In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a...In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.展开更多
A decentralized PID neural network(PIDNN) control scheme was proposed to a quadrotor helicopter subjected to wind disturbance. First, the dynamic model that considered the effect of wind disturbance was established vi...A decentralized PID neural network(PIDNN) control scheme was proposed to a quadrotor helicopter subjected to wind disturbance. First, the dynamic model that considered the effect of wind disturbance was established via Newton-Euler formalism.For quadrotor helicopter flying at low altitude in actual situation, it was more susceptible to be influenced by the turbulent wind field.Therefore, the turbulent wind field was generated according to Dryden model and taken into consideration as the disturbance source of quadrotor helicopter. Then, a nested loop control approach was proposed for the stabilization and navigation problems of the quadrotor subjected to wind disturbance. A decentralized PIDNN controller was designed for the inner loop to stabilize the attitude angle. A conventional PID controller was used for the outer loop in order to generate the reference path to inner loop. Moreover, the connective weights of the PIDNN were trained on-line by error back-propagation method. Furthermore, the initial connective weights were identified according to the principle of PID control theory and the appropriate learning rate was selected by discrete Lyapunov theory in order to ensure the stability. Finally, the simulation results demonstrate that the controller can effectively resist external wind disturbances, and presents good stability, maneuverability and robustness.展开更多
针对液压弯辊控制系统的时变性、非线性和不确定性等特点,设计利用G A(遗传算法)优化的P I D神经网络(P I D N N)液压弯辊控制系统。P I D N N控制器不仅具有不依赖被控对象数学模型的优点,而且有很好的动态性能,结构简单易于设计。利用...针对液压弯辊控制系统的时变性、非线性和不确定性等特点,设计利用G A(遗传算法)优化的P I D神经网络(P I D N N)液压弯辊控制系统。P I D N N控制器不仅具有不依赖被控对象数学模型的优点,而且有很好的动态性能,结构简单易于设计。利用G A代替B P算法对P I D N N权值进行优化,克服了B P算法易陷于局部极小的不足。2种优化方法的仿真结果对比表明:G A-P I D N N控制器能够使液压弯辊力快速达到目标值,并且具有较强的抗干扰能力。展开更多
针对飞行模拟器人感系统的高度非线性和易受干扰性,提出一种基于PIDNN(Proportional Integral Differential Neural Network)的控制方案.首先对飞行模拟器人感系统的模型进行分析研究,对它所受到的外界干扰作理论分析,整理出系统的数学...针对飞行模拟器人感系统的高度非线性和易受干扰性,提出一种基于PIDNN(Proportional Integral Differential Neural Network)的控制方案.首先对飞行模拟器人感系统的模型进行分析研究,对它所受到的外界干扰作理论分析,整理出系统的数学模型,再利用PIDNN控制器优良的在线训练、学习和调整功能对该模型进行仿真控制.与传统PID(Propor-tional Integral Differential)控制器相比,PIDNN结构简单、自适应性强、收敛速度快、不会陷入局部极小.仿真结果表明:PIDNN控制系统响应速度快、稳态精度高、具有良好的动静态特性和鲁棒性,满足实时控制的要求.展开更多
文摘In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.
基金Project(2011ZA51001)supported by National Aerospace Science Foundation of China
文摘A decentralized PID neural network(PIDNN) control scheme was proposed to a quadrotor helicopter subjected to wind disturbance. First, the dynamic model that considered the effect of wind disturbance was established via Newton-Euler formalism.For quadrotor helicopter flying at low altitude in actual situation, it was more susceptible to be influenced by the turbulent wind field.Therefore, the turbulent wind field was generated according to Dryden model and taken into consideration as the disturbance source of quadrotor helicopter. Then, a nested loop control approach was proposed for the stabilization and navigation problems of the quadrotor subjected to wind disturbance. A decentralized PIDNN controller was designed for the inner loop to stabilize the attitude angle. A conventional PID controller was used for the outer loop in order to generate the reference path to inner loop. Moreover, the connective weights of the PIDNN were trained on-line by error back-propagation method. Furthermore, the initial connective weights were identified according to the principle of PID control theory and the appropriate learning rate was selected by discrete Lyapunov theory in order to ensure the stability. Finally, the simulation results demonstrate that the controller can effectively resist external wind disturbances, and presents good stability, maneuverability and robustness.
文摘针对液压弯辊控制系统的时变性、非线性和不确定性等特点,设计利用G A(遗传算法)优化的P I D神经网络(P I D N N)液压弯辊控制系统。P I D N N控制器不仅具有不依赖被控对象数学模型的优点,而且有很好的动态性能,结构简单易于设计。利用G A代替B P算法对P I D N N权值进行优化,克服了B P算法易陷于局部极小的不足。2种优化方法的仿真结果对比表明:G A-P I D N N控制器能够使液压弯辊力快速达到目标值,并且具有较强的抗干扰能力。
文摘针对飞行模拟器人感系统的高度非线性和易受干扰性,提出一种基于PIDNN(Proportional Integral Differential Neural Network)的控制方案.首先对飞行模拟器人感系统的模型进行分析研究,对它所受到的外界干扰作理论分析,整理出系统的数学模型,再利用PIDNN控制器优良的在线训练、学习和调整功能对该模型进行仿真控制.与传统PID(Propor-tional Integral Differential)控制器相比,PIDNN结构简单、自适应性强、收敛速度快、不会陷入局部极小.仿真结果表明:PIDNN控制系统响应速度快、稳态精度高、具有良好的动静态特性和鲁棒性,满足实时控制的要求.
