model predictive controller

Load-frequency control in an isolated microgrid is very important.By turbulence the load, the frequency of the microgrid begins to oscillate which must be damped using the load-frequency control system (secondary control). In this paper, we have reduced the number of utilized controllers for the power supplies in the microgrid (less complexity) and we have incorporated the Model Predictive Controller (MPC), whose weight parameters are optimized using the novel Hybrid Craziness based Particle Swarm Optimization-Pattern Search (HCRPSO-PS) algorithm, for controlling the load-frequency in a two-area microgrid. The results of the proposed controller, in a number of different scenarios, are compared with other methods such as the PID controller optimized with the Social Spider Optimization (SSO) algorithm and the model predictive controller whose weight parameters are obtained using the Craziness based Particle Swarm Optimization (CRPSO) algorithm (to demonstrate the efficiency of the novel hybrid algorithm), considering load variations and the distribution of the sources in the microgrid. We have demonstrated the efficiency of the proposed method in terms of response speed, overshoot/undershoot reduction and control complexity(less complex). Simulations are performed using MATLAB software

In this paper, Model Predictive Controller has been used for controlling the hybrid power system, which contains grid connected photovoltaic and energy storage systems (battery) considering the economic issues like optimization of profit from selling energy to grid by way of selling energy to grid during the high cost of energy and with regard to technical issues like improved dynamic response and smoothing power output of photovoltaic system and also maximization the batteries life by way of minimization the cycle of the change and discharge is used and such for optimization the results of model Predictive Control, PSO algorithm is used to obtain the coefficient of the objective function suggested controller. In addition, the suggested controller adapts itself. Dynamically and automatically and yet is able to respond external requests like price signals or satisfy the constraints of the power system or request of operator. Finally the simulation results of system with the proposed controller and result of PID controller is presented and compared.

In this paper, a novel methodology is proposed to improve performance of the Networked Control System (NCS) in the face of random time-delays, using Model Predictive Controller (MPC) approach. For this purpose, a new state-feedback MPC structure is developed to cope with random network time-delays when the system is subjected to uncertainties with state and control constraints. The main idea is to reduce the disturbing effect of random network time-delays on regulatory performance of the NCS using a new robust formulation in MPC design. A terminal penalty constraint has been added to the finite horizon objective function to guarantee the stability of the system stability. Finally, applicability of the presented method is evaluated in a real pilot plant within a NCS configuration, being realized by an industrial Ethernet and Foundation Fieldbus technology. It is demonstrated that the proposed online methodology is effective to provide a better performance, having faster response, smaller overshoot and stronger robustness compared to the conventional MPC method with less aggressive control actions.

Supply chain management system (SCM) is a network of suppliers and manufacturers and warehouses and distributors and retailers, with large delay times. The control system aims at operating the supply chain at the optimal point despite the influence of demand changes. In this paper, an information coupled supply chain management system model based on “Beer Game Theory” was used and was developed to an supply chain management consist of supply, manufacture, warehouse, distribution, retailer units. Then a centralized constrained model predictive controller applied on that. Also a move suppression term added to cost function, which increased system robustness toward changes on demands.