Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

In this paper, we consider the control of multivariable substructured systems with input constraints. Model Predictive Control (MPC) is used to synchronize the interface between the physical and numerical substructures. As a case study, a quasi-motorcycle suspension system is converted into a multivariable substructured system. An MPC controller is developed for this system. Simulation results ...

ABSTRACT: This paper proposes the Model Predictive Control (MPC) scheme in a bubble cap distillation column . Even though PID controllers are widely used for the control of nonlinear system, there is a need for optimizing and conservation of energy. Here, MPC scheme is designed and it is used for controlling the composition in distillation columns.The tuning of PID controller is done using Sund...

Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easi...

The control of multi-input multi-output (MIMO) systems is a common problem in practical control scenarios. However in the last two decades, of the advanced control schemes, only linear model predictive control (MPC) was widely used in industrial process control (Maciejowski, 2002). The fundamental common idea behind all MPC techniques is to rely on predictions of a plant model to compute the op...

Cyber-Physical Systems (CPS) are systems of collaborating computational elements controlling physical entities via communication. Such systems involve control processes of physical entities and computational processes. The control complexities originated from the physical dynamics and systematic constraints are difficult for traditional control approaches (e.g., PID control) to handle without a...

This paper determines the application of a model predictive control (MPC) technique to improve the behavior of the water network supply system, to maintain stable operation of the water flow rate, and reduce the operational cost by manipulating the pump speed. The MPC algorithm is one of the most common automatic control system that has got a wide spread application in process industry. MPC is ...

This thesis is concerned with the theoretical foundations of Robust Model Predictive Control and its application to tracking control problems. Its first part provides an introduction to MPC for constrained linear systems as well as a survey of different Robust MPC methodologies. The second part consists of a discussion of the recently developed Tube-Based Robust MPC framework and its extension ...

In this paper, Model Predictive Control of Quadruple tank process for centralized and decentralized method is proposed. Multi Input Multi Output (MIMO) processes are inherently more complex than Single Input Single Output (SISO) process because process interactions occur between controlled and manipulated variables. This problem can be solved using centralized and decentralized controllers. Mod...

In this paper, a ‘‘third generation’’ benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control ~MPC! scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction...