In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulatio...

This work addresses the problem of offset-free Model Predictive Control (MPC) when tracking an asymptotically constant reference. In the first part, compact and intuitive conditions for offset-free MPC control are introduced by using the arguments of the internal model principle. In the second part, we study the case where the number of measured variables is larger than the number of tracked va...

This work describes a modelling method for a non-linear system which is based on a multi-point linear approximation for a model predictive control (MPC) purpose. The method is derived from artificial neural network techniques and exploits good properties of a Orthogonal Activation Function based Neural Network (OAF-NN). In this work, we describe a technique of a Explicit-MPC (EMPC) which perfor...

This paper studies dependability of software implementation of DMC (Dynamic Matrix Control) and GPC (Generalised Predictive Control) Model Predictive Control (MPC) algorithms. Explicit formulation of algorithms is considered in which the control laws are calculated off-line. Dependability is evaluated usig software implemented fault injection approach. Tests are performed in the control system ...

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is diffic...

This paper presents a general formulation of the model predictive control ~MPC! scheme with special reference to acceleration feedback in structural control under earthquakes. The MPC scheme is based on a prediction model of the system response to obtain the control action by minimizing an objective function. Optimization objectives include minimization of the difference between the predicted a...

Traditional irrigation techniques usually cause the wasting of water resources. In addition, crops that are located in rural areas require pumps powered by environmentally unfriendly fossil fuels. This research proposes a smart system energized microgrid. The proposal includes two stages: first generates daily profile based on an expert for adequate use water. Then, considering profile, power r...

This work presents some preliminary results of a project aimed at the application of industrial Model Predictive Control (MPC) to thermal Power Plants. The rationale which motivates this research is the need to improve the efficiency of power plants to cope with the high levels of competitions induced by the liberalization of the energy market. A detailed plant simulator is coupled to an indust...

Safety has being a concern as result of the high rate road accidents and led to development driver assistance system such Active braking, Cruise Control, Lane departure warning lane keeping etc. become very active research area. However, this paper presents performance robustness analysis model predictive control proportional integral derivative for maneuvers an autonomous vehicle using compute...