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...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.展开更多
针对国际水协(IWA)开发的基准仿真模型(Benchmark simulation model No.1,BSM1)中第5分区溶解氧质量分数和第2分区硝态氮质量分数的控制问题,提出了一种基于神经网络的多变量预测控制系统。控制系统中主要包括两部分:神经网络辨识器,用...针对国际水协(IWA)开发的基准仿真模型(Benchmark simulation model No.1,BSM1)中第5分区溶解氧质量分数和第2分区硝态氮质量分数的控制问题,提出了一种基于神经网络的多变量预测控制系统。控制系统中主要包括两部分:神经网络辨识器,用于提取对象的输出数据;神经网络控制器,用于输出控制变量。仿真结果表明:基于神经网络的预测控制系统具有较好的适应性和鲁棒性。展开更多
文摘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.
文摘针对国际水协(IWA)开发的基准仿真模型(Benchmark simulation model No.1,BSM1)中第5分区溶解氧质量分数和第2分区硝态氮质量分数的控制问题,提出了一种基于神经网络的多变量预测控制系统。控制系统中主要包括两部分:神经网络辨识器,用于提取对象的输出数据;神经网络控制器,用于输出控制变量。仿真结果表明:基于神经网络的预测控制系统具有较好的适应性和鲁棒性。
