A robustness constraint approach ieee transactions on automatic control, 59 6 2014, pp. We are concerned with the design of model predictive control mpc schemes such that. Based on the exact penalization theorem, this paper presents a discrete time statespace model predictive control strategy with a. Unconstrained nonlinear model predictive control and. Model predictive controllers rely on dynamic models of. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. The ctmpc approach uses taylor series expansion to derive a closedform solution to the problem of model predictive control even though the system behaviour is described by a nonlinear model.
However, due to its mathematical complexity and heavy computation effort, it is mainly suitable in processes with slow dynamics. Speci cally we consider the so called sampleddata nonlinear model predictive control approach. Control objective function objective function weighting matrices for states, input, output, auxiliary real variables weighting matrix for. Our control approach, which aims at reducing the number of transmitting control samples to the plant, is derived by parallelly solving optimal control problems with. Model predictive control mpc refers to a class of algorithms that compute the trajectory of manipulated variable adjustment to optimize the future behaviour of a. Continuoustime model predictive control for economic. In so doing, the optimization is performed with respect to sequences, as in discretetime nonlinear mpc, but the continuoustime evolution of the system is. Robust economic model predictive control of continuous. Problems framework and motivation in this paper we deal with discretetime, optimal control problems with nonlinear dynamics. Shirobust distributed model predictive control of constrained continuoustime nonlinear systems.
Continuoustime model predictive control rmit research. A widearea continuous time model predictive control. Model predictive control of wind energy conversion systems addresses the predicative control strategy that has emerged as a promising digital control tool within the field of power electronics, variablespeed motor drives, and energy conversion systems the authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variable. Model predictive control system design and implementation. Robust model predictive control of continuoustime sampleddata nonlinear systems with integral sliding mode m. Yet, most continuoustime model predictive control mpc frameworks had to assume continuity of the resulting feedback law, being unable to address an important. Realtime imlementation of model predictive control 2. The response time of the control signal in the continuous time mpc case is a bit slower compared to the discrete time case 1. Model predictive control system design and implementation using matlab proposes methods for design and implementation of mpc systems using basis functions that confer the following advantages. In addition, the formulation for multivariable systems with timedelays is straightforward. Robust model predictive control of continuoustime sampled. Model predictive control of wind energy conversion systems addresses the predicative control strategy that has emerged as a promising digital control tool within the field of power electronics, variablespeed motor drives, and energy conversion systems. Model predictive control of continuoustime nonlinear.
Continuoustime model predictive control of a permanent. The results are developed within the sampledata model predictive control mpc framework considering constrained nonlinear continuoustime timevarying dynamical systems. A barrier function based continuous time algorithm for linear model predictive control christian feller and christian ebenbauer abstract in this paper, we present a novel linear model predictive control mpc scheme that relies on a continuous time, barrier function based algorithm which asymptotical ly. In this paper, a new decentralized model predictive control has been proposed for continuoustime nonlinear largescale systems made of multiple. The approach uses orthonormal functions to describe the trajectory of the control variable, and a multivariable state space model is. Boilerturbine control system design using continuoustime. It will take the reader through the principles of continuoustime. We consider recursive feasibility of nonlinear continuoustime model predictive control mpc without stabilizing terminal costs and constraints.
However, continuous time mpc requires less optimization steps to. An introduction to modelbased predictive control mpc. Dec 17, 2016 model predictive control of wind energy conversion systems addresses the predicative control strategy that has emerged as a promising digital control tool within the field of power electronics, variablespeed motor drives, and energy conversion systems. Model predictive control of continuous time uncertain nonlinear largescale systems samane fazeli1, naiem abdollahi2, hashem imani marrani3, hamid malekizade4 and hasan hosseinzadeh5 abstract. Feedback control is crucial for achieving the stringent regulatory requirements on cqas of pharmaceutical products in the. An introduction to modelbased predictive control mpc by stanislaw h. Nonlinear systems with piecewise constant control lalo magni and riccardo scattolini abstracta new model predictive control mpc algorithm for nonlinear systems is presented.
The approach uses orthonormal functions to describe the trajectory of the control variable, and a multivariable state space model is used in the design. This thesis deals with linear model predictive control, mpc, with the goal of making a controller for an arti cial pancreas. Most approaches, however, were derived on the basis of discrete time models, and their corresponding continuous counter. In this paper, we investigate the use of relaxed logarithmic barrier functions in the context of linear model predictive control. A comparison of fuzzy and cpwl approximations in the. Shirobust distributed model predictive control of constrained continuous time nonlinear systems. In this paper, an overview of the most commonly used six methods of mpc with history.
Model predictive control for nonlinear continuoustime systems. Continuoustime systems with and without timedelays. 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. The design and the experimental validation of a continuoustime model predictive control ctmpc for a permanent magnet synchronous motor pmsm drive with disturbance decoupling is discussed.
Model predictive control for continuoustime piecewise. Pdf international journal of control continuous time. Review of mpc methods there are various control design methods based on model predictive control concepts. Research article model predictive control for continuous. A new robust adaptive decentralized tube model predictive. Model predictive control of wind energy conversion systems. Pdf continuous time model predictive control for a magnetic. Discontinuous feedbacks, discontinuous optimal controls. Tutorial on model predictive control of hybrid systems. Continuoustime linear mpc algorithms based on relaxed.
Model predictive control has had an exceptional history with early intimations in the academic literature coupled with an explosive growth due to its independent adoption by the process industries where it proved to be highly successful in comparison with alternative methods of multivariable control. This type of problems can be obtained, for example, by appropriately discretizing continuoustime problems. Apply the first value of the computed control sequence at the next time step, get the system state and recompute. This paper presents design and implementation of a continuous time model predictive control algorithm cmpc to an active magnetic bearing system amb. The ctmpc approach uses taylor series expansion to derive a closedform solution to the problem of model predictive control even though the system. The continuous time laguerre functions and kautz functions discussed in chapter 5 are utilized in the design of continuous time model predictive control. Research article model predictive control for continuoustime singular jump systems with incomplete transition rates xinxingu,jiweiwen,andlipeng key laboratory for advanced process control of light industry of the ministry of education, school of internet of. Yet, most continuous time model predictive control mpc frameworks had to assume continuity of the resulting feedback law, being unable to address an important. This paper presents a distributed model predictive control dmpc scheme for continuous. Nonlinear model predictive control technique for unmanned. Model predictive control notation meaning j q x, q u, q y, q z q xt.
Selftriggered model predictive control for continuous. Model predictive control has received wide attention from researchers in both industry and universities over the last two decades. Model predictive optimal control of a timedelay distributedparameter system nhan nguyen. Introduction the extraordinary industrial success of model predictive control mpc techniques based on linear plant models, see e. A complete solution manual more than 300 pages is available for course instructors. In this article, a discretetime mpc is derived from a unique inputoutputlinearized approximation of the full model based on the average population values of the parameters. Robust economic model predictive control of continuoustime. This chapter discusses continuoustime model predictive control cmpc without constraints. Magni abstracta hierarchical nonlinear model predictive control nmpc scheme with guaranteed inputtostatepracticalstability isps is proposed. The application is to control motor torque and specific mechanic energy. Stabilizing model predictive control of nonlinear continuous.
Year 2007 abstract model predictive control mpc refers to a class of algorithms that optimize the future behavior of the plant subject to operational constraints 46. Continuoustime model predictive control of underactuated. Model predictive control for continuoustime piecewise af. This paper presents the design and implementation of a continuous time model predictive controller using laguerre functions.
The results are developed within the sampledata model predictive control mpc framework considering constrained nonlinear continuous time time varying dynamical systems. We derive conditions on the horizon length such that the mpc algorithm is recursively feasible assuming local cost controllability, i. Model predictive control of continuoustime nonlinear systems with piecewise constant control lalo magni and riccardo scattolini abstracta new model predictive control mpc algorithm for nonlinear systems is presented. Piyabongkarn, development of a realtime digital control system with a hardwareintheloop magnetic levitation device for reinforcement of controls education.
Specically we consider the so called sampleddata nonlinear. A barrier function based continuoustime algorithm for linear. Continuous time model predictive control design using. There are several advantages using the continuoustime approaches. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Most approaches, however, were derived on the basis of discrete time models, and their corresponding continuous counter part is still in a relatively immature state of development because of obstacles in obtaining predictions and imposing constraints on the control. Continuoustime model predictive control authors truong, q. Model predictive control with a relaxed cost function for. This class includes systems with interest in practice, such as nonholonomic systems, frequently appearing in robotics and other areas. This thesis investigates design and implementation of continuous time model predictive control using laguerre polynomials and extends the design approaches proposed in 43 to include intermittent predictive control, as well as to include the case of the nonlinear predictive control. Firstly, a continuoustime mathematical model is suited better to the extrusion plant that has a fast sampling rate and a wide range of time constants. Based on a controllability assumption and a corresponding in nitedimensional optimization pro blem, performance estimates and. Nasa ames research center, moffett field, ca 94035 this paper presents an optimal control method for a class of distributedparameter systems governed by.
Continuoustime model predictive control of food extruder. Distributed model predictive control for continuous. The plant under control, the state and control constraints, and the performance index to be minimized are described in continuous time, while the manipulated. A widearea continuous time model predictive control scheme. Model predictive control mpc refers to a class of algorithms that optimize the future behavior of the plant subject to operational constraints 46. Robust model predictive control with integral sliding mode in continuous time sampleddata nonlinear systems. Hybrid control problem binary inputs continuous inputs binary states continuous states online decision maker desired behavior constraints hybrid process 42166 model predictive control of hybrid systems ut yt hybrid system reference rt input output measurements controller model. Discontinuous feedbacks, discontinuous optimal controls, and. Model predictive optimal control of a timedelay distributedparameter system.
Pdf sampleddata nonlinear model predictive control for. This paper presents a continuoustime version of recent res ults on unconstrained nonlinear model predictive control mpc schemes. A primary advantage to the approach is the explicit handling of constraints. The mpc is constructed using control and optimization tools. Sampleddata model predictive control for constrained. Continuous time model predictive control for a magnetic bearing. Continuous time model predictive control design using orthonormal. A diabetic is simulated by a mathematical model, and based on this model the mpc will compute the optimal insulin input, taking constraints, disturbances and noise into account. The idea behind this approach can be explained using an example of driving a car.
Continuous time model predictive control this section provides a brief discussion of the continuous time model predictive control 7 used in this paper. Nonlinear model predictive control technique for unmanned air. We consider recursive feasibility of nonlinear continuous time model predictive control mpc without stabilizing terminal costs and constraints. The continuoustime laguerre functions and kautz functions discussed in chapter 5 are utilized in the design of continuoustime model predictive control. Model predictive optimal control of a timedelay distributed. When a set of laguerre functions is used in the design, the desired closedloop response can be achieved by tuning the time scaling factor p and the number of terms n.
This means the performance is slightly worse, because it takes more time before the system is stabilized. Model predictive control, cost controllability, and homogeneity. The purpose of this paper is to provide an introduction and overview to the eld of model predictive control for continuous time systems. Mpc model predictive control also known as dmc dynamical matrix control. This thesis addresses the design of optimizationbased control laws for the case where convergence to a desired setpoint, minimization of an arbitrary performance index, or a combination of the two, is the desired objective. A robustness constraint approach ieee transactions on. Model predictive control of an integrated continuous. 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. Continuous time model predictive control authors truong, q.
Pdf model predictive control of continuoustime nonlinear. Dubay 2007 provided real time comparison of a number of predictive controllers 6. The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variablespeed. A continuoustime nonlinear model predictive controller nmpc was designed for a boilerturbine unit. A stopping criterion in the admm algorithm limits the iterations and therefore the required communication effort during the dmpc solution at the expense of a suboptimal. Continuous time model predictive control for a magnetic. Control objective function objective function weighting matrices for states, input, output, auxiliary real variables weighting matrix for final state xt norm 2 prediction horizon mpc or final time. Model predictive control laws can be formulated for both discrete and continuoustime systems. Realtime implementation of model predictive control mpc. Model predictive control is the class of advanced control techniques most widely applied in the process industries. This paper considers the model predictive control mpc of critical quality attributes cqas of products in an endtoend continuous pharmaceutical manufacturing pilot plant, which was designed and constructed at the novartismit center for continuous manufacturing. A continuoustime predictive control system is designed based on the continuoustime model of the plant. Model predictive control of continuoustime nonlinear systems.
Mpc model predictive control also known as dmc dynamical matrix control gpc generalized predictive control rhc receding horizon control control algorithms based on numerically solving an optimization problem at each step constrained optimization typically qp or lp receding horizon control. In recent years it has also been used in power system balancing models and in power electronics. Pdf international journal of control continuous time model. The controller was designed by optimizing a recedinghorizon performance index, with the nonlinear system approximated by its. The basic philosophy of model predictive control is to calculate the future behavior of the plant inputs by optimizing the plant output within a fixed time window. The design and the experimental validation of a continuous time model predictive control ctmpc for a permanent magnet synchronous motor pmsm drive with disturbance decoupling is discussed. Design of a model predictive controller to control uavs. The model predictive control technique is widely used for optimizing the performance of constrained multiinput multioutput processes.