M Geetha

Weighted fuzzy fault tolerant model predictive control

This paper proposes a new active fault-tolerant control (FTC) using fuzzy predictive logic. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model to be used in fault accommodation, in a model predictive control (MPC) scheme. The fault accommodation is performed with one fuzzy model for each identified fault. The FTC scheme is used to accommodate the faults of real-time CSTR level process. The fuzzy FTC scheme proposed in this paper was able to detect, isolate and accommodate correctly the considered faults of the system.

Soft computing techniques based optimal tuning of virtual feedback PID controller for chemical tank reactor

CSTR plays a vital role in almost all the chemical reactions and is a highly nonlinear system exhibiting stable as well as unstable steady states. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration [1]. So, a virtual feedback control is implemented to control the state variables using Extended Kalman Filter (EKF) in the feedback path. Since it is hard to determine the optimal or near optimal PID parameters using classical tuning techniques like Ziegler Nichols method, a highly skilled optimization algorithm like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are used. This work is based on the optimal tuning of virtual feedback PID control for a CSTR system using soft computing algorithm for minimum Integral Square Error (ISE) condition.

Critical evaluation of non-linear filter configurations for the state estimation of Continuous Stirred Tank Reactor

Developed discrete state space model of CSTR.Implemented three non-linear filters to estimate concentration and temperature.Performance of the three filters is analyzed under various operating conditions. A systematic approach has been attempted to design a non-linear observer to estimate the states of a non-linear system. The neural network based state filtering algorithm proposed by A.G. Parlos et al. has been used to estimate the state variables, concentration and temperature in the Continuous Stirred Tank Reactor (CSTR) process. (CSTR) is a typical chemical reactor system with complex nonlinear dynamics characteristics. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration. In this work, the comparison of the performances of an extended Kalman filter (EKF), unscented Kalman filter (UKF) and neural network (NN) based state filter for CSTR that rely solely on concentration estimation of CSTR via measured reactor temperature has been done. The performances of these three filters are analyzed in simulation with Gaussian noise source under various operating conditions and model uncertainties.

Real-time implementation and performance analysis of two dimension PID fuzzy controller for continuous stirred tank reactor

Tuning the parameters of a controller is very important in system performance. Ziegler and Nichols tuning method is simple and cannot guarantee to be effective always. In order to overcome the parameter uncertainties, enhance the fast tracking performance of a process system, a brand-new two-dimension PID fuzzy controller, fuzzy PI+ fuzzy ID, is proposed in this paper. The self-tuning fuzzy PI+ fuzzy ID controller is fast; computing on-line easily and can reduce stability error. To demonstrate the advantages of the fuzzy PI+ fuzzy ID controller, has been applied to an application in the control of Continuous Stirred Tank Reactor (CSTR) level loop. The simulation and real-time implementation were executed and its results show that the proposed control scheme not only enhances the fast tracking performance, but also increases the robustness of the system. From the simulation it is clear that there is substantial improvement in the Self-tuning Fuzzy PID controller in terms of peak overshoot, settling time, peak time, rise time, Integral Square Error (ISE) and Integral Absolute Error (IAE).

Real-time implementation and performance analysis of state estimation based model predictive controller for CSTR plant

Model Predictive Control (MPC) schemes are now widely used in process industries for the control of key unit operations. Linear model predictive control schemes which make use of linear dynamic model for prediction, limit their applicability to systems which exhibit mildly nonlinear dynamics. In this paper, a state estimation based model predictive controller for nonlinear system has been proposed. The model predictive controller is designed by considering a state space model and an extended Kalman filter to predict the future behavior of the system. The efficacy of the proposed MPC scheme has been demonstrated by conducting simulation studies on the level process of a Continuously Stirred Tank Reactor (CSTR) - a MIMO system, and the real time implementation has been done in the CSTR plant to illustrate the online optimization constraint and also the advantage of MPC over conventional controller by comparison of servo-regulatory responses through ISE values.

Comparative performance analysis of extended Kalman filter and neural observer for state estimation of continuous stirred tank reactor

In this paper, a systematic approach to design a non-linear observer to estimate the states of a non-linear system is proposed. The neural network based state filtering algorithm proposed by A.G. Parlos et al. has been used to estimate the state variables, concentration and temperature in the Continuous Stirred Tank Reactor (CSTR) process. CSTR is a typical chemical reactor system with complex nonlinear dynamics characteristics. The variables which characterize the quality of the final product in CSTR are often difficult to measure in realtime and cannot be directly measured using the feedback configuration. In this work, the authors compare the performance of an Extended Kalman Filter (EKF) with respect to Neural Network (NN) based state filter for CSTR that rely solely on concentration estimation of CSTR via measured reactor temperature. The performance of these two filters is analyzed in simulation with Gaussian noise source under various operating conditions and model uncertainties.