Multi-Scenario Model Predictive Control Based on Genetic Algorithms for Level Regulation of Open Water Systems under Ensemble Forecasts

Constrained Non-linear Model Based Predictive Control using Genetic Algorithms

In this paper we propose a new approach for constrained model based predictive control based on the use on non linear models as a prediction models for the plant under control.. A genetic algorithm based approach is then used in an MBPC structure to deal with the problem of optimisation which is non convex and thus difficult to solve. The efficiency of this approach had been demonstrated with s...

application of upfc based on svpwm for power quality improvement

در سالهای اخیر،اختلالات کیفیت توان مهمترین موضوع می باشد که محققان زیادی را برای پیدا کردن راه حلی برای حل آن علاقه مند ساخته است.امروزه کیفیت توان در سیستم قدرت برای مراکز صنعتی،تجاری وکاربردهای بیمارستانی مسئله مهمی می باشد.مشکل ولتاژمثل شرایط افت ولتاژواضافه جریان ناشی از اتصال کوتاه مدار یا وقوع خطا در سیستم بیشتر مورد توجه می باشد. برای مطالعه افت ولتاژ واضافه جریان،محققان زیادی کار کرده ...

Stochastic model predictive control of LPV systems via scenario optimization

A stochastic receding-horizon control approach for constrained Linear Parameter Varying discrete-time systems is proposed in this paper. It is assumed that the time-varying parameters have stochastic nature and that the system’s matrices are bounded but otherwise arbitrary nonlinear functions of these parameters. No specific assumption on the statistics of the parameters is required. By using a...

Neuro-predictive Control Design Based on Genetic Algorithms

Genetic algorithm based neuro-predictive controller design of a non-linear dynamic system is described. The genetic algorithm represents an optimisation procedure, where the cost function to be minimized comprises the closed-loop simulation of the control process and a selected performance index evaluation. Using this aproach the neural model of the process has been optimised from point of view...

Model Based Predictive Control for Linear Systems