摘要
This paper discusses two industrial control applications using advanced control techniques. They are theoptimal-tuning nonlinear PID control of hydraulic systems and the neural predictive control of combustor acoustic ofgas turbines. For hydraulic control systems, an optimal PID controller with inverse of dead zone is introduced toovercome the dead zone and is designed to satisfy desired time-domain performance requirements. Using the adaptivemodel, an optimal-tuning PID control scheme is proposed to provide optimal PID parameters even in the case wherethe system dynamics is time variant. For combustor acoustic control of gas turbines, a neural predictive controlstrategy is presented, which consists of three parts: an output model, output predictor and feedback controller. Theoutput model of the combustor acoustic is established using neural networks to predict the output and overcome thetime delay of the system, which is often very large, compared with the sampling period. The output-feedback con-troller is introduced which uses the output of the predictor to suppress instability in the combustion process. The a-bove control strategies are implemented in the SIMULINK/dSPACE controller development environment. Theirperformance is evaluated on the industrial hydraulic test rig and the industrial combustor test rig.
This paper discusses two industrial control applications using advanced control techniques. They are the optimal-tuning nonlinear PID control of hydraulic systems and the neural predictive control of combustor acoustic of gas turbines. For hydraulic control systems, an optimal PID controller with inverse of dead zone is introduced to overcome the dead zone and is designed to satisfy desired time-domain performance requirements. Using the adaptive model, an optimal-tuning PID control scheme is proposed to provide optimal PID parameters even in the case where the system dynamics is time variant. For combustor acoustic control of gas turbines, a neural predictive control strategy is presented, which consists of three parts: an output model, output predictor and feedback controller. The output model of the combustor acoustic is established using neural networks to predict the output and overcome the time delay of the system, which is often very large, compared with the sampling period. The output-feedback controller is introduced which uses the output of the predictor to suppress instability in the combustion process. The a-bove control strategies are implemented in the SIMULINK/dSPACE controller development environment. Their performance is evaluated on the industrial hydraulic test rig and the industrial combustor test rig.
