Nonlinear Predictive Control Using Particle Swarm Optimization
Application to Power Systems
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It is well recognized that linear control methods are not always the optimum way to deal with typical nonlinear plants. Due to continual increment in the complexity of systems and tighter product specifications, the quality requirements from automatic control have increased. Recently, the available computing power has rose to fantastic levels as well. Consequently, computationally intensive control methods can now be applied to complex systems. Model Predictive Control (MPC) techniques were developed to obtain tighter control and were applied successfully to several industrial applications. In...
It is well recognized that linear control methods are not always the optimum way to deal with typical nonlinear plants. Due to continual increment in the complexity of systems and tighter product specifications, the quality requirements from automatic control have increased. Recently, the available computing power has rose to fantastic levels as well. Consequently, computationally intensive control methods can now be applied to complex systems. Model Predictive Control (MPC) techniques were developed to obtain tighter control and were applied successfully to several industrial applications. In this book, a new implementation of MPC is proposed using Particle Swarm Optimization (PSO). The proposed method formulates the MPC as an optimization problem and PSO is used to minimize it. This gives advantages like adaptability, possibility of varying control objectives, and enhanced capability of handling constraints. The proposed method is applied to an area of industrial systems that has been relatively unexplored by MPC, i.e. power systems. Both SISO and MIMO nonlinear systems are considered. Three practical Power System problems are taken and the proposed technique is applied to them.
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