Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Wind turbine control using PI pitch angle controller

This paper suggests two methods to calculate the gains of a proportional-Integral pitch angle controller for a 5 MW wind turbine. The first method is analytical and the second one is based on simulation. Firstly, the power coefficient characteristics for different pitch angles are calculated. Secondly, the output powers vs. rotor speed curves from cut-in to cut-out wind speeds are simulated. The results from first and second analyses used to find the control gains at different wind speeds. Finally, the results are compared using a wind turbine model to determinate turbine’s tracking characteristic

Model-based fault detection and isolation for wind turbine

In this paper, a quantitative model based method is proposed for early fault detection and diagnosis of wind turbines. The method is based on designing an observer using a model of the system. The observer innovation signal is monitored to detect faults. For application to the wind turbines, a first principles nonlinear model with pitch angle and torque controllers is developed for simulation and then a simplified state space version of the model is derived for design. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. A multiobjective optimization method is then employed to solve this dual problem. Simulation results are presented to demonstrate the performance of the proposed method

Multivariable predictive PID control for quadruple tank

In this paper multivariable predictive PID controller has been implemented on a multi-inputs multi-outputs control problem i.e., quadruple tank system, in comparison with a simple multiloop PI controller. One of the salient feature of this system is an adjustable transmission zero which can be adjust to operate in both minimum and non-minimum phase configuration, through the flow distribution to upper and lower tanks in quadruple tank system. Stability and performance analysis has also been carried out for this highly interactive two input two output system, both in minimum and non-minimum phases. Simulations of control system revealed that better performance are obtained in predictive PID design

Model-based controller design for a plastic film extrusion process

This paper reports the development and implementation of a model-based cross-directional controller for plastic film extrusion and other web-forming processes. The controller design has a similar structure to that of internal model control (IMC) with the addition of an observer whose gain is designed to minimise process and model mis-match. The observer gain is obtained by solving a multi-objective optimisation through the application of a genetic algorithm and simulation results are presented in this paper demonstrating improvements that can be achieved by the proposed controller over two existing CD controllers

Design and tuning of fractional-order PID controllers for time-delayed processes

Frequency domain based design methods are investigated for the design and tuning of fractional-order PID for scalar applications. Since Ziegler-Nichol’s tuning rule and other algorithms cannot be applied directly to tuning of fractional-order controllers, a new algorithm is developed to handle the tuning of these fractional-order PID controllers based on a single frequency point test just like Ziegler-Nichol’s rule for integer order PID controllers. Critical parameters of the system are obtained at the ultimate point and controller parameters are calculated from these critical measurements to meet design specifications. Thereafter, fractional order controller is obtained to meet a specified robustness criteria which is the phase-invariability against gain variations around the phase cross-over frequency. Results are simulated on a second –order plus dead time plant to demonstrate both performance and robustness

On fractional predictive PID controller design method

A new method of designing fractional-order predictive PID controller with similar features to model based predictive controllers (MPC) is considered. A general state space model of plant is assumed to be available and the model is augmented for prediction of future output. Thereafter, a structured cost function is defined which retains the design objective of fractional-order predictive PI controller. The resultant controller retains inherent benefits of model-based predictive control but with better performance. Simulations results are presented to show improved benefits of the proposed design method over dynamic matrix control (DMC) algorithm. One major contribution is that the new controller structure, which is a fractional-order predictive PI controller, retains combined benefits of conventional predictive control algorithm and robust features of fractional-order PID controller

Comparisons of nonlinear estimators for wastewater treatment plants

This paper deals with five existing nonlinear estimators (filters), which include Extended Kalman Filter (EKF), Extended H-infinity Filter (EHF), State Dependent Filter (SDF), State Dependent H-Infinity Filter (SDHF) and Unscented Kalman Filter (UKF) that are formulated and implemented to estimate unmeasured states of a typical biological wastewater system. The performance of these five estimators of different complexities, behaviour and advantages are demonstrated and compared via nonlinear simulations. This study shows promising application of UKF for monitoring and control of the process variables, which are not directly measurable

A control and monitoring oriented model of a film manufacturing process

This paper describes the development of a control and monitoring oriented model of a plastic film manufacturing process. The model is mainly derived from first-principles and has been implemented in the Matlab/Simulink dynamic simulation environment. The development of the model forms the first phase of a project that aims to develop a nonlinear sub-space based monitoring, fault detection and trouble shooting system for the film manufacturing process