Nonparametric nonlinear model predictive control

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC

Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference

Shaping of molecular weight distribution using b-spline based predictive probability density function control

Issues of modelling and control of molecular weight distributions (MWDs) of polymerization products have been studied under the recently developed framework of stochastic distribution control, where the purpose is to design the required control inputs that can effectively shape the output probability density functions (PDFs) of the dynamic stochastic systems. The B-spline Neural Network has been implemented to approximate the function of MWDs provided by the mechanism model, based on which a new predictive PDF control strategy has been developed. A simulation study of MWD control of a pilot-plant styrene polymerization process has been given to demonstrate the effectiveness of the algorithms

Non-linear models for a gypsum kiln. A comparative analysis

INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL. WORLD CONGRESS (15.2002.BARCELONA)This paper presents several non-linear models adjusted in order to capture the dynamics of a gypsum kiln. The behavior of this kind of processes is affected by nonlinear effects caused by the existence of disturbances and the coupling among some variables. The use of second order Volterra and Hammerstein models as appropriate solutions to describe the process dynamics is analyzed. A thorough study of the best model order and structure is performed. Coefficients that best fit real data are also selected. This work aims to obtain a good non-linear model in order to implement a non-linear predictive controller, able to improve the performances of those linear controllers already tested on the plant.Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Predictive functional control for the temperature control of a chemical batch reactor

A predictive functional control (PFC) technique is applied to the temperature control of a pilot-plant batch reactor equipped with a mono-fluid heating/cooling system. A cascade control structure has been implemented according to the process sub-units reactor and heating/cooling system. Hereby differences in the sub-units dynamics are taken into consideration. PFC technique is described and its main differences with a standard model predictive control (MPC) technique are discussed. To evaluate its robustness, PFC has been applied to the temperature control of an exothermic chemical reaction. Experimental results show that PFC enables a precise tracking of the set-point temperature and that the PFC performances are mainly determined by its internal dynamic process model. Finally, results show the performance of the cascade control structure to handle different dynamics of the heating/cooling system

Shaping of molecular weight distribution by iterative learning probability density function control strategies

A mathematical model is developed for the molecular weight distribution (MWD) of free-radical styrene polymerization in a simulated semi-batch reactor system. The generation function technique and moment method are employed to establish the MWD model in the form of Schultz-Zimmdistribution. Both static and dynamic models are described in detail. In order to achieve the closed-loop MWD shaping by output probability density function (PDF) control, the dynamic MWD model is further developed by a linear B-spline approximation. Based on the general form of the B-spline MWD model, iterative learning PDF control strategies have been investigated in order to improve the MWD control performance. Discussions on the simulation studies show the advantages and limitations of the methodology

Comparison of PID and MPC controllers for continuous stirred tank reactor (CSTR) concentration control

Continuous Stirred Tank Reactor (CSTR) is amajorarea in process, chemical and control engineering. In this paper, PID and MPC controllers are designed for CSTR in order to analyze the output concentration of the system by comparing the two proposed systems using Matlab/Simulink. Comparison have been made using two desired concentration input (Random reference and step) signals with and without input side disturbance (Flow rate error). The simulation result shows that the continuous stirred tank reactor with MPC controller have better response in minimizing the overshoot and tracking the desired concentration for the system without input disturbance and with the effect of the disturbance makes the continuous stirred tank reactor with MPC controller output with small fluctuations and still better than the continuous stirred tank reactor with PID controller. Finally the comparative analysis and simulation results prove the effectiveness of the continuous stirred tank reactor with MPC controller

Review on Advanced Control Technique in Batch Polymerization Reactor of Styrene

Polymerization process have a nonlinear nature since it exhibits a dynamic behavior throughout the process. Therefore, accurate modeling and control technique for the nonlinear process needs to be obtained