Offset-free fuzzy model predictive control of a boiler–turbine system based on genetic algorithm

This paper presents a model predictive control (MPC) strategy based on genetic algorithm to solve the boiler–turbine control problem. First, a Takagi–Sugeno (TS) fuzzy model based on gap values is established to approximate the behavior of the boiler–turbine system, then a specially designed genetic algorithm (GA) is employed to solve the resulting constrained MPC problem. A terminal cost is added into the standard performance index so that a short prediction horizon can be adopted to effectively decrease the on-line computational burden. Moreover, the GA is accelerated by improving the initial population based on the optimal control sequence obtained at the previous sampling period and a local fuzzy linear quadratic (LQ) controller. Simulation results on a boiler–turbine system illustrate that a satisfactory closed-loop performance with offset-free property can be achieved by using the proposed method.