraction rooms, is then proposed and allows for an optimal use of information provided by the wireless sensor network (WSN). The complexity of the resulting model, due to the nonlinearities, different time scales and time-delays, is handled by using an on-line shape prediction, included in the design of an optimal sequence of control actions over a finite horizon. Physical and communication constraints are successfully handled at the design stage and the resulting closed-loop system is robust with respect to variations in the pollutant dynamics

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

Dielectric elastomers are a promising technology for wave energy harvesting. An optimal system operation can allow maximizing the extracted energy and, simultaneously, reducing wear that would lead to a reduction in the wave harvester lifetime. We pursue a model-based optimization approach to identify optimal controls for wave energy harvesters based on dielectric elastomers. First, a direct method is used for time-discretization of the dielectric elastomer wave energy harvester in the optimal control problem. The two conflicting objectives are considered in a multiobjective optimization framework. Considering a periodic, sinusoidal wave excitation, the optimal solution shows turnpike properties for the optimal periodic mode of operation. However, since real wave motion is neither monochromatic nor predictable on longer time horizons, further extensions are pursued. First, we introduce a stochastic wave excitation. Second, an iterative model-predictive control scheme is designed. Due to multiple objectives, the control scheme has to include an automated adaption of the corresponding priorities. Here, we propose and evaluate a heuristic rule-based adaption in order to maintain the damage below target levels. The approach presented here might be used in the future to guarantee for autonomous operation of farms of wave energy harvesters