This study developed new methods for controller design for SISO systems, integrating and unstable processes, cascade control systems and MIMO systems. This study also developed how to interface model predictive controller (advanced control system) with low level loops(e.g. PID loops).
First, the IMC-PID approach is generalized and how to obtain PID parameters for general process model is proposed. PID parameters are obtained for general process models by approximating the feedback form of an IMC controller with a Maclaurin series in the Laplace domain. These PID parameters yield closed-loop responses that are closer to the desired responses than those obtained by PID controllers tuned by other methods. The improvement in closed-loop control performance becomes more prominent as the dead time of the process model increases.
Second, a new design method for two degree of freedom controllers is proposed in this thesis. Such controllers are essential for unstable processes, and provide significantly improved dynamic performance over single degree of freedom controllers for stable processes when the disturbances enter through the process.
Third, a new method for PID controller tuning based on process models for integrating and unstable processes with time delay is proposed. The method consists of first finding the ideal controller that gives the desired closed loop response and then finding the PID approximation of the ideal controller by Maclaurin series. Closed-loop responses with the controllers tuned by the proposed method are compared with those tuned by the existing methods. The results show that the proposed tuning method is simpler to use and gives better closed loop performance than the existing methods.
Fourth, a new method for PID controller tuning based on process models for cascaded control systems is proposed. This method can be applied to any open loop stable processes. Furthermore, it enables us to tune the PID controllers both for the inner-loop ...