Mpc has gained wide acceptance in the chemical and other process industries as the basis for advanced multivariable control schemes. Advanced control, introduction to model predictive control sgaasspassac an explicit process model is incorporated into the control computation. Model predictive control mpc stands for a family of methods that select control actions based on online optimization of an objective function. Suboptimal model predictive control feasibility implies stability abstract. Advanced control introduction to model predictive control. The common ground of these algorithms is that they. Model predictive control mpc is a control strategy that calculates control inputs by solving constrained optimal control problem over a. Is model predictive control a suboptimal technique in. Jones model predictive control part ii constrained finite time optimal controlspring semester 2014 27 2 constrained optimal control.
The initial idcom and mpc algorithms represented the first generation of mpc. Model predictive control constraint satisfaction problem boolean variable sewer network hybrid modelling approach these keywords were added by machine and not by the authors. Predictive control has become a popular topic in the recent years. Introduction model predictive control mpc strategies have found a wide range of applications from refineries to food processing and become a standard control algorithm for process industries. Optimal control of a nonlinear fedbatch fermentation. Model predictive control toolbox provides functions, an app, and simulink blocks for designing and simulating model predictive controllers mpcs. Tutorial overview of model predictive control ieee.
Limits on the storage space or the computation time restrict the applicability of model predictive controllers mpc in many real problems. Application to sewer networks carlos ocampomartinez ari ingimundarson alberto bemporad vicenc puig arc centre of excellence for complex dynamic systems and con trol. Hardware synthesis of explicit model predictive controllers. On robustness of suboptimal minmax model predictive control.
Constraints included in the design set point optimal optimal plant operation l n nd ints rag s trnt t nt e t l l no oe of trts t nt far m trts l ant opern rag s. This allows the controller, in principle, to deal directly. On the inherent robustness of suboptimal model predictive control james b. Unesco eolss sample chapters control systems, robotics and automation vol. Introduction the full bridge dcdc converter was initially proposed in previous studies 1 for both high power density and high power applications. Model predictive optimal control of a timedelay distributedparameter system nhan nguyen. Second, we present and analyze two suboptimal mpc schemes that are guaranteed to be stabilizing, provided an initial feasible solution is available and for which the computational requirements are more reasonable. This can be useful for computational reasons, because optimal control problems may be easier to solve if the control variable is penalized. In mpc, a system model and current and historical mea. See the paper by mattingley, wang and boyd for some detailed examples of mpc with cvxgen. It has been in use in the process industries in chemical. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. Introduction model predictive controller mpc is traced back to the 1970s. A block diagram of a model predictive control system is shown in fig.
Stability 2 of mpc 1 consider the system where f is continuously differentiable with respect to itsis continuously differentiable with respect to its arguments and f0,00. A process model is used to predict the current values of the output variables. Advanced process control, multivariable predictive control, crude distillation processes, model identification. Three decades have passed since milestone publications by several industrialists spawned a flurry of research and industrial commercial activities on model predictive control mpc. This paper presents a new model predictive control method for timeoptimal pointtopoint motion control of mechatronic systems.
We then focus on a moving average model with an integrator, and derive computationally simpler suboptimal algorithms. Suboptimal model predictive control of hybrid systems. The formulation of timeoptimal behavior within the model predictive control framework and the structure of the underlying optimization problem are discussed and modifications are presented in order to decrease the computational load of the numerical solution method. Model predictive control, backstepping control, stability analysis. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect. The idea behind this approach can be explained using an example of driving a car. Nonlinear model predictive control technique for unmanned air.
Nasa ames research center, moffett field, ca 94035 this paper presents an optimal control method for a class of distributedparameter systems governed by. Suboptimal model predictive control of hybrid systems based. Model predictive control stanford engineering everywhere. Suboptimal model predictive control is a technique that can reduce the computational cost of model predictive control mpc by. Realtime suboptimal model predictive control using a. Model constraints stagewise cost terminal cost openloop optimal control problem openloop optimal solution is not robust must be coupled with online state model parameter update requires online solution for each updated problem analytical solution possible only in a few cases lq control. Model predictive control for a full bridge dcdc converter.
By running closedloop simulations, you can evaluate controller performance. Model predictive control mpc represents a very simple idea for control design, which is intuitively understandable and can be implemented using standard tools. Model predictive control mpc is one of the most successful control techniques that can be used with hybrid systems. Suboptimal model predictive control of hybrid systems based on modeswitching constraints a.
Optimal control of a nonlinear fedbatch fermentation process. Suboptimal model predictive control feasibility implies. Worstcase formulations of model predictive control for systems with. Simulationbased optimal tuning of model predictive control. Model predictive control mpc is an advanced method of process control that is used to control a process while satisfying a set of constraints. Due to global competition, customers have more supply alternatives than ever before. Model predictive control linear convex optimal control. This is because pure product, which is the main goal of control, would be achieved using a fine control, whose way goes through modelbased control methods. Macadams driver model 1980 consider predictive control design simple kinematical model of a car driving at speed v. It has 10 states, 3 manipulated variables mv, and 3 outputs ov. The toolbox lets you specify plant and disturbance. Model predictive control mpc is an established method for the control of multiv ari able constrained systems. Suboptimal model predictive t l f ibilit i li t bilit ieee ac v l 44 3 648 654 riccardo scattolini dip.
A semismooth predictor corrector method for suboptimal model. Suboptimal and simplified mpc strategies, 26, 27, 28, 29, allow computational complexity to be reduced at the cost of per formance loss. A model predictive control approach for time optimal point. Combining the philosophies of nonlinear model predictive control and approximate dynamic programming, a new suboptimal control design technique is presented in this paper, named as model. Model predictive control is a receding control approach, that basically does online dynamic optmisation on a finite horizon while implementing online, the first control optimal action of the. These tools originate from di erent elds of research such as system theory, modeling, di erential and di erence equations, simulation, optimization and optimal control. Introduction model predictive control mpc strategies have found a wide range. A model predictive control approach for time optimal pointto. Nonlinear model predictive control technique for unmanned.
Model predictive optimal control of a timedelay distributed. Dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Backstepping controller bs and model predictive controller mpc have. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid system. Since the beginning of the 1990s, a real boom in the number of industrial. The rockwell automation model predictive control delivers customer value. An introduction to modelbased predictive control mpc. Model predictive control tutorial a basic model predictive control mpc tutorial demonstrates the capability of a solver to determine a dynamic move plan.
To determine whether the suboptimal solution provides acceptable control performance for your application, run simulations across your operating range. The formulation of timeoptimal behavior within the model predictive control. An introduction to modelbased predictive control mpc by stanislaw h. Practical difficulties involved in implementing stabilizing model predictive control laws for nonlinear systems are well known.
Index termsdualmode control, nonconvex nonlinear optimization, nonlinear model predictive control, suboptimal control. Bates department of chemical and biological engineering and computer sciences department department of civil and industrial engineering dici, university of pisa, italy 20 siam conference on control and. Some simulation abilities were provided to simulate the closed loop performance of the controlled hybrid. However, cant we say that in principle due to the finitehorizon nature of mpc, the solution result obtains does not guarantee global optimality in the infinite. Integration of model predictive control and backstepping approach. Nlc with predictive models is a dynamic optimization approach that seeks to follow. Model predictive control mpc, also known as receding horizon control or moving horizon control, uses the range of control methods, making the use of an explicit dynamic plant model to predict the effect of future reactions of the manipulated variables on the output and the control signal obtained by minimizing the cost function 7. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. Use suboptimal solution in fast mpc applications matlab.
Bemporad abstractmodel predictive control mpc is recognized as a very versatile and effective way of controlling constrained hybrid dynamical systems in closedloop. The residuals, the differences between the actual and predicted outputs, serve as the feedback signal to a. The university of newcastle, callaghan,nsw, 2308,australia advancedcontrol systems sac, technical university of ca talonia. The first control action is taken and then the entire process is repeated at the next time instance. 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. Nlc with predictive models is a dynamic optimization approach that seeks to. It is very attractive because of its zero voltage switching,lowcomponentstresses,andhighpowerdensityfea. Tutorial overview of model predictive control ieee control. The first decade is characterized by the fastgrowing industrial adoption of the. The idea of inputtostate stability iss is introduced and a lyapunovlike sufficient condition for iss is presented. On the inherent robustness of suboptimal model predictive control. Bemporad abstract model predictive control mpc is recognized as a very versatile and effective way of controlling constrained hybrid dynamical systems in closedloop. To this end, we introduce a nonempty state con straint set x.
Abstractmodel predictive control mpc is recognized as a very versatile and effective way of controlling constrained hybrid dynamical systems in closedloop. Model predictive control university of connecticut. Apr 02, 2015 dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Ee392m winter 2003 control engineering 1217 mpc as imc mpc is a special case of imc closedloop dynamics filter dynamics integrator in disturbance estimator n poles z0 in the fsr model update plant prediction model reference optimizer output disturbance. This lecture provides an overview of model predictive control mpc, which is one of the most powerful and general control frameworks. The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. The plant model is a stable randomly generated statespace system. Nasa ames research center, moffett field, ca 94035 this paper presents an optimal control method for a.
The process is repeated because objective targets may change or updated measurements may have. In this paper we introduce a new approach combining the two paradigms of explicit and online mpc to overcome their individual limitations. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization. Model free methods due to lack of detailed information of the system model can not lead to a high performance controller. Because mpc is based on perpetually solving optimal control. Suboptimal model predictive control of hybrid systems based on. Introduction model predictive control mpc originated in the late seventies. It started to emerge industrially in the 1980s as idcom richalet et. 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. An algorithm is developed that allows quick computation of suboptimal control moves. Model predictive control mpc this example, from control systems, shows a typical model predictive control problem.
Is model predictive control a suboptimal technique in principle when. Currently available methods either compute the optimal controller online or derive an explicit control law. This is because pure product, which is the main goal of control, would be achieved using a fine control, whose way goes through model based control methods. Tutorial overview of model predictive control ieee control systems mag azine author. Ee392m winter 2003 control engineering 1217 mpc as imc mpc is a special case of imc closedloop dynamics filter dynamics integrator in disturbance estimator n poles z0 in the fsr. Optimal and suboptimal eventtriggering in linear model. Suboptimal hybrid model predictive control springerlink.
Jun 10, 2018 this lecture provides an overview of model predictive control mpc, which is one of the most powerful and general control frameworks. On the other hand, penalizing u may also be desired for modeling pur. Process control in the chemical industries 115 model predictive control an introduction 1. Based on this, we show that the suboptimal predictive. Certaintyequivalent control i a simple usually suboptimal policy i replace each w twith some predicted, likely, or typical value t i stochastic control problem reduces to deterministic control problem, called. Simulationbased optimal tuning of model predictive. Stochastic control i can solve stochastic control problem in some cases i x,uw nite and as a practical matter, not too big i x,uw nite dimensional vector spaces, f ta ne, g tconvex quadratic i and a few other special cases i in other situations, must resort to heuristics, suboptimal policies 4. This process is experimental and the keywords may be updated as the learning algorithm improves. Mpc has gained wide acceptance in the chemical and other. Model used to represent the process and the noises.
1182 660 370 653 1021 230 728 889 897 724 364 90 884 149 656 237 1001 781 87 387 995 201 640 1165 1485 219 1286 1478 323 916 882 1321 898