/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

System Identification I

Introduction to stochastic programming

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations

This paper discusses a novel probabilistic approach for the design of robust model predictive control (MPC) laws for discrete-time linear systems affected by parametric uncertainty and additive disturbances. The proposed technique is based on the iterated solution, at each step, of a finite-horizon optimal control problem (FHOCP) that takes into account a suitable number of randomly extracted scenarios of uncertainty and disturbances, followed by a specific command selection rule implemented in a receding horizon fashion. The scenario FHOCP is always convex, also when the uncertain parameters and disturbance belong to nonconvex sets, and irrespective of how the model uncertainty influences the system's matrices. Moreover, the computational complexity of the proposed approach does not depend on the uncertainty/disturbance dimensions, and scales quadratically with the control horizon. The main result in this work is related to the analysis of the closed loop system under receding-horizon implementation of the scenario FHOCP, and essentially states that the devised control law guarantees constraint satisfaction at each step with some a priori assigned probability p, while the system's state reaches the target set either asymptotically, or in finite time with probability at least p. The proposed method may be a valid alternative when other existing techniques, either deterministic or stochastic, are not directly usable due to excessive conservatism or to numerical intractability caused by lack of convexity of the robust or chance-constrained optimization problem.

https://staff.polito.it/giuseppe.calafiore/Home_Page/Publications_files/Robust_MPC_TAC.pdf

Robust Model Predictive Control via Scenario Optimization

This paper presents simulation and experimental results regarding a new class of wind energy generators, denoted as KiteGen, which employ power kites to capture high altitude wind power. A realistic kite model, which includes the kite aerodynamic characteristics and the effects of line weight and drag forces, is used to describe the system dynamics. Nonlinear model predictive control techniques, together with an efficient implementation based on set membership function approximation theory, are employed to maximize the energy obtained by KiteGen, while satisfying input and state constraints. Two different kinds of KiteGen are investigated through numerical simulations, the yo-yo configuration and the carousel configuration, respectively. For each configuration, a generator with the same kite and nominal wind characteristics is considered. A novel control strategy for the carousel configuration, with respect to previous works, is also introduced. The simulation results show that the power generation potentials of the yo-yo and carousel configurations are very similar. Thus, the choice between these two configurations for further development of a medium-to-large scale generator will be made on the basis of technical implementation problems and of other indexes like construction costs and generated power density with respect to land occupation. Experimental data, collected using the small-scale KiteGen prototype built at Politecnico di Torino, are compared to simulation results. The good matching between simulation and real measured data increases the confidence with the presented simulation results, which show that energy generation with controlled power kites can represent a quantum leap in wind power technology, promising to obtain renewable energy from a source largely available almost everywhere, with production costs lower than those of fossil sources.

High Altitude Wind Energy Generation Using Controlled Power Kites

Adaptive receding horizon control for constrained MIMO systems

The problem of vehicle yaw control is addressed in this paper using an active differential and yaw rate feedback. A reference generator, designed to improve vehicle handling, provides the desired yaw rate value to be achieved by the closed loop controller. The latter is designed using the second-order sliding mode (SOSM) methodology to guarantee robust stability in front of disturbances and model uncertainties, which are typical of the automotive context. A feedforward control contribution is also employed to enhance the transient system response. The control derivative is constructed as a discontinuous signal, attaining an SOSM on a suitably selected sliding manifold. Thus, the actual control input results in being continuous, as it is needed in the considered context. Simulations performed using a realistic nonlinear model of the considered vehicle show the effectiveness of the proposed approach.

Lorenzo Fagiano

Papers

The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations

Robust Model Predictive Control via Scenario Optimization

High Altitude Wind Energy Generation Using Controlled Power Kites

Adaptive receding horizon control for constrained MIMO systems

Vehicle Yaw Control via Second-Order Sliding-Mode Technique