Long-range Predictive Control Algorithm Research Articles

Fuzzy model-based predictive control using an ARX structure with feedforward

A long-range model-based predictive control algorithm which is based on the Takagi–Sugeno–Kang piece-wise linear fuzzy modelling approach is proposed. A non-linear multivariable chemical processes represented by a binary distillation column is used as a test-bed for a series of experiments, results of which suggest that the controller performs better than the long-range predictive control algorithm which is based on a linear modelling approach, in terms of effective set-point tracking and interactions handling.

Adaptive estimation for fuzzy TSK model-based predictive control

A long-range predictive control algorithm for nonlinear processes operating over a wide range is proposed and is based on the Takagi-Sugeno-Kang (TSK) piece-wise linear fuzzy modelling approach. The performance of the fuzzy modelling approach integrated with the proposed control algorithm to form an adaptive control scheme is examined using a series of experiments on a liquid level system and on a continuous stirred tank reactor (CSTR) system.

Adaptive Fuzzy TSK Model-Based Predictive Control Using a Carima Model Structure

A new long-range predictive control algorithm for non-linear processes is proposed and is based on the popular Generalized Predictive Control (GPC) algorithm using the Takagi-Sugeno Kang (TSK) piece-wise fuzzy modelling approach. The proposed fuzzy modelling approach is integrated with the proposed control algorithm to form an adaptive control scheme based on a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model structure. The performance of the adaptive control scheme is assessed using a series of experiments on the binary distillation column and on the Continuous Stirred Tank Reactor (CSTR) system.

Adaptive Estimation for Fuzzy TSK Model-Based Predictive Control

A long-range predictive control algorithm for nonlinear processes operating over a wide range is proposed based on the Takagi-Sugeno piece:wise linear fuzzy modelling method. The performance of the fuzzy modelling method and the proposed control algorithm is examined using a series of simulations on a continuous stirred tank reactor (CSTR) system.

Long-Range Predictive Control of the Parametric Hammerstein Model

Predictive control algorithms are promising also in the case of nonlinear systems. Long-range predictive control algorithms are derived here for the nonlinear Hammerstein model. A quadratic cost function is minimized, which considers the quadratic deviation of the reference signal and the output signal predicted in a future horizon and punishes also the squares of the control increments. The predictive incremental form of the Hammerstein model is used to predict the output signal. Suboptimal versions of the control algorithm are given with different assumptions for the control signal during the control horizon. Some properties of these algorithms are shown through simulation examples. Behaviour of long-range predictive control algorithms is compared with the performance of one-step-ahead predictive control algorithms.

A neural net model-based multivariable long-range predictive control strategy applied in thermal power plant control

A constrained multivariable control strategy along with its application in more efficient thermal power plant control is presented in this paper. A neural network model-based nonlinear long-range predictive control algorithm is derived, which provides offset-free closed-loop behavior with a proper and consistent treatment of modeling errors and other disturbances. A multivariable controller is designed and implemented using this algorithm. The system constraints are taken into account by including them in the control algorithm using real-time optimization. By running a simulation of a 200 MW oil-fired drum-boiler thermal power plant over a load-profile along with suitable PRBS signals superimposed on controls, the operating data is generated. Neural network (NN) modeling techniques have been used for identifying global dynamic models (NNARX models) of the plant variables off-line from the data. To demonstrate the superiority of the strategy in a MIMO case, the controller has been used in the simulation to control main steam pressure and temperature, and reheat steam temperature during load-cycling and other severe plant operating conditions.

A Cost Function for Continuous-flow Grain Drying and its Use in Control

A cost function for continuous-flow grain drying was developed, which relates grain moisture content, grain temperature and thermal damage, fuel used for drying and elapsed time to the cost of drying per tonne of output grain. A simplified model of a grain dryer was developed for rapid calculation of these parameters during simulation of unsteady-state drying. The cost function and dryer model were used in a long-range predictive control algorithm which, for each control action of an existing feedforward-plus-feedback system, determined and applied near-optimal values of target moisture content and drying air temperature. The complete system was tested using computer simulation of a mixed-flow dryer to represent a real dryer, and the overall cost of drying was compared with that given by a system with the lowest cost combination of a fixed target moisture content and drying air temperature. The system demonstrated a well-behaved response over a range of input grain moisture content from 0·20 to 0·30 dry basis and showed an 18% saving in time and a cost saving of 0·30 £ t −1. The cost incurred by grain recirculation and the problem of control instability caused by recirculation were also investigated.

Dynamic matrix control of an autogenous grinding circuit

Autogenous grinding is characterized by non-linearities, time-varying dynamics and a high level of uncertainties, conditions which usually originate from the variability of the ore feed characteristics (hardness and grade). These characteristics make this grinding operation particularly appealing for some type of knowledge-based control. This paper discusses the application of the dynamic matrix control algorithm to an autogenous grinding operation. This control algorithm is a long-range predictive control algorithm which has been successfully applied to other processes. The study was carried out using an empirical simulator calibrated with industrial data. The simulation results were compared to those obtained using PID and learning controllers. The ability of the dynamic matrix control to improve the efficiency of this complex and highly sensitive process is clearly demonstrated.

Long-range predictive control of a rougher flotation unit

This work deals with the long-range predictive control of a flotation circuit simulated using a phenomenological model. As in the generalized predictive control approach, the control strategy is based on a multistep cost function minimization subject to the constraint that, over a certain control horizon, the future control increments are equal to zero. A linear discrete input-output model is used to represent the complex dynamics of the flotation circuit. The control variables are the air feed rate and the collector feedrate to ore feedrate ratio. A robust identification scheme is used for parameter estimation purposes. Simulation results highlight the performance of this long-range predictive control algorithm.

Nonlinear Long-Range Predictive Control of a Distillation Pilot Plant

Abstract A distillation column separating ethanol-water mixture has been built at our department several years ago. The pilot plant is equipped with analogue instrumentation, manipulating board and alarm devices. It is connected to a microcomputer and a supervisory computer. The aim of the control is to keep the concentration of the products constant. Further on several physical quantities have to be controlled (feed flow rate, feed temperature, reflux flow rate, liquid level in the reboiler, etc.). The plant is highly nonlinear. Long-range predictive control algorithms based on nonparametric system description have been generalized to nonlinear systems characterized by Volterra series by Bars and Haber (1988). These algorithms have been applied successfully to some control loops of the distillation column. Initial experimental results are presented here.

Multivariable Long-Range Predictive Control Algorithm Applied to a Continuous Flow Fermentation Process

The dynamics of biotechnological processes are highly complex and usually unknown. These processes are described by a set of non linear and non stationary equations derived from mass balance considerations. The complexity of these systems limits the application of classical control techniques and indicates that adaptive techniques may be of interest. This paper presents a simulation study of the multivariable generalized predictive control applied to a continuous flow fermentation process. The control design is based on predicting the process outputs over several steps and assumptions on future control actions. The control design is based on a linear discrete-time model with unknown and possibly varying parameters. The simulation is performed using a nonlinear and time-varying model of the process . These parameters are estimated by a constant trace adaptation algorithm with necessary features (data filtering and normalisation, UD factorization, dead zone .. ) that enhance the robustness of the resulting self-tuning algorithm. In the parametric model, the dilution rate and the influent substrate concentration are the control variables (Inputs) and the biomass and substrate concentrations stand for the controlled variables (Outputs). Numerical results illustrate the good performance and the excellent properties of the presented control scheme, particularly its ability to cope with varying dynamics and high level disturbances.