Discrete-time sliding mode control for a class of nonlinear process

The efficient control of nonlinear processes is generally considered to be challenging. The development of digital computers promotes the study of nonlinear process control technology. Due to the discrete sampling of digital computer, it is necessary to develop the corresponding control algorithms for nonlinear processes. In this paper, a new equivalent control-based discrete-time sliding mode control is proposed for a class of nonlinear process with uncertainty and external disturbance. An adaptive law and a disturbance observer are designed to estimate the uncertainty and the disturbance, respectively. By combining with them, the new discrete-time sliding mode control is developed with good performance. The corresponding theoretical analysis is well verified by using Lyapunov function. Finally, the proposed approach is demonstrated by case studies in light of MATLAB.

Theoretical study of the Nonlinear Quadratic Optimal Control implementation

In this brief, I study the finite and infinite nonlinear discrete time optimal control. The quadratic control problem for nonlinear case can be solved with different methods such as: linearization of the system model around each operation point or some different methods, where should be used an on-line parameter identification algorithm. In this paper, I study some properties of these algorithms in order to improve the control efficiency of the nonlinear process control. In this paper, I supposed the all states are accessible, so there is not necessary any state estimation algorithm for the implementation of the proposed optimal control (LQR - Linear Quadratic Regulators) methods.

Assessment of control loop performance for nonlinear process

Controller performance assessment (CPA) is concerned with the design of analytical tools that are utilized to evaluate the performance of process control loops. The objective of the CPA is to ensure that control systems operate at their full potential, and also to indicate when a controller design is performing unsatisfactorily under current closed loop conditions. Such monitoring efforts are imperative to minimize product variability, improve production rates and reduce wastage. Various studies conducted on process control loop performance indicate that as many as 60% of control loops often suffer from some kind of performance problem. It is therefore an important task to detect unsatisfactory control loop behavior and suggest remedial action. Such a monitoring system must be integrated into the control system life span as plant changes and hardware issues become apparent. CPA is well established for linear systems. However, not much research has been conducted on CPA for nonlinear systems. Traditional CPA analytical tools depend on the theoretical minimum variance control law that is derived from models of linear systems. In systems exhibiting dominant nonlinear behavior, the accuracy of linear based CPA is compromised. In light of this, there is a need to broaden existing CPA knowledge base with comprehensive benchmarking indices for the performance analysis of nonlinear process control systems. The research efforts presented in this thesis focuses on the development and analysis of such CPA tools for univariate nonlinear process control loops experiencing the negative effects of dominant nonlinearities emanating from the process. Two novel CPA frameworks are proposed; first a model based nonlinear assessment index is developed using an open loop model of the plant in an artificial neural network NARMAX (NNARMAX) representation. The nonlinear control loop is optimized offline using a proposed Nelder Mead-Particle Swarm Optimization (NM-PSO) hybrid search to determine global optimal control parameters for a gain scheduled PID controller. Application of the benchmark in real-time utilizes a synthetic process output derived from the NNARMAX system which is compared to the actual closed loop performance. In the case where no process model is available, a second method is presented. An autonomous data driven approach based on Multi-Class Support Vector Machines (MC- SVMs) is developed and analyzed. Unlike the model based method, the closed loop performance is classified according to five distinct class groups. MC-SVM classifier requires minimal process loop information other than routine operating closed loop data. Several simulation case studies conducted using MATLAB™ software package demonstrate the effectiveness of the proposed performance indices. Furthermore, the methodologies presented in this work were tested on real world systems using control loop data sets from a computer interfaced full scale pilot pH neutralization plant and pulp and paper industry.

Comparative Study of Controller Design by Intelligent Techniques on Nonlinear Process Control Reactor - Continuous Stirred Tank Reactor (CSTR)

Removed due to plagiarism The original research was published in World Academy of Science, Engineering and Technology, Vol:4 2010-08-25Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process By R.Vinodha S. Abraham Lincoln and J. Prakash