Decoupling and tracking control of induction motor drive

This work introduces an adaptive observation system and a robust control system for achieving the favorable decoupling control and high-precision speed tracking property of an induction motor (IM) drive system. First, an adaptive observation system with an inverse rotor time-constant observer is derived on the basis of model reference adaptive system (MRAS) theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. The adaptive observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Moreover, a robust control system is developed based on the principle of computed torque control. In the robust control system, a grey uncertainty predictor is utilized to adapt the lumped uncertainty on line to relax the requirement of the lumped uncertainty in the design of a computed torque speed controller. In addition, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results.     

基于模糊补偿神经网络辨识器的发动机转速控制系统

提出了一种带模糊补偿的神经网络辨识器,并应用在某型涡扇发动机转速控制系统中。一个动态神经网络用于被控装置的在线辨识,然后根据被控装置的输出和参考模型的响应迭代出控制信号,具有4条简单规则的模糊逻辑块用于提高整个系统的闭环特性。试验结果显示,对比传统的机械-液压式控制器和模拟式电子控制器,提出的控制策略具有更好的瞬变特性及抗干扰特性,同时提高了系统的过渡过程品质,保证了航空发动机对高性能指标和高控制精度的要求。      

Variable structure and adaptive control of a synchronous generator

Damping of synchronous generator oscillations has been investigated by using three types of controllers, i.e., the PI (proportional-integral) controller, the model reference adaptive controller (MRAC) and the variable-structure model-following controller (VSMFC). It was found that the PI controller fails to accommodate itself to the situation where there is a significant change in generator loading conditions. The MRAC greatly improves the dynamic responses of the generator over a wide range of operating conditions by minimizing the error in the responses obtained from the plant with a specified reference model. The damping of the generator is further improved by the VSMFC, which reduces the sensitivity of generator responses relative to plant parameter variations by providing sliding-mode operations     

Predictor-based model reference adaptive roll and yaw control of a quad-tiltrotor UAV

An attempt is made to apply modern control technology to the roll and yaw control of a rudderless quad-tiltrotor Unmanned Aerial Vehicle (UAV) in the latter part of the flight mode transition, where aerodynamic forces on the tiltrotor’s wings start to take effect. A predictor-based adaptive roll and yaw controller is designed to compensate for system uncertainties and parameter changes. A dynamics model of the tiltrotor is built. A Radial-Basis Function (RBF) neural network and offline adaptation method are used to reduce flight controller workload and cope with the nonlinearities in the controls. Simulations are conducted to verify the reference model response tracking and yaw-roll control decoupling ability of the adaptive controller, as well as the validity of the offline adaptation method. Flight tests are conducted to confirm the ability of the adaptive controller to track different roll and yaw reference model responses. The decoupling of roll and yaw controls is also tested in flight via coordinated turn maneuvers with different rotor tilt angles.     

基于神经网络的无人直升机姿态控制系统设计

首先根据模型参考自适应控制理论,将模型逆与在线神经网络结合,设计了神经网络自适应姿态控制系统。接着叙述反馈线性化及模型逆理论,分析系统的模型跟踪误差动力特性,设计神经网络控制器及在线算法。然后以某无人直升机俯仰通道为例,对神经网络姿态控制系统进行仿真。结果表明该系统能够对未建模特性、参数不确定性等引起的模型逆误差进行自适应,而且在传感器输出中具有白噪声时仍然能够获得较好的响应特性。     

飞行/推进系统自适应神经网络综合控制仿真研究

 提出一种基于发动机喘振裕度自适应的飞行 /推进系统综合控制。在发动机喘振裕度较大的某些飞行条件或飞行包线内,通过调整喷口面积,使发动机喘振裕度保持在一个较小值,既保证发动机稳定工作,又增加发动机推力,从而改善飞机的性能。采用分散控制方案,综合控制系统由 5个控制子系统组成。各控制子系统的设计采用自适应控制和神经网络相结合的方法,所提出的参数和权重的自适应调整律保证系统的稳定性。全包线范围内飞机平飞加速和爬升数字仿真结果表明,该综合控制方法可缩短飞机的平飞加速时间和爬升时间。     

电液力伺服系统的神经网络并行自适应预测 PI 控制

考虑电液伺服系统的复杂非线性和不确定性特性,提出一类基于神经网络的并行自适应预测PI控制结构,该结构使控制参数的调整和系统的实时控制操作可并行进行,不仅做到了神经模型和控制器的在线辨识和设计,而且避免了神经网络方法通常存在的实时控制的困难,使复杂系统的在线学习控制成为可能。仿真结果表明该控制器具有良好的适应性和鲁棒性。      

基于滑模神经网络的自主飞艇姿态控制

针对自主飞艇飞行环境的不确定性,提出了一种基于自适应滑模神经网络的姿态控制系统.平流层高空飞行环境对飞艇控制产生了许多不确定性因素,利用自适应变结构控制和神经网络方法设计了飞艇的俯仰通道控制器.非线性仿真结果表明:控制器能够适应对象结构参数及外部扰动的大范围变化,满足姿态控制稳定性要求,同时也消除了变结构控制系统的抖振,具有良好的鲁棒性和动态性能.     

Robust variable structure speed control for induction motor drive

In order to eliminate the effect of parameter variation on field-oriented control for induction motor drive, an adaptation algorithm for tuning the rotor time-constant is proposed. Based on the adaptive observation of the rotor flux linkages, the rotor time-constant is adapted to obtain an exact indirect-field-oriented control (IFOC). In this proposed algorithm, a related function to the variation of the rotor time-constant is designed, then the accurate slip frequency needed for IFOC is obtained from this error function through a PI-type (proportional-integral) filter. Furthermore, a novel variable structure speed control with integral sliding surface is proposed under the adaptive field-oriented operation. By means of the variable structure speed control, the dynamics of motor speed have the property of an exponentially convergent rate. Using the proposed adaptive field-oriented control and the variable structure speed control, the IFOC is robust to the variation of the rotor time-constant and the speed control is insensitive to parameter uncertainty and load disturbance. Finally, some simulation and experimental results are presented to validate the effectiveness     

航空发动机神经网络自学习PID控制

将神经网络与传统的PID控制相结合,构成神经网络自学习PID控制,用神经网络在线整定PID控制器的比例、积分及微分三个参数,使被控对象跟踪理想参考模型的输出。该系统具有自学习能力,能适用于非线性、时变的被控对象。将神经网络自学习PID控制方法用于航空发动机全包线控制以及蜕化发动机的控制,进行了数字仿真,验证了该方法的有效性。     

RFNN control for PMLSM drive via backstepping technique

A robust fuzzy neural network (RFNN) control system is proposed in this study to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories. First, an ideal feedback linearization control law is designed based on the backstepping technique. Then, a fuzzy neural network (FNN) controller is designed to be the main tracking controller of the proposed RFNN control system to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. Moreover, to relax the requirement for the bound of uncertainty term, which comprises a minimum approximation error, optimal parameter vectors and higher order terms in Taylor series, an adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the simulated and experimental results due to periodic reference trajectories demonstrate that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.     

一种新型的内置式永磁同步电机无位置传感器控制策略

针对内置式永磁同步电机(IPMSM)在低速域运行时模型参考自适应观测器对电机参数变化敏感、位置检测精度低以及鲁棒性差等缺点,提出了通过利用电机本体的参考模型和相应可调模型的差值构造滑模面,取代了传统模型参考自适应观测器中采用PI调节器作为自适应机构的做法,并且采用模糊控制器自适应调整滑模增益以抑制滑模运动的抖振。在MATLAB/Simulink环境下搭建了仿真模型。仿真表明:在外部扰动以及电机参数变化时,估计转速和转子位置均能跟踪到实际的转速和转子位置。     

新一代歼击机超机动飞行的动态逆控制

 根据反馈线性化理论, 讨论了神经网络自适应非线性动态逆控制设计。首先根据时标分离原则, 采用动态逆方法设计快回路和慢回路控制器; 其次提出基于模型逆的神经网络非线性直接自适应控制方案, 设计一种在线神经网络用于补偿模型逆误差。仿真表明, 该控制方案具有较好的自适应能力和鲁棒性。     

Accommodating controller malfunctions through fault tolerant control architecture

In previous work we have proposed a supervised globalized dual heuristic programming (GDHP) controller as a solution to the fault tolerant control (FTC) problem of nonlinear plants subject to abrupt and incipient faults capable of drastically modifying the system dynamics to maintain stability and performance. The neural network (NN) based adaptive critic controller presented the best choice for the flexibility and power necessary to accomplish the task, however no success guarantees can be made for the online training of neural weights for the unrestricted fault recovery problem. Built on the existing framework, we propose a novel supervisory system capable of detecting controller malfunctions before the stability of the plant is compromised. Furthermore, due to its ability to discern between controller malfunctions and faults within the plant, the proposed supervisor acts in a specific fashion in the event of a controller malfunction to provide new avenues with a greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions from the faults in the plant itself is achieved through an advanced decision logic based on three independent quality indexes. Proof-of-the-concept simulations over a nonlinear plant demonstrate the validity of the approach.     

Quantitative speed control for SRM drive using fuzzy adapted inverse model

Quantitative and robust speed control for a switched reluctance motor (SRM) drive is considered to be rather difficult and challenging owing to its highly nonlinear dynamic behavior. A speed control scheme having two-degree-of-freedom (2DOF) structure is developed here to improve the speed dynamic response of an SRM drive. In the proposed control scheme, the feedback controller is quantitatively designed to meet the desired regulation control requirements first. Then a reference model and a command feedforward controller based on an inverse plant model are employed to yield the desired tracking response at nominal case. As the variations of system parameters and operating conditions occur, the prescribed control specifications may not be satisfied any more. To improve this, the inverse model is adaptively tuned by a fuzzy control scheme so that the model-following tracking error is significantly reduced. In addition, a simple disturbance cancellation robust controller is added to improve the tracking and regulation control performances further.     

三轴式无人旋翼飞行器及自适应飞行控制系统设计

设计了一种操控简便的三轴式无人旋翼飞行器,由三组共轴双旋翼组成,各旋翼由直流电机直接驱动,只需调节各电机转速就能控制旋翼飞行器运动姿态和轨迹。为使三轴式无人旋翼飞行器飞行控制系统设计得到有效验证,研究了旋翼飞行器的飞行动力学非线性建模,运用叶素动量理论建立了共轴双旋翼变转速旋翼载荷计算方法,分析了旋翼入流分布对共轴双旋翼气动载荷模型的影响,通过试验验证了共轴双旋翼气动载荷计算模型的正确性。由于旋翼飞行器飞行动力学模型的非线性及未建模动力学的影响,难于建立非常精确的数学模型,给飞行控制系统设计带来了挑战。本文根据旋翼飞行器飞行动力学非线性模型推导出了旋转动力学模型逆和平移动力学模型逆控制器,利用神经网络在线自适应修正模型逆误差,采用线性PD或PI控制器调节指令跟踪误差,应用由向心回转和垂直上升组合的机动科目进行了仿真验证,给出了具有外界阵风干扰模拟的仿真结果,表明所设计的飞行控制系统具有自适应性和鲁棒性,能实现精确的轨迹跟踪控制。     

基于径向基神经网络的空天飞行器鲁棒自适应轨迹线性化控制

基于轨迹线性化控制方法(TLC)以及径向基神经网络(RBFNN)技术研究了一种新的鲁棒自适应轨迹线性化控制方案并应用于空天飞行器(ASV)飞行控制系统设计中.首先基于被控对象的分析模型设计系统的TLC控制器,然后利用RBFNN对系统不确定的逼近能力,设计了鲁棒自适应控制器及参数的自适应调节律,并采用Lyapunov方法严格证明了闭环系统所有误差信号一致最终有界.最后应用新控制方案设计了ASV飞行控制系统,仿真结果表明了方法的有效性.      

直升机结构响应自适应控制研究

研究了基于自适应滤波技术、采用伺服惯性力发生器（SIFG）的直升机结构响应主动控制;比较了被动动力吸振器和SIFG的减振效果。研究表明,SIFG在直升机前飞速度和旋翼转速及前飞速度改变的情况下均能保持良好的减振效果。     

Fault tolerant control system design with explicit consideration of performance degradation

A new approach is proposed for active fault tolerant control systems (FTCS), which allows one to explicitly incorporate allowable system performance degradation in the event of partial actuator fault in the design process. The method is based on model-following and command input management techniques. The degradation in dynamic performance is accounted for through a degraded reference model. A novel method for,selecting such a model is also presented. The degradation in steady-state performance is dealt with using a command input adjustment technique. When a fault is detected by the fault detection and diagnosis (FDD) scheme, the reconfigurable controller is designed automatically using an eigenstructure assignment algorithm in an explicit model-following framework so that the dynamics of the closed-loop system follow that of the degraded reference model. In the mean time, the command input is also adjusted automatically to prevent the actuators from saturation. The proposed method has been evaluated using the lateral dynamics of an F-8 aircraft against actuator faults subject to constraints on the magnitude of actuator inputs. Very encouraging results have been obtained.     

基于自适应神经模糊推理算法的无人机电推进燃料电池供气系统性能优化

本文针对某无人机基于聚合物交换膜燃料电池和锂离子电池的混合动力电推进系统的应用,研究开发了一种基于自适应神经模糊推理系统的电源管理系统控制技术,以控制混合动力电力推进系统,同时优化燃料电池供气系统的性能。本文以所建立的某无人机混合电推进系统数学模型为研究对象,研究了燃料电池电流与燃料电池供气系统压缩机功率之间的关系,建立了燃料电池电流与最佳压缩机功率关系的参考模型。在参考模型的基础上,引入自适应控制器来优化燃料电池供气系统的性能。基于自适应神经模糊推理系统的控制器将压缩机的实际运行功率动态调整到参考模型中定义的最佳值。自适应控制器的在线学习和训练能力用来辨识燃料电池电流的非线性变化,并产生压缩机电机电压的控制信号,以优化燃料电池供气系统的性能。在Matlab 仿真环境中开发了质子交换膜燃料电池和锂离子混合动力电推进系统模型并对所设计的控制器进行了仿真分析,结果表明基于自适应神经模糊推理系统的控制器为燃料电池供气系统压缩机性能优化提供了一种新颖而全面的途径,使燃料电池供气系统获得最大净功率输出。将燃料电池系统的净功率输出与最佳压缩机功率和恒定压缩机功率进行了比较,结果表明优化的压缩机功率配置比恒定的压缩机功率配置节能2.62%。同时,燃料电池自适应神经模糊推理系统控制器优化了燃料电池供气系统的能量利用。