The model predictive control mpc toolbox is a collection of functions commands developed for the analysis and design of model predictive control mpc systems. The model predictive control mpc has been applied in many practical process control areas by using receding optimization at every step to generate closedloop feedback control. Model predictive control for the process industries. Discrete event simulation software discrete event simulation engine provides detailed modeling and optimization for all process driven simulation environment. The proposed approach is based on the dual problem of a mpc optimization problem involving all systems. Acquisition control software with fast connections and data transfers to and from any system. 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. You can use whichever is most convenient for your application and convert from one format to another.
Optimal control theory is a branch of applied mathematics that deals with finding a control law for a dynamical system over a period of time such that an objective function is optimized. Convergence analysis and digital implementation of a discretetime neural network for model predictive control december 2014 ieee transactions on industrial electronics 6112. In this paper we give an overview of some results in connection with model predictive control mpc. Model predictive control college of engineering uc santa barbara. Online relative footstep optimization for legged robots dynamic walking using discretetime model predictive control. Model predictive control was conceived in the 1970s primarily by industry. The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variablespeed. Model predictive power control approach for threephase. Oct 01, 2011 general information mpc is a pure python module for the simulation of discrete time linear time invariant dynamic systems which can be controlled by a model predictive controller mpc or an infinite horizon linearquadratic controller lq. Discrete time model predictive control approach for inverted pendulum system with input constraints harshita joshi1, nimmy paulose2 1,2electrical engineering department, mnnit, allahabad,india. Model predictive control of discretetime hybrid systems with discrete inputs b. Tutorial on model predictive control of hybrid systems.
Model predictive control of discretetime hybrid systems. The process is repeated because objective targets may change or updated measurements may have adjusted parameter or state estimates. It recently has been successfully extended to several discrete event systems, e. Design of a model predictive controller to control uavs. Model predictive control, generally based on state space models, needs the complete state for feedback. Fast model predictive control using online optimization. Model predictive control is an advanced method of process control that is used to control a process while satisfying a set of constraints. However, the updated model and conditions remain constant over the prediction horizon. Selection of the most appropriate mpc approach depend on the specific problem. The acc system decides which mode to use based on real time radar measurements. Robust model predictive control for nonlinear discrete. In fact, as optimal control solutions are now often implemented digitally, contemporary control theory is now primarily concerned with discrete time systems and solutions.
Mpc using transfer function representations of the model. In the design of model predictive controller mpc, the traditional approach of expanding the projected control signal uses the forward operator to obtain the. Observerbased model predictive control bas rosety and henk nijmeijery model predictive control in combination with discrete time nonlinear observer theory is studied in this paper. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. Similarly, if the lead car is further away, the acc system switches from spacing control to speed control. The basic ideaof the method isto considerand optimizetherelevant variables, not. Pdf online relative footstep optimization for legged. The most wellstudied mpc approaches with guaranteed stability use a control lyapunov function as terminal cost. Discrete event simulation software simcad pro free trial. Some of the toolbox functions have been modified slightly to enhance the functionality, as described in appendix c. Model predictive control for discretetime linear systems. For example, the cstr model could include direct feedthrough from the unmeasured disturbance, c ai, to either c a or t but direct feedthrough from t c to either output would violate this restriction.
Model predictive control algorithms for applications with. Its popularity steadily increased throughout the 1980s. This paper presents a newmodel predictive control mpc scheme for linear constrained discrete time periodic systems. 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. The following is an introductory video from the dynamic optimization course. This paper proposes a model predictive control mpc strategy that takes the advantage of a cost function minimization technique to eliminate the circulating currents and carry out the voltage balancing task of an mmcbased backtoback hvdc system. Robust model predictive control for discretetime fractional. Model predictive optimal control of a timedelay distributedparameter system nhan nguyen. This controller should obtain the operational benefits of pull e. For example, if the lead car is too close, the acc system switches from speed control to spacing control. Convergence analysis and digital implementation of a. We deal with linear, nonlinear and hybrid systems in both small scale andcomplex large scale applications. An introduction to modelbased predictive control mpc.
This paper describes a model predictive control mpc algorithm for the solution of a statefeedback robust control problem for discretetime nonlinear systems. In this paper, a novel controller design which consists of discrete time model predictive control dmpc based on laguerre functions and space vector pulse width modulation svpwm is proposed to. Application of interiorpoint methods to model predictive control. One thing to note is that the adaptive mpc block requires a discrete plant model. 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. A discretetime mathematical model of the system is derived and a predictive model. The software converts the plant input and output variables to dimensionless form. Farahani, rupak majumdar, vinayak prabhu, sadegh esmaeil zadeh soudjani abstractwe present shrinking horizon model predictive control shmpc for discretetime linear systems with signal. The examples thus far have shown continuous time systems and control solutions. Model predictive power control approach for threephase singlestage gridtied pv moduleintegrated converter amir moghadasi, student member, ieee, arman sargolzaei, member, ieee, arash khalilnejad, student.
Model predictive control toolbox documentation mathworks. Model predictive control is a form of control scheme in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop. Firstly, an mpc law is derived by minimizing the cost function composed of predictive discrete time state and control variables. This syntax sets the model property of the controller. Therefore, mpc typically solves the optimization problem in smaller time windows than the whole. Necessary for preventing from having no solution at a given time no control input would be defined. Our research lab focuses on the theoretical and realtime implementation aspects of constrained predictive modelbased control.
This thesis aims at lowering the practical burden of applying fast mpc algorithms in the realworld. Model predictive control is a family of algorithms that enables to. Nonlinear model predictive control, continuousdiscrete extended kalman filter. It has numerous applications in both science and engineering. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the. The idea behind this approach can be explained using an example of driving a car. Article pdf available in ieee transactions on automatic control 601. When you do not specify a sample time, the plant model, model. Timedelay compensation techniques predict process output one time delay ahead. Free engineering optimization software by alphaopt. Nonlinear model predictive control theory and algorithms. In this paper, the model predictive control problem is investigated for a class of discrete.
After chapter 1, the model predictive control toolbox is needed or comparable software. To develop better, fast, accurate and robust process control, modelbased modern control algorithms and efficient adaptive and learning techniques are required. To adapt to changing operating conditions, adaptive mpc supports updating the prediction model and its associated nominal conditions at each control interval. The first control action is taken and then the entire process is repeated at the next time instance. See this paper for the precise problem formulation and meanings of the algorithm parameters. General information mpc is a pure python module for the simulation of discretetime linear timeinvariant dynamic systems which can be controlled by a model predictive controller mpc or an infinite horizon linearquadratic controller lq. Robust model predictive control for discretetime fractionalorder systems. The model predictive control toolbox software prohibits direct instantaneous feedthrough from a manipulated variable to an output. By typing it in the command window, we can see the design parameters such as the prediction and control horizons, constraints and weights. Ingredients marcello farina introduction to mpc 19.
Model predictive control of hybrid systems ut yt hybrid system reference rt input output measurements controller model. Linear model predictive control mpc has become an attractive feedback strategy, especially for linear processes. Model predictive control mpc is an advanced method of process control that is used to control. Model predictive control of discretetime hybrid systems with. Process control in the chemical industries 119 from the process. The randomly occurring nonlinearity, which describes the phenomena of a class of nonlinear disturbances occurring in a random way, is modeled according to a bernoulli distributed white. Pdf whither discrete time model predictive control. Model predictive control, which is based on implementing solutions to optimal. For confronting such problems, several robust model predictive control rmpc techniques have been developed in recent. Nasa ames research center, moffett field, ca 94035 this paper presents an optimal control method for a class of distributedparameter systems governed by.
Temporal logic model predictive control for discretetime systems. Stabilization of uncertain nonlinear discretetime switched. Mpcbased controllers are mostly designed based on a discretetime state space representation of linear timeinvariant lti systems. The algorithms proposed in this paper were implemented as a software package that is available for download. It is often referred to as model predictive control mpc or dynamic optimization. Model predictive optimal control of a timedelay distributed. We use a discrete time riccat i recursion to solve the linear equation s efficiently at each iteration of the interiorpoint method, and show that this recursio n is numericall y stable. This chapter presents a scheme of model predictive discretetime sliding mode control mpdtsmc with proportionalintegral pi sliding function and state observer for the motion tracking control of a nanopositioning system driven by piezoelectric actuators.
Most of these use the modified mpc toolbox functions listed above. In each period of the system, a new periodic state feedback control law is computed via a convex optimization problem that minimizes an upper bound on an infinite horizon cost function subject to state and input constraints. We demonstrat e the effectiveness of the approach by applying it to three process control problems. 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 capabilities of continuous time sliding mode control smc 22, 23,24, and discrete time sliding model control dsmc 25, 26, as robust and lowcost nonlinear control design techniques, have been shown in the literature for the throttle control problem. Model predictive control has a number of manipulated variable mv and controlled variable cv tuning constants. Abstract model predictive control mpc includes a recedinghorizon control techniques based on the process model for predictions of the plant output. The main contributions of this work are to use the discrete time system model based on mpc to achieve the trajectory tracking control and to avoid obstacle collisions. Model predictive control system design and implementation. Model predictive control of wind energy conversion systems. Predictive control of a modular multilevel converter for a. Most existing mpc implementations use a discretetime form.
Explicit model predictive control for linear timevariant systems with. Model predictive control mpc is an optimalcontrol based method to select control inputs by. Communications in computer and information science, vol 487. Builds discretetime model, accounting for computational delay. Nmpc schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different nmpc. Model predictive controllers rely on dynamic models of. Thus, by repeatedly solve an openloop optimization problem with every initial conditions updated at each time step, the model predictive control strategy. Hence, we concentrate our attention from now onwards on results related to discretetime systems. This article presents a tracking control approach with obstacle avoidance for a mobile robot. Robust modelbased discrete sliding mode control of an. The objective of this thesis is the development of novel model predictive control mpc schemes for nonlinear continuous time systems with and without time delays in the states which guarantee asymptotic stability of the closedloop.
To this aim, it contributes two software packages, which are released as opensource code in order to stimulate their widespread use. Model predictive control or mpc is an advanced method of process control that has been in use in the process industries such as chemical plants and oil refineries since the 1980s and has proved itself. This book offers readers a thorough and rigorous introduction to nonlinear model predictive control nmpc for discrete time and sampleddata systems. Here we are concerned with predictive control techniques that predict the process output over a longer time horizon. Despite the presence of discrete actuators in many industrial processes, model predictive control mpc theory typically considers only continuous actuators, which requires discrete decisions to be removed from the mpc layer. Here is a collection of matlab software related to examples and problems which appear in the book. The main advantage of mpc is the fact that it allows the current timeslot to be optimized, while. Instead, there exist approaches where mpc is used with continuous and discrete time models. N2 the modular multilevel converter mmc is one of the most potential converter topologies for highpowervoltage systems, specifically for highvoltage direct current hvdc. In recent years it has also been used in power system balancing models and in power electronics. The control law is obtained through the solution of a.
This paper focuses on this purpose by constructing a discrete eventdriven model predictive control empc for realtime wip rwip optimization. Simulation of nonlinear system can also be performed. Model predictive control of discrete time hybrid systems with discrete inputs b. Model predictive control mpc includes a recedinghorizon control techniques based on the process model for predictions of the plant output. A timevarying extremumseeking control approach for.
Discretetime predictive trajectory tracking control for. Our contributions include the discovery of fundamental theoretical results, the development of novel control algorithms and their experimental validation carried. Discrete time model predictive control design using laguerre. Model predictive control discrete impulse response models consider a single input, single output process. Model predictive control toolbox software supports the same lti model formats as does control system toolbox software. In addition, the laguerre functions are able to efficiently. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. Discrete eventdriven model predictive control for realtime.
The mpc controller converts the input disturbance model to a discretetime. Distributed model predictive control of linear discrete. Model predictive control for discreteevent and hybrid systems. Shrinking horizon model predictive control with signal. 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 proposed approach is applied in the design of nonlinear model predictive control algorithms where the extremumseeking controller is used to perform the realtime optimization of the mpc. Model predictive control for the process industries 395 the laguerre functions are well suited to modeling the types of transient signals found in process control because they have similar behavior to the processes being modeled and are also an orthogonal function set. The discretetime model will selection from predictive control of power converters and electrical drives book.
Model predictive control based on discretetime models. This paper proposes a distributed model predictive control dmpc approach for a family of discretetime linear systems with local uncoupled and global coupled constraints. Discretetime optimal control over a finite horizon as an optimization. Some description of this toolbox is given in appendix c of the book, but there is also a complete tutorial available.
An introduction to modelbased predictive control mpc by stanislaw h. See this paper for the precise problem formulation and meanings of the. A method to solve dynamic control problems is by numerically integrating the dynamic model at discrete time intervals, much like measuring a physical system at particular time points. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. A widely recognized shortcoming of model predictive control mpc is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. So, we need to convert the continuous time state space model used by mpc1 to discrete time. The first is a discretetime model predictive methodbased trajectory tracking control law that is derived using an optimal quadratic algorithm. Shrinking horizon model predictive control with signal temporal logic constraints under stochastic disturbances samira s.
A well known technique for implementing fast mpc is to compute the entire control law offline, in which case the online. For example, the commonly used simulinkmatlab environment has been used to develop a discretetime simulation model of the discrete event manu. Abstractmodel predictive control mpc includes a recedinghorizon control techniques based on the process model for predictions of the plant output. T1 predictive control of a modular multilevel converter for a backtoback hvdc system. By and large, the main disadvantage of the mpc is that it cannot be able of explicitly dealing with plant model uncertainties. Hence, we concentrate our attention from now onwards on results related to discrete time systems. Usually mpc uses linear or nonlinear discretetime models. Lmibased model predictive control for linear discretetime. Discrete eventdriven model predictive control for real. Adaptive cruise control system using model predictive control.
967 1194 1529 1522 322 786 19 1041 157 1275 181 1048 1495 1291 1663 65 516 1142 923 1584 578 1317 454 1003 202 73 746 1066 194 1204 588 581 1305