Learningbased model predictive control on a quadrotor. Stochastic optimal control is rooted in stochastic pro. Introduction to stochastic processes lecture notes with 33 illustrations. The proposed stochastic model predictive control smpc problem in this stage is characterized through a chanceconstrained optimization formulation that can effectively capture the system and the. Model predictive control mpc is a control strategy that has been used successfully in numerous and diverse application areas. Control of a multiinput multioutput nonlinear plant. Alamo abstractmany robust model predictive control mpc schemes are based on minmax optimization, that is, the future control input trajectory is chosen as the one which minimizes the performance due to the worst disturbance.
Introduction to stochastic processes lecture notes. Stochastic model predictive control pantelis sopasakis imt institute for advanced studies lucca february 10, 2016. The performance objective of a model predictive control algorithm determines. The starting point is classical predictive control and the appropriate. Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. Stochastic model predictive control with joint chance constraints mit. A stochastic model predictive control strategy for energy. Stochastic optimization in energy iso new england august 18, 2014 warren b. Model predictive control for stochastic systems by randomized algorithms. On stochastic model predictive control with bounded. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic modelpredictive control problem is formulated.
Model predictive control for stochastic systems by randomized. Ho we ver, results on predicti ve control of stochastic distrib uted parameter systems, to the best of our kno wledge, are not available. Audio slides for the paper stochastic model predictive control how does it work. Stochastic model predictive control university of oxford. Stochastic unit commitment hand ling generator or transmission failures, weather variations, pri ce spikes, and the growing impact. Such a model is useful in an equally important but quite different way. Model predictive control for stochastic systems by randomized algorithms citation for published version apa. Introduction stochastic model predictive control smpc accounts for model uncertainties and disturbances based on their statistical description. Partial differential equations in modelling and control of. Scenariobased model predictive control of stochastic. Stochastic model predictive control smpc provides a probabilistic framework for mpc of systems with stochastic uncertainty. Model predictive control certaintyequivalent control constrained linearquadratic regulator in nite horizon model predictive control mpc with disturbance prediction 1. On stochastic model predictive control with bounded control inputs peter hokayem, debasish chatterjee, john lygeros abstractthis paper is concerned with the problem of model predictive control and rolling horizon control of discretetime systems subject to possibly unbounded random noise inputs, while satisfying hard bounds on the control. This chapter provides a tutorial exposition of several smpc approaches.
Stochastic models possess some inherent randomness. What is the difference between stochastic programming and. Model predictive control linear convex optimal control. Model predictive control of nonlinear stochastic pdes. Stochastic model predictive control with joint chance. Only boundary control methods were considered, since the arrival rate of the manufacturing system the in. Stochastic model predictive control based on gaussian.
Stochastic modelpredictive control for lane change. Such often mentioned attributes as realism, elegance, validity, and reproducibility are important in evaluating a model only insofar as they bear on that models ultimate. The aim of the present entry is to discuss how the basic ideas of mpc can be extended to problems involving random model uncertainty with known probability distribution. Create and simulate a model predictive controller for a mimo plant. The performance objective of a model predictive control algorithm determines the optimality, stability and convergence properties of the closed loop control law. Tutorial on model predictive control of hybrid systems. Stochastic model predictive control with active uncertainty learning. Predictive modeling is a broader field that could possibly touch stochastic programming techniques as well. Model predictive control for stochastic systems by. Model predictive control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. For the first time, a textbook that brings together classical predictive control with treatment of uptodate robust and stochastic techniques. In this section we consider how to generalize the quadratic cost typically employed in linear optimal control problems to. The cost function consists of an economic part and a regulatory part, and a new empc algorithm with piecewise constant control is designed.
I stochastic control problem reduces to deterministic control problem, called. The concept history and industrial application resource. Stochastic model predictive control how does it work. A complete solution manual more than 300 pages is available for course instructors. Follow 7 views last 30 days shokat m on 5 jun 2015. If it never happens, we will be waiting forever, and. Stochastic nonlinear model predictive control with e cient. Economic model predictive control of sampleddata linear.
The key steps in this methodology are a developing a hybrid automaton ha. Timeaverage constraints in stochastic model predictive control james fleming mark cannon acc, may 2017 james fleming, mark cannon timeaverage constraints in stochastic mpc acc, may 2017 1 24. Timeaverage constraints in stochastic model predictive. Convexication for model predictive control under uncertainty with reliable online computations the workshop also provides real life applications and reports on the actual transition from theory to practice. Stochastic optimization in energy ferc, washington, d. In stochastic programming you assign possibilities on the variables or the objectives. Stochastic model predictive control ali mesbah, ilya kolmanovsky and stefano di cairano i. Examples of diverse types of stochastic models are spread throughout this book.
Model predictive control onpolicy learning offpolicy learning. A key feature of smpc is the inclusion of chance constraints, which enables a systematic tradeoff between attainable control performance and probability of state constraint violations in a stochastic setting. The system designer assumes, in a bayesian probabilitydriven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables. Table 1 an overview of applications of stochastic model predictive control for linear smpc and nonlinear systems. Lbmpc combines aspects of learningbased control and model predictive control mpc. The two most promising control strategies, lyapunovs stability theory and nonlinear model predictive control nlmpc, have been inves. Model predictive control mpc, also known as recedinghorizon control, has demonstrated exceptional success for the highperformance control of technical systems in a variety of applications such as automotive applications, building climate control, microgrids, process systems control, and robotics and vehicle path planning lee, 2011, mayne, 2014, morari, lee, 1999. Model predictive controllers use plant, disturbance, and noise models for prediction and state estimation.
Stochastic model predictive control stanford university. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closedloop stability and performance. The same set of parameter values and initial conditions will lead to an ensemble of different. Stochastic programming applied to model predictive control d. Nonlinear model predictive control, continuousdiscrete extended kalman filter, maximum likelihood estimation, stochastic di. Stochastic receding horizon control with output feedback and bounded controls. Scenariobased model predictive control of stochastic constrained linear systems daniele bernardini yand alberto bemporad abstract in this paper we propose a stochastic model predictive control mpc formulation based on scenario generation for linear systems affected by. Model predictive control mpc unit 1 distributed control system pid unit 2 distributed control system pid fc pc tc lc fc pc tc lc unit 2 mpc structure. Publishers pdf, also known as version of record includes final page, issue and volume numbers. The proposed model has been evaluated by performing lane change simulations in matlabsimulink, while considering the effect of combination prediction. Stochastic model predictive control mitsubishi electric research. Splitting power is a tricky problem for series plugin hybrid electric vehicles sphevs for the multiworking modes of powertrain and the hard prediction of future power request of the vehicle. These properties however can be satisfied only if the underlying model used for prediction of. In this work, we present a methodology for splitting power for a battery pack and an auxiliary power unit apu in sphevs.
1318 514 28 959 108 339 323 726 1271 106 1414 809 122 891 480 1437 155 162 405 417 24 514 1515 231 173 651 201 1325 76 358 1200 635 844 1246 384 317 1471 834 185 1079 962 971 846 1349 143 776 901 975 1203