Neural controllers for systems with unknown dynamics

This study presents a methodology for specifying a neural controller for a system about which no a priori model information is available. The neural design presumes that a finite duration input/output (I/O) histogram on the system is available. The design procedure extracts from the histogram sufficient information to specify the neural feedback controller. The resultant controller will drive the system along a general output reference profile (unknown during the design). The resultant controller also exhibits the capability of disturbance rejection and the capacity to stabilize unstable plants     

基于专家控制器的电动汽车感应电机弱磁区优化控制研究

由于感应电机驱动系统采用数字控制器和脉宽调制输出会伴随着数字延迟的问题,加之参数可能存在的扰动,使得传统的间接磁场定向控制方法在感应电机高速弱磁区的控制性能降低。针对此问题,提出了一种基于专家控制器和模糊推理机制的感应电机弱磁区优化控制策略。考虑到传统间接磁场定向控制中电流调节器在弱磁区若没有获得适合的电流参考指令,则可能会产生高频振荡乃至失稳。因此,在传统方法的基础上将转速闭环输出的电流参考先送入到专家控制器,专家控制器基于数据库和模糊推理,对电流参考进行修正,其中模糊推理机制基于简单的高斯函数逻辑实现。最后,构建了感应电机驱动试验平台,开展了电机在弱磁区的高速驱动试验,试验结果验证了新型控制策略的有效性。     

挠性航天器的退步直接自适应姿态跟踪控制

针对参数不确定的挠性航天器姿态跟踪控制问题,提出了一种退步直接自适应控制算法。首先验证了挠性航天器动力学子系统的近似严格正实性,并设计了具有理想控制性能的参考模型;然后对以姿态四元数描述的运动学子系统设计常系数输出反馈中间控制律,使航天器姿态四元数输出渐近跟踪参考模型输出;最后退一步,对具有参数不确定特性的动力学子系统,基于非线性直接自适应控制理论和Lyapunov稳定性理论,设计了退步直接自适应姿态跟踪控制器,并证明了闭环系统的稳定性。仿真结果表明,所提控制方法能有效抑制挠性附件的振动,对挠性航天器的控制是有效的。     

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。     

航空发动机主供油计量活门故障主动容错控制器设计

针对航空发动机主供油计量活门执行机构发生故障时,系统的输出将会发生跳变,不能很好地跟踪系统期望输出的现象,采用模型参考自适应方法设计了主动容错控制器来解决这一问题.首先针对执行机构故障问题进行了数学描述.其次,选取系统的标称模型为参考模型,以输出误差最小化为优化目标,基于李雅普诺夫稳定性原理,寻找合适的自适应调节律,设计了自适应容错控制器.最后,针对发动机某一工况点进行了不同故障情况下的数值仿真.仿真结果表明:在主供油计量活门发生跳变故障和卡死故障时,自适应容错控制器可以通过参数自动调节,使得系统的输出能够很好地跟踪模型的输出,跟踪误差最终逼近于零.      

Design and implementation for a digital synchronous reluctancedrive

A systematic controller design for a synchronous reluctance drive system is presented. This controller consists of two parts: a forward-loop H^∞ controller to improve the transient response, and a load compensator to reduce the load disturbance. Based on a simplified model of the drive system, a control algorithm has been derived. Detailed analysis of the characteristics of the closed-loop system is presented. The effects of the parameter variations are also studied. A digital signal processor, TMS-320-C30, is used to implement the control algorithm. Both the speed control and the position control of the drive system can be implemented by using the proposed control method. Furthermore, all the control loops are executed by the digital signal processor. The system, as a result, is very flexible. The whole drive system performs well although its hardware is very simple. For speed control, the system can be operated at a speed as low as 1 r/min. For position control, the system can accurately control a one-axis table. In addition, the system also has good position tracking ability. Several experimental waveforms validate the simulated results     

Output Power Controller for a Wind-Driven Induction Generator

A constant stator power output controller for a wind-driven grid-connected induction generator is described. The load torque of the generator is controlled by varying the rotor resistance electronically. A dynamic model for the wind energy conversion system is proposed for both controlled and uncontrolled operation.The model is then used to predict the changes in shaft speed,turbine torque, and stator power output in response to change in wind speed. Data pertaining to a 60 kW commercial wind turbine generator is used in this investigation. Results based on analog computer simulation demonstrate the feasibility of the controller for scheduling a desired power output from the induction generator.Results of the tests on a laboratory induction generator driven by amicrocomputer-controlled dc motor drive simulating the characteristics of the wind turbine are included.     

应用保护映射理论的高超声速飞行器自适应控制律设计

针对高超声速飞行器包线范围广、参数变化大的控制需求,应用保护映射理论提出一种高超声速飞行器的自适应控制律设计方法。首先建立整个飞行包线内的线性变参数(LPV)模型,在参数变化边界点设计一个初始的控制结构和参数,然后基于保护映射理论分析初始控制结构使闭环系统稳定的参数范围,通过迭代自动获取整个包线内满足性能指标的控制参数,进而通过多项式拟合设计出高超声速飞行器自适应控制律。所提出的方法能够根据初始控制结构自动寻找一系列满足性能要求的控制器参数,并确定这些控制参数满足闭环系统稳定的设计范围。仿真结果表明,所设计的自适应控制律能够确保高超声速飞行器大包线的设计要求,实现闭环系统的鲁棒稳定。     

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.     

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.     

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.     

Induction motor servo drive with adaptive rotor time-constantestimation

An indirect filed-oriented induction motor (IM) position servo drive with adaptive rotor time-constant estimation and an on-line trained neural network controller is presented. First, the rotor time-constant is estimated real-time on the basis of the model reference adaptive system (MRAS) theory. Next, a linear model-following controller (LMFC) is designed according to the estimated plant model to allow the state responses of the plant to follow the reference model. Then an augmented signal generated from the proposed neural network controller, whose connective weights are trained on-line according to the model-following error of the states, is added to the LMFC system to preserve a favorable model-following characteristic under various operating conditions     

新的参考模型自适应控制方法在航空发动机中的应用

本文针对航空发动机模型的结构和参数不能明确已知,提出一种阶次低、鲁棒性较好的参考模型自适应控制法,即只需知道系统的相对阶次,且控制器结构简单,易于实现,适应性强。      

Study on the Behavior of Solar Array Deployment with Root Hinge Drive Assembly

In this paper, a method of using a root hinge drive assembly (RHDA) to control the solar array deployment is provided and a multi-DOF mechanism dynamic model of the system is established. In this way, the root hinge torque can be calculated iteratively. Then taking the predicted torque as a reference, a RHDA is designed for a large multiple-stage packaging and deployable solar array system. The control effect of the drive assembly is validated by ground tests. The test results indicate that the solar arrays can be deployed smoothly, and the deployment velocities are restricted by the drive assembly as expected. During the tests, the RHDA output speed and output torque are obtained. In order to examine the impact force when the yoke is lock-up with a hard stop, dynamics simulations are performed according to the actual behavior. The simulation result indicates that the designed RHDA reduces the impact force significantly and improves the lock-up reliability effectively.     

Attitude Control Considering Variable Input Saturation Limit for a Spacecraft Equipped with Flywheels

A new attitude controller is proposed for spacecraft whose actuator has variable input saturation limit. There are three identical flywheels orthogonally mounted on board. Each rotor is driven by a brushless DC motor (BLDCM). Models of spacecraft attitude dynamics and flywheel rotor driving motor electromechanics are discussed in detail. The controller design is similar to saturation limit linear assignment. An auxiliary parameter and a boundary coefficient are imported into the controller to guarantee system stability and improve control performance. A time-varying and state-dependent flywheel output torque saturation limit model is established. Stability of the closed-loop control system and asymptotic convergence of system states are proved via Lyapunov methods and LaSalle invariance principle. Boundedness of the auxiliary parameter ensures that the control objective can be achieved, while the boundary parameter’s value makes a balance between system control performance and flywheel utilization efficiency. Compared with existing controllers, the newly developed controller with variable torque saturation limit can bring smoother control and faster system response. Numerical simulations validate the effectiveness of the controller.     

基于干扰观测器的高超声速飞行器预测控制器设计简

 针对具有强耦合特性与模型不确定性特点的高超声飞行器控制问题,提出一种新型的姿态预测控制器设计方法。引入参考模型,建立了飞行器姿态预测控制模型。基于此,利用预测理论设计了飞行器的预测控制器,同时设计了干扰观测器实时观测外界未知干扰来进行补偿控制,从而实现滚动优化的目的;基于干扰观测值与真值的误差,利用Lyapunov稳定性理论,确定了控制精度与预测步长大小的关系;最后,在参数标称与拉偏的情形下进行了高超声速飞行器姿态控制系统仿真,仿真结果表明,干扰观测器能快速跟踪干扰,并且所设计的预测步长可以满足飞行器高精度的控制要求。     

基于干扰观测器的高超声速飞行器预测控制器设计

针对具有强耦合特性与模型不确定性特点的高超声飞行器控制问题,提出一种新型的姿态预测控制器设计方法。引入参考模型,建立了飞行器姿态预测控制模型。基于此,利用预测理论设计了飞行器的预测控制器,同时设计了干扰观测器实时观测外界未知干扰来进行补偿控制,从而实现滚动优化的目的;基于干扰观测值与真值的误差,利用Lyapunov稳定性理论,确定了控制精度与预测步长大小的关系;最后,在参数标称与拉偏的情形下进行了高超声速飞行器姿态控制系统仿真,仿真结果表明,干扰观测器能快速跟踪干扰,并且所设计的预测步长可以满足飞行器高精度的控制要求。     

涡轴发动机非线性模型预测控制

 对涡轴发动机进行了非线性模型预测控制(NMPC)研究,设计了非线性模型预测,该控制器主要包括3个方面：预测模型、滚动优化和反馈校正。利用神经元网络模型预测涡轴发动机动态响应过程,得到预测模型;运用序列二次规划(SQP)优化算法进行发动机的在线滚动优化,得到发动机的燃油控制量;根据神经元网络模型与实际发动机对象的输出误差,对控制器的指令信号进行了反馈校正。最后进行了仿真实验,与常规串级PID控制相比较,非线性模型预测控制器的超调量从2.2%降低到0.8%,响应时间从6 s降低到2 s,具有很好的控制品质。     

Robust image-based coordinated control for spacecraft formation flying

This paper addresses a coordinated control problem for Spacecraft Formation Flying (SFF). The distributed followers are required to track and synchronize with the leader spacecraft. By using the feature points in the two-dimensional image space, an integrated 6-degree-of-freedom dynamic model is formulated for spacecraft relative motion. Without sophisticated three-dimensional reconstruction, image features are directly utilized for the controller design. The proposed image-based controller can drive the follower spacecraft in the desired configuration with respect to the leader when the real-time captured images match their reference counterparts. To improve the precision of the formation configuration, the proposed controller employs a coordinated term to reduce the relative distance errors between followers. The uncertainties in the system dynamics are handled by integrating the adaptive technique into the controller, which increases the robustness of the SFF system. The closed-loop system stability is analyzed using the Lyapunov method and algebraic graph theory. A numerical simulation for a given SFF scenario is performed to evaluate the performance of the controller.