About cookies, including instructions on how to turn off cookies if you wish to do so. Model predictive control of a nonlinear aeroelastic system using. Design of robust constrained modelpredictive controllers. Three extended horizon adaptive nonlinear predictive control schemes based on the parametric volterra model. How we measure reads a read is counted each time someone views a. The purpose of this study is to investigate the potential effectiveness of using a volterra based model predictive control strategy to control a nonlinear aeroelastic system. Use of a nonlinear minmax model predictive control with large prediction horizon. This paper presents a constrained model predictive control strategy that can be used to control nonlinear processes described by volterra models. More than 15 years after model predictive control mpc appeared in industry as an effective means to. The advantage of the proposed model is that it is linear in parameters and the parameters can be estimated using recursive leastsquare method. Nonlinear mpc controllers, using second and thirdorder volterra models, are then used to control the nonlinear aeroelastic system for regulator and tracking.
Since, a lyophilization cycle involves a high energy demands it is needed to be used an improved control strategy in order to minimize the. In general, industrial processes are nonlinear, but, as has been shown in this book, most mpc applications are based on the use of linear models. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non. Exploiting the volterra laguerre model structure robert s. In particular, we extend an adaptive predictive algorithm without taking into account constraints. This proposed robust control copes with physical constraints and geometrical constraints due to parameter uncertainties. It also offers a thirdorder volterra series based nonparametric nonlinear modelling technique for nmpc design, which relieves practising engineers from the need for deriving a physicalprinciples based model first. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The predictive control problem of nonlinear systems can be transformed into predictive control of linear systems, the method include hammerstein m odel, wiener model, volterra model or neural network68, etc.
Model predictive control mpc originated in the late seventies and has developed considerably since then. This control strategy is computationally efficient as the exact worst case cost can be. Part of the aerospace engineering commons scholarly commons citation law, wai leuk, model predictive control of a nonlinear aeroelastic system using volterra series. In this paper, a new nonlinear volterra series model is built based upon positive, negative and double step responses. Robust stability is achieved through the addition of cost function constraints that prevent the sequence of the optimal controller costs from increasing for the true plant. Pdf three extended horizon adaptive nonlinear predictive.
A model predictive control algorithm based on the second order volterra model is proposed, and simulation results for the control of two isothermal continuous stirred tank reactors are presented. Nonlinear predictive control of smooth nonlinear systems. This work addresses the design of a nonlinear model predictive control nmpc strategy for greenhouse temperature control using natural ventilation. A methodology is proposed for designing robust nonlinear model predictive controllers based on a volterra series model with uncertain coefficients. The term model predictive control does not designate a specific control strategy but rather an ample range of control methods which make explicit use of a model of the process to obtain the control signal by minimizing an objective function. Pdf model predictive control of a nonlinear aeroelastic. Volterra series models for nonlinear system control. Model predictive control, volterra series, process control, nonlinear control. Two formulations of a nonlinear model predictive control scheme based on the secondorder volterra series model are presented. A nonlinear model predictive control scheme using second.
Highlights identification of an uncertain nonlinear volterra series model of a pilot plant. A key idea for this study is that based on the volterra model it is possible to formulate the robust predictive control problem as. Read nonlinear mpc based on a volterra series model for greenhouse temperature control using natural ventilation, control engineering practice on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. During the past decade model predictive control mpc, also referred to as receding horizon control or. We consider single inputsingle output siso nonlinear systems described by arxplus volterra models. Pdf nonlinear model predictive control of a simulated. The controller is based on a truncated fuzzyneural volterra predictive model and a simplified gradient optimization algorithm. Highorder volterra model predictive control and its application to a nonlinear polymerisation process article pdf available in international journal of. Constrained nonlinear predictive control using volterra models.
This paper proposes a new approach for synthesizing a robust predictive controller for nonlinear uncertain process by exploiting a reduced complexity discretetime volterra model known as sparafac volterra model applied to the quadratic case. Design of fuzzyneural volterra model volterra models are widely used to model nonlinear processes. Due to the significant difference between the time constants of the. Korean journal of chemical engineering 2004, 21 2, 329337.
Process identification using second order volterra models. Model predictive control using volterra series springerlink. As a simulation study, the new predictive control algorithm is applied to a non. Department of chemical and petroleum engineering university of pittsburgh pittsburgh, pa 15261 abstract an analytical solution to the nonlinear model predictive control nmpc optimization problem is derived for singleinput. This process is experimental and the keywords may be updated as the learning algorithm improves.
In this thesis we develop a nonlinear robust model predictive control nrmpc algorithm for nonlinear plants modeled by second order volterra series. This paper describes a method for designing a nonlinear model predictive controller to be used in a lyophilization plant. Highorder volterra model predictive control and its application to a nonlinear polymerisation process. Polymerization reactor control using autoregressive. Fast nonlinear model predictive control using second order volterra models based multiagent approach. The results obtained during the simulation experiment are compared to the case when a standard gradient optimization procedure is used. Nonlinear modelbased control using secondorder volterra. This paper presents the development of a nonlinear model predictive controller nmpc based on the identification of a volterra model from inputoutput data. Sufficient conditions are developed in this work for robust stability and pefformance of nonlinear model. Model predictive control mpc, also known as receding horizon control rhc, entails computing optimal control inputs over a finite time horizon, applying a portion of the computed optimal control sequence.
The proposed approach is studied to control the product temperature in a lyophilization plant. Highorder volterra model predictive control and its application to a nonlinear polymerisation process article pdf available in international journal of automation and computing 22. Pdf on jan 1, 2011, y todorov and others published volterra model predictive control of lyophilization plant. By continuing to browse this site you agree to us using cookies as described in about cookies remove maintenance message. The advantages of the developed approach lie not only in control performance superior to existing nmpc methods, but also in eliminating the need for converting an analytical model and then convert it to a volterra model obtainable only up to the second order. This control strategy is computationally efficient. The first formulation determines the control action using successive substitution, and the second method directly solves a. A newton optimization method approach find, read and cite all the research you. Model predictive control mpc has recently found wide acceptance in the process industry, but the existing design and implementation methods are restricted to linear process models. Introduction model predictive control mpc refers to a class of computer control algorithms that utilize. This model structure is used for adaptive identification of. Lyophilization plants are widely used by pharmaceutical industries to produce stable dried medications and important preparations.
Highorder volterra model predictive control and its. Modified volterra modelbased nonlinear model predictive. This paper presents a new nonlinear minmax model predictive control strategy based on volterra models. Model predictive control mpc, also known as receding horizon control rhc, entails computing optimal control inputs over a finite time horizon, applying a portion of the computed optimal control sequence, and then. However, hammerstein model, wiener model and volterra model can only be used for some specific process. Pdf we present model predictive control mpc with constraints on continuous mixed reactor and based on a volterra model. This paper deals with the identification and the control of nonlinear processes described by inputoutput models, such as parametric volterra models. To have a less complexity model we expand each linear sub model on an orthogonal laguerre basis, the characteristic pole of which should be optimized. Volterra model predictive control of lyophilization plant. Design of a nonlinear minmax model predictive control with low computational burden. Model predictive control of a nonlinear aeroelastic system using volterra series representations wai leuk law follow this and additional works at. Robust nonlinear model predictive control using volterra.
Nonlinear mpc based on a volterra series model for. Model predictive control prediction horizon volterra series volterra model manipulate variable these keywords were added by machine and not by the authors. Pdf nonlinear model predictive control of greenhouse. Fast nonlinear model predictive control using second order. Sparse identification of nonlinear dynamics for model predictive.
Volterra models for nonlinear model predictive control design of. The responses of both reactors controlled with a linear mpc scheme and the second order volterra mpc scheme are compared to a desired, linear reference trajectory. For the design of a model predictive control strategy based on the identified secondorder volterra series model, the following general model predictive control optimization problem is considered. Volterra model predictive control of a lyophilization plant abstract. Constrained nonlinear model based predictive control using arxplus volterra models. Model predictive control offers several important advantages.
These models are particularly suitable for nonlinear model predictive control scheme. Model predictive control of a nonlinear aeroelastic system using reducedorder volterra models. Predictive control esterification reactor parametric volterra model constrained optimization. A nonlinear controller synthesis scheme is presented that retains the original spirit and characteristics of conventional linear model predictive control mpc. Nonlinear model based control using secondorder volterra models. Lssvm predictive control based on improved free search. Pdf fast nonlinear model predictive control using second order. Model predictive control of a nonlinear aeroelastic system.
The predictive control algorithm for nonlinear system is then proposed, and the existence and uniqueness of the solution are proved mathematically. Nonlinear modelbased control using secondorder volterra models. The nmpc strategy is based on a secondorder volterra series model identified from experimental inputoutput data of a. Nonlinear minmax model predictive control with inputtostate practical stability. Nonlinear model predictive control of an esterification. Volterra model predictive control of a lyophilization. Robust nonlinear model predictive control using volterra models. Modelbased control strategies, such as model predictive control, are. Volterra model based nonlinear model predictive control nmpc is then developed to regulate the airfuel ratio afr at the stoichiometric value. The scheme employs a volterra model a simple and convenient nonlinear extension of the linear convolution model employed by conventional mpcand gives rise to a controller composed of a.
The controller exploits the special structure of the model to achieve an online feasible solution to the general optimization problem. This paper proposes a nonlinear model based predictive control nmpc algorithm for nonlinear systems by using multiple models approach. Pdf constrained nonlinear modelbased predictive control. Volterra model predictive control of a lyophilization plant conference paper pdf available october 2008. Modelling of nonlinear dynamics of an air manifold and fuel injection in an internal combustion ic engine is investigated in this paper using the volterra series model. Applied for the quadratic hammerstein and volterra models. Modified volterra model based nonlinear model predictive control of ic engines with realtime simulations yiran shi, dingli. Pdf highorder volterra model predictive control and its. It develops an analytical framework for nonlinear model predictive control nmpc. Find, read and cite all the research you need on researchgate. In this paper a nonlinear adaptive constrained model predictive control scheme based on models identified from inputoutput data is proposed.
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