Decoupling ARX time-space model based GPC for distributed parameter system

A model predictive control approach with relevant identification in dynamic PLS framework

An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.

In the complex heating systems, higher order, large inertia, large delay and uncertainty typically exist. This work introduces generalized predictive PID control algorithm to adjust the system. The purpose of the control algorithm is to achieve a steady state in a shorter time. On the basis of analyzing PID control algorithm and Generalized Predictive Control (GPC), configuring a given performance function index control-weighted sequences to GPC control. Derived form of generalized predictive control algorithm with PID. The Matlab simulation shows that using generalized predictive PID control algorithm can produce better results in shorter time than simple using PID or GPC in the heating system.

In order to improve the dependability and security of the lever control system, we introduced the generalized predictive control (GPC) algorithm. We used MATLAB/Simulink program development tools to control the double capacity water tank. At first, we used the water tank level control device as the object and set up a mathematical model of the control system. Then we put forward the generalized predictive control algorithm based on the model. The simulation results show that the presented algorithm has a good dynamic response performance, fast-track settings, and a preferable control effect. Experimental results show that the improved methods are reasonable and practicable.

Generalized predictive controller (GPC) has robust performance when faced to control non-minimum phase and open-loop unstable processes. GPC has been originally developed with linear predictor models which leads to a formulation that can be solved analytically. If a nonlinear model is used then nonlinear optimization and nonlinear modeling techniques are necessary. This paper presents a new method of multivariable GPC design for nonlinear systems. The proposed GPC design incorporates the neural network model based on radial basis function neural network and optimization methodology of cost function by Lamarckian hybrid method using evolutionary programming and Nelder-Mead’s simplex algorithm. Steps of the control law implementation and intelligent procedure are discussed. Simulation results on a nonlinear MIMO (multi-input multi-output) system are presented to show the effectiveness of the neuro-evolutionary GPC method. The new GPC design is applicable for improve the approximation of nonlinear systems with unknown interactor matrix using neural network model and an efficient control law optimization task by simplex-evolutionary hybrid methodology.

In this work, a practical event-based framework for multivariable processes is introduced. The developed control algorithm is based on a Generalized Predictive Control (GPC) algorithm suitable for MIMO systems. The most important advantage of the proposed control scheme is the adaptability of control actions to process disturbances and process interactions. Moreover, event-based approach allows prioritizing selected control variables, paying more attention to important variables (often related to security or costs) while neglecting non-critical ones. This is possible through adjusting the sensor deadband value, making feasible a tradeoff between control effort and control performance. Using this approach, it is possible to keep less relevant variables within the established limits, focusing the control system attention on those critical for process operation. The analyzed event-based control technique for multivariable systems is tested through a simulation study and considering an industrial stirred tank reactor process. The obtained results show several benefits regarding event-based control application, keeping all properties of event-based approach as well as model predictive control ones.

During the spinning forming process of chitosan and collagen absorbable suture, the diameter is uneven. To solve this problem, this article provides a diameter control system of absorbable suture which based on generalized predictive control (GPC) algorithm. The system consists of the diameter detection module based on linear CCD, the control module based on STM32, the actuator of reverse series type double hydraulic cylinder. The control algorithm adopts self-adaptive generalized predictive control (GPC) algorithm which successes in realizing closed loop control of large time delay system in the process of absorbable suture forming. The results of the experiment show that the control system has good rapidity and good stability, the control accuracy reached 1%.

In the servo system, the autotuning controller based on a generalized predictive control (GPC) usually requires a specified reference instruction and the accurately linear system model. The former seriously limits the servo applications, while the latter's lack easily reduces the control performance. This article proposes a variable-structure proportional–integral (VSPI) velocity controller based on a GPC and an interconnected structure to address the above limitations. First, the VSPI controller is designed to track the arbitrary-order instruction without a static difference. Second, based on the Taylor series and the higher order difference operators, an improved prediction model, whose prediction output only includes the velocity error, is constructed to liberate the GPC from the reference instruction. Then, the control parameters of the VSPI controller can be adjusted online by the rolling optimization. Furthermore, the interconnected structure using three modified sliding-mode observers (MSMOs) is designed to efficiently provide the system model for the GPC, where an adaptive gain and an equivalent derivative designed in each MSMO make it possible for the identification parameters to converge exponentially. Finally, the third MSMO is used to identify the load disturbance for promoting the interconnected structure and the disturbance compensation, which can ensure the identification accuracy and the linearization of the system model. Simulation and experimental results demonstrate the effectiveness of the proposed method.

S0ingle-input/single-output generalised predictive control (GPC) has been applied successfully to muscle-relaxant anaesthesia in both simulations and clinical trials. This work was extended later to the multivariable case involving simultaneous control of muscle relaxation through EMG measurements, and unconciousness using blood-pressure monitoring in a series of simulation studies. To achieve even higher degrees of automation, a fault detection isolation and accommodation layer has been superimposed on top of the multivariable GPC strategy. The performance of the overall supervisory control scheme has been evaluated in a series of simulation runs on a work station. The results are encouraging in the sense that the algorithm has proved very effective in detecting, diagnosing and eventually compensating for various simulated faults normally encountered in the operating theatre. The experiments used two versions of GPC: the multivariable GPC (using a P-canonical form for the process model) and GPC with feedforward, and were conducted in noise-free and noise-corrupted situations since operating theatres can be electrically dirty environments. The study forms the basis for an experiment protocol which will be followed in clinical trials.

This paper presents a generalized predictive control (GPC) strategy for a spatially distributed transport-reaction process based on time–space decomposition. First, the Karhunen–Loeve (K–L) decompo...

A kind of soft-sensing method of gauge of strip based on fast generalized predictive control(GPC) algorithm is presented. Based on in-depth studying simplified calculating principle of fast GPC algorithm, and using fast algorithm’s characteristics of small calculation amount and fast calculating speed the method establishes the real-time online soft-sensing model of the strip gauge. Combining GPC’s advantages of multi-steps prediction, rolling horizon optimization and feedback compensation etc, it can better solves problems of measurement delay caused by gage installation location and measurement errors caused by tension time-varying and equipment tremor. The test results show that the method can real-time, on-line measures the exit thickness of rolling strip accurately, and provides reliable guarantee for optimized control of shape and gauge. It can also be used in the key parameters measurement of other complex industrial process.

Background Availability of numerous oil deposits with a wide variety of the conditions of occurrence, reservoir properties and physical-chemical properties of fluids makes important the need of grouping of development objects and identification of relatively uniform groups by certain criteria. Grouping allows to define similarities and differences between deposits in determining development objects, and to substantiate systems of development. Analysis of large data sets on objects characterized by the measured or estimated parameters is labor-intensive, requiring a lot of time and effort, so there is a question of dimension reduction, i.e. of reducing the original data set to a smaller number of parameters, and grouping of objects into relatively uniform groups. Here the parameters can be selected from the original ones or obtained by calculation and conversion (with the least loss of information about the objects under study). An effective tool of analysis that can help in this matter is the method of principal component analysis. Aims and Objectives Apply the method of principal component analysis to reduce the number of parameters which define objects of study, so that to find effective ways to develop new oil and gas deposits, and fast and correct solution of practical problems through the use of gained summary experience and assessment of similarities and differences of oil and gas reservoirs by combinations of geological-field attributes. Methods The problem of ranking oil deposits in Western Siberia, is solved by the method of principal component analysis, which is a linear transformation of features in a new set of «independent» random values, which are arranged in descending order of their variances, and the main components are determined by the eigenvectors of the correlation matrix obtained based on input parameters characterizing the objects under investigation. Having selected the components that comprise the bulk of the variances, one can calculate them for different types of objects and classify by clusterable points. Grouping of objects of research was carried out using built-in functions of computer statistics programs (a system of statistical analysis Statistica, analytical applications for Microsoft Excel XLStat). The same calculation was performed on the basis of the theory of mathematical statistics. The calculation results coincided. Results The method of principal component analysis allowed selection from the original data set comprising 50 research objects characterized by 12 parameters of six relatively uniform groups using only 4 factors (principal components) which, however, contain much more information than the individual parameters. As a result of the carried out analysis data were systematized and the dimension of the initial population (transition from multidimensional space to the four-dimensional) was reduced, with no significant loss of information about research objects. Conclusion Based on the proposed algorithm, and relatively uniform groups obtained there is a possibility for inclusion of new deposits into one of these groups and selection of the nearest to it analogous object, that will simplify the design of development system and identify ways and means of improving system’s effectiveness.

ABSTRACTA second look at biodiesel reactor control using Recursive Least Squares (RLS)‐based adaptive Generalized Predictive Control (GPC) strategy revealed the possibility of a simpler alternative to the previously published centralized RLS‐based GPC controller (CRLS‐GPC). New results show that the simpler decentralized RLS‐based GPC controller (DRLS‐GPC) was on par with the more sophisticated centralized version in terms of servo and regulatory control, process interactions handling, and the resultant controller moves. Moreover, the simplified control scheme remained superior to the conventional Proportional–Integral controller. Such attributes make the DRLS‐GPC an attractive compromise between complexity and performance. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd.

ABSTRACTThis study presents an integrated approach, based on data envelopment analysis (DEA) and principal component analysis (PCA) methods, to evaluate the influence of Six Sigma deployment on key job characteristics in an automotive industry. The job characteristics are defined as satisfaction, stress, and security. A standard questionnaire is designed and distributed among the employees at the company's production site, who were affected by the implementation of Six Sigma. DEA and PCA methods are applied to measure the performance of the sub-groups of employees in the company. Consequently, the most efficient and inefficient sub-groups are determined. According to the findings of this investigation, it was perceived that the implementation of Six Sigma has had the greatest impact on job satisfaction. Additionally, a design of experiment was carried out to recognize the most effective job factor, which was identified to be the overall working conditions for the related case study. This is the first study that integrates DEA and PCA toward identifying and optimizing job characteristics in terms of Six Sigma implementation. The approach, employed in this study, can be easily used in the other manufacturing systems, in order to assist them to identify and improve their key job characteristics.

Locality Preserving Projections (LPP) is a linear projective map that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many fields, it has limits to solve recognition problem. Thus, a new palmprint recognition method is proposed based on Kernel Locality Preserving Projections (KLPP). Different from LPP method, KLPP not only describes the nonlinear correlations between pixels, but also preserves the local structure of the palmprint image space. In this way, the unwanted variations resulting from in lighting may be eliminated or reduced. We compare our proposed approach with Principal Component Analysis (PCA), LPP and Kernel Principal Component Analysis (KPCA) methods on PolyU palmprint database. Experiment results demonstrate that KLPP achieves better recognition rate as the dimension of the palmprint subspace changes.