Sazuan Nazrah Mohd Azam: Model based controller is one of the advanced control strategy that is currently common and extensively recognized in industry and academic, famously known as Model Predictive Control (MPC).
MPC is a controller that utilizes the identified model of a system to predict its future behaviour, given a prediction horizon. The main idea is to minimize the cost function and taking into account the constraints. Then the first controller moves is implemented at a sampling instants over the control horizon, by implementing only the first move the optimal feedback is achieved and then the complete sequence will be repeated again, which is known as moving horizon concept. Nowadays the applications of MPC are not limited to the process control field, but also including other various fields. This work described comprehensively an outline for MPC implementation for a linear system on a lab scale system in a simple and constructive method.
Throughout this work, the Modified Quadruple Tank System is utilized as an example to assimilate the fundamental theory of Model Predictive Controller to an exemplification of a multi-input-multi-ouput system, an illustration of the real-world complex system applications which is widely used for education in demonstrating advanced control strategies. It is a simple process that is non-linear but demonstrates complicated interactions between the manipulated and controlled variables. The system consists of four identical tanks and two pumping systems. Flows through the pumps can be controlled in order to achieve desired setpoints of water levels in these tank s in occurrence of some unknown measurement noise and stochastic disturbance. The thesis shows on the modeling part of the system, realization of a linear discrete-time state space model, state estimation using Kalman filter and finally demonstrates the application of MPC in a methodical mannered.