针对存在临界点的A类被控对象及不存在临界点的B类被控对象,分别采用其-180?和-120?相位点的频率和增益提出了PID (Proportional-integral-derivative)控制器参数的优化整定方法.基于Tchebyshev多项式和分数阶积分器求取被控对象-180?或...针对存在临界点的A类被控对象及不存在临界点的B类被控对象,分别采用其-180?和-120?相位点的频率和增益提出了PID (Proportional-integral-derivative)控制器参数的优化整定方法.基于Tchebyshev多项式和分数阶积分器求取被控对象-180?或-120?相位点的频率和增益,建立其积分滞后模型.采用负载扰动下跟踪误差平方和(Sum of squares of tracking errors, SSE)最小作为优化指标,使闭环系统具有强的鲁棒性的最大灵敏度和最大补灵敏度为约束方程,针对两类被控对象,分别建立了基于-180?和-120?相位点频率和增益的PID控制器比例、积分与微分三个参数的优化整定规则.通过与其他常用PID控制方法的仿真与物理对比实验,表明所提方法的优越性.展开更多
This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capaci...This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor （RLC） network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative （PID） with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic （LQ） in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.展开更多
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
文摘针对存在临界点的A类被控对象及不存在临界点的B类被控对象,分别采用其-180?和-120?相位点的频率和增益提出了PID (Proportional-integral-derivative)控制器参数的优化整定方法.基于Tchebyshev多项式和分数阶积分器求取被控对象-180?或-120?相位点的频率和增益,建立其积分滞后模型.采用负载扰动下跟踪误差平方和(Sum of squares of tracking errors, SSE)最小作为优化指标,使闭环系统具有强的鲁棒性的最大灵敏度和最大补灵敏度为约束方程,针对两类被控对象,分别建立了基于-180?和-120?相位点频率和增益的PID控制器比例、积分与微分三个参数的优化整定规则.通过与其他常用PID控制方法的仿真与物理对比实验,表明所提方法的优越性.
基金National Natural Science Foundation of China (60904066)National Basic Research Program of China (2010CB327904)"Weishi" Young Teachers Talent Cultivation Foundation of Beihang University (YWF-11-03-Q-013)
文摘This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor （RLC） network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative （PID） with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic （LQ） in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.
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.
