Temperature Control of a CSTR Using a Nonlinear PID Controller

Continuous stirred tank reactor (CSTR) which plays a key role in the chemical plants exhibits highly nonlinear behavior as well as time-varying characteristics during operation. So, CSTR process control over the whole operating range has been a challenging issue especially for control engineers. A variety of feedback control algorithms and their tuning methods have been developed to guarantee the satisfactory performance despite the varied dynamic characteristics of CSTRs. This thesis presents a scheme of designing a nonlinear PID controller incorporating with a real-coded genetic algorithm (RCGA) for the temperature control of a CSTR process. The gains of the NPID controller are composed of easily implementable nonlinear functions based on the error and/or the error rate and its parameters are tuned using the RCGA by minimizing the integral of time-weighted absolute error (ITAE). A set of simulation works for reference tracking and disturbance rejecting performances and robustness to parameter changes are carried out to compare with two other nonlinear controllers and show the feasibility of the proposed method.Abstract List of Tables List of Figures Chapter 1. Introduction Chapter 2. Continuos Stirred Tank Reactor Chapter 3. Existing Controllers Chapter 4. Proposed NPID Controller Chapter 5. Simulation and Review Chapter 6. Conclusion Reference

Control strategy for automatic gantry crane systems: a practical and intelligent approach

The use of gantry crane systems for transporting payload is very common in building constructions. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. Various attempts in controlling gantry cranes system based on open- loop and closed-loop control systems were proposed. However, most of the proposed controllers were designed based on the model and parameter of the crane system. In general, modeling and parameter identifications are troublesome and time consuming task. To overcome this problem, in this paper, a practical and intelligent control method for automatic gantry crane is introduced and evaluated experimentally. The results show that the proposed method is not only effective for controlling the crane but also robust to parameter variation

The disturbance model in model based predictive control

Model Based Predictive Control (MBPC) is a control methodology which uses a process model on-line in the control computer; this model is used for calculating output predictions and optimizing control actions. The importance of the system model has been generally recognized, but less attention has been paid to the role of the disturbance model. In this paper the importance of the disturbance model is indicated with respect to the EPSAC approach to MBPC. To illustrate this importance, an example of this advanced control methodology applied to a typical mechatronic system is presented, to compare the performances obtained by using different disturbance models. It clearly shows the benefits of using an "intelligent" disturbance model instead of the "default" model generally adopted in practice

Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network

Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.Comment: 6 pages, 12 figure

Experiments in identification and control of flexible-link manipulators

Interest in the study of flexible-link manipulators for space-based applications has risen strongly in recent years. Moreover, numerous experimental results have appeared for the various problems in the modeling, identification and control of such systems. Nevertheless, relatively little literature has appeared involving laboratory verification of tuning controllers for certain types of realistic flexible-link manipulators. Specifically flexible-link manipulators which are required to maintain endpoint accuracy while manipulating loads that are possibly unknown and varying as they undergo disturbance effects from the environment and workspace. Endpoint position control of flexible-link manipulators in these areas are discussed, with laboratory setups consisting of one and two-link manipulators

Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system

PI/PID Controller Relay Experiment Auto-Tuning with Extended Kalman Filter and Second-Order Generalized Integrator as Parameter Estimators

This paper presents a method for the estimation of key parameters of limit cycle oscillations (amplitude and frequency) during a relay experiment used for automatic tuning of proportional-integral (PI) and proportional-integral-derivative (PID) feedback controllers. The limit cycle parameter estimator is based on the first-order extended Kalman filter (EKF) for resonance frequency estimation, to which a second-order generalized integrator (SOGI) is cascaded for the purpose of limit cycle amplitude estimation. Based on thus-obtained parameters of the limit cycle oscillations, the ultimate gain and the ultimate period of the limit cycle oscillations are estimated. These are subsequently used for the tuning of PI and PID controller according to Takahashi modifications of Ziegler-Nichols tuning rules. The proposed PI and PID controller auto-tuning method is verified by means of simulations and experimentally on the heat and air-flow experimental setup for the case of air temperature feedback control. The results have shown that the proposed auto-tuning system based on relay control experiment for the heat and air-flow process PI/PID temperature control can capture the ultimate gain and period parameters fairly quickly in simulations and in experiments. Subsequent controller tuning according to Takahashi modifications of Ziegler-Nichols rules using thus-obtained ultimate point parameters can provide favourable closed-loop load disturbance rejection, particularly in the case of PID controller