参数不确定高超声速飞行器自适应模糊H_∞控制

针对具有参数不确定性特点的高超声速飞行器输出跟踪问题,提出了一种自适应模糊H∞控制器设计方法。考虑系统存在的参数不确定性,利用自适应模糊系统在线逼近动态逆控制器中的非线性项,同时引入鲁棒补偿项,减轻模糊系统逼近误差和系统外部干扰对控制系统稳定性造成的影响,提高控制器的H∞性能。利用Lyapunov理论对整个系统的稳定性进行证明。对比仿真结果表明该方法能够保证高超声速飞行器具有良好的跟踪性能和很强的鲁棒性。     

飞翼无人机的一种鲁棒自适应控制律设计方法

针对飞翼无人机纵向全包线飞行时非线性特性明显和操纵效率变化显著的问题, 采用鲁棒伺服LQR(RSLQR)与L1自适应相结合的综合自适应控制方法(RSLQR-L1), 以C*(加速度、角速率)为被控变量, 设计了飞翼无人机纵向飞行控制系统。结合无人机实际飞行控制品质需求, 采用RSLQR方法, 设计无人机纵向主控制器;在RSLQR控制器的基本结构上扩展设计L1自适应输出反馈补偿控制器。在系统阐述RSLQR-L1综合自适应控制原理和设计方法的基础上, 通过数值仿真验证了控制结构的先进性和鲁棒性, 满足了飞翼无人机的控制要求。     

倒立摆系统的鲁棒H_∞加权混合控制

为了建立控制理论教学及开展各种控制实验的理想实验平台,研究了存在结构参数不确定性和具有干扰信号的倒立摆系统。首先将该系统分解为摆杆控制系统和小车控制系统。摆杆控制系统采用T S模糊模型来描述,利用系统的不确定性把系统表示成不确定系统的形式,采用鲁棒H_∞控制策略及LMI优化算法解算出反馈值,并设计出全局渐进稳定的模糊模型;小车控制系统则采用对位置误差和小车速度进行模糊化计算的方法,再利用模糊控制器进行处理计算,并最终得出控制量。最后再对两个系统进行加权混合控制。对倒立摆系统进行外加干扰信号、给定平移指令以及参数摄动等实验时,系统均可以在0.4 s的时间内取得良好的控制效果。实验结果表明:提出的加权控制方法具有较强的鲁棒稳定性和良好的抗干扰性能,验证了该方法的有效性。     

LMI方法非脆弱鲁棒H∞控制器设计

研究了控制器增益在加法式摄动下的非脆弱鲁棒H∞控制问题,基于线性矩阵不等式(LMI)理论,提出了非脆弱鲁棒H∞控制器存在的充分条件,将具有控制器增益不确定性的非脆弱鲁棒控制器设计问题,化为具有线性矩阵不等式约束和线性目标函数的凸优化问题求解,得到了一种非脆弱鲁棒H∞状态反馈控制器的简化设计方法.所设计的控制器在容许的增益摄动下,仍能保证闭环系统的稳定性和性能要求,系统不仅具有鲁棒性而且是非脆弱的,从而使系统具有更高的可靠性.仿真结果表明了设计方法的有效性.     

H∞回路成形直升机双回路控制系统设计

根据直升机飞行控制系统的内外回路设计概念,利用H∞回路成形方法设计了直升机双回路控制系统。首先采用H∞回路成形方法设计姿态命令/姿态保持控制器,使得直升机内回路具有良好的带宽和响应时间,且保持通道的解耦和鲁棒性;采用H∞回路成形方法构建直升机的速度回路作为外回路,实现对直升机的前飞速度和横向速度的控制,保证速度通道上具有良好的跟踪性能和鲁棒性能。并按照ADS-33E对所设计的控制系统的操纵品质做出了评价。仿真结果显示了良好的控制效果,验证了该设计方法的有效性。     

基于滚转角速率的无人机横侧向控制律设计

针对某小型样例无人机横侧向转动惯量小、副翼效率高等特点,设计了以滚转角速率为内回路的滚转角、航迹角和航迹跟踪控制律。根据各控制指标与性能加权矩阵Q、控制加权矩阵R的关系,确定了Q阵和R阵,应用鲁棒伺服LQR优化方法,给出了滚转角控制律参数。与常规的滚转角控制器比较表明,以滚转角速率为主控变量的控制器抗干扰能力强,满足样例无人机横侧向控制的要求。     

DI/QFT控制器在四旋翼无人直升机飞行控制中的应用

首先建立了四旋翼无人机的非线性数学模型.然后针对该无人机数学模型的不确定性和非线性,采用动态逆(DI)和定量反馈理论(QFT)相结合的方法设计了该无人机姿态回路的鲁棒控制器.应用动态逆方法处理对象的非线性,将系统等效为一个解耦但存在不确定性的线性对象.鉴于动态逆控制在气动参数摄动的情况下不能满足控制要求的事实,设计了QFT控制器,QFT控制器能克服对象的参数不确定性,保障系统的鲁棒性.仿真结果表明,在气动数据变化±20%的范围内,DI/QFT控制器实现了对姿态角的精确控制.     

不确定系统的鲁棒容错H∞控制

针对不确定线性连续系统,研究了执行器失效情况下的鲁棒容错H∞控制问题.利用LMI,给出了不确定线性系统对任意执行器故障均保持渐近稳定且满足给定干扰衰减指标的鲁棒容错H∞控制器存在的充要条件,讨论了参数不确定线性系统的鲁棒容错H∞控制器的设计问题.根据凸优化理论,进一步给出了鲁棒容错最优H∞控制器的线性凸优化设计算法和设计步骤.采用所设计的状态反馈控制器,当任意执行器出现故障时,闭环系统仍保持渐近稳定且满足给定的干扰衰减性能指标.数值仿真例子验证了该设计方法的可行性和有效性.     

基于遗传算法的模糊神经网络控制技术研究

提出了一种基于去模糊优化的模糊神经网络控制器及模糊神经网络的遗传学习算法.利用遗传算法优化包含控制器性能的指标来离线寻找最优的模糊神经网络控制器结构和参数,经过遗传算法训练的模糊神经网络控制器被接入模糊神经网络智能控制系统中.仿真结果表明,利用此方法实现的控制,系统的控制精度高,超调量小,鲁棒性能很强,获得了良好的控制效果.     

基于对策论的H2/H∞多目标鲁棒控制研究

为保证无人机在飞行过程中同时具有稳定鲁棒性和性能鲁棒性,必须采用H2/H∞混合控制方案。基于对策论的思想,把H2/H∞混合控制问题抽象为两个对局者信息不完全情况下的非零和博弈模型,利用纳什最大最小谈判解原理设计出求解H2/H∞混合控制的一般算法,得到H2/H∞非零和博弈模型的纳什均衡点。以某小型无人机纵向运动模型为研究对象,仿真结果表明通过纳什均衡点设计出的H2/H∞状态反馈控制器能够使系统在保持鲁棒稳定性的前提下获得最优化的动态性能指标,证明了这种思路的正确性和算法的有效性。     

传感器部分失效的网络控制系统H_∞容错控制

针对存在固定时滞和外部扰动的网络控制系统,建立了其传感器部分失效的控制输入数学模型。为了降低对于网络传输的要求,采用状态增广以克服时滞对网络控制系统分析的影响。通过构造适当的Lyapunov函数,并运用H∞控制理论和线性矩阵不等式方法(LMI),导出了网络控制系统H∞容错控制器存在的条件及具体方法。最后通过一个仿真算例证明了方法的可行性和有效性。     

基于模糊干扰观测器的无人机自主着陆逆控制器设计

针对具有显著非线性和不确定性的无人机自主着陆系统,提出基于模糊干扰观测器的非线性动态逆的控制方法,用于降低控制器对不确定性的要求。基于时标分离原则,将无人机自主着陆系统分为快回路、慢回路、非常慢回路和极慢回路,通过在快回路、慢回路和非常慢回路设计动态逆控制律使状态解耦,设计直线下滑和指数拉平的着陆轨迹,并在极慢回路进行跟踪。设计基于模糊系统的干扰观测器,以逼近外部干扰和内部不确定性等复合干扰,基于李雅普诺夫理论证明系统稳定性。最后给出了无人机自主着陆轨迹跟踪控制仿真,仿真结果表明设计控制器具有良好鲁棒性,完成无人机在外界干扰下的自主着陆控制。     

Fuzzy Sliding-Mode Control of Active Suspensions

In this paper, a robust fuzzy sliding-mode controller for active suspensions of a nonlinear half-car model is introduced. First, a nonchattering sliding-mode control is presented. Then, this control method is combined with a single-input–single-output fuzzy logic controller to improve its performance. The negative value of the ratio between the derivative of error and error is the input and the slope constant of the sliding surface of the nonchattering sliding-mode controller is the output of the fuzzy logic controller. Afterwards, a four-degree-of-freedom nonlinear half-car model, which allows wheel hops and includes a suspension system with nonlinear spring and piecewise linear damper with dry friction, is presented. The designed controllers are applied to this model in order to evaluate their performances. It has been shown that the designed controller does not cause any problem in suspension working limits. The robustness of the proposed controller is also investigated for different vehicle parameters. The results indicate the success of the proposed fuzzy sliding-mode controller.      

红外成像导引系统抗干扰性能评估研究

为解决红外成像导引系统抗干扰性能难于定量评估问题,给出了一种新的评估指标体系。首先通过详细分析红外光学系统、调制器、探测器、图像处理系统、陀螺伺服系统的性能参数,得到红外导引系统在抗干扰过程中的固有性能指标;然后规划了在不同仿真条件下的抗干扰仿真实验,得到抗干扰识别算法概率和各种干扰因素的数据;最后采用层次分析法建立了红外成像导引系统抗干扰性能计算模型,并用算例进行了计算,结果表明该指标体系具有一定的实用性。     

Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.     

PID神经网络在飞控解耦中的仿真研究

在进行飞行控制律设计时,为了获得好的控制品质,有必要对各输出量进行解耦,并且使控制器能根据被控对象动力学特性的改变而自适应调整。为此,介绍了两种PID神经网络解耦控制结构和一种神经网络辨识器,并分别给出了PID神经网络解耦控制器和辨识器的前向算法及反向传播算法;最后分别应用两种解耦方法针对飞机横侧向线性模型进行计算仿真,得到了两种解耦结果;证明了PID神经网络解耦控制的有效性,并比较了两种结构的优缺点。     

Modeling, control and experimental validation of a novel actuator based on shape memory alloys

The paper presents the development of a mechanical actuator using a shape memory alloy with a cooling system based on the thermoelectric effect (Seebeck–Peltier effect). Such a method has the advantage of reduced weight and requires a simpler control strategy as compared to other forced cooling systems. A complete mathematical model of the actuator was derived, and an experimental prototype was implemented. Several experiments are used to validate the model and to identify all parameters. A robust and nonlinear controller, based on sliding-mode theory, was derived and implemented. Experiments were used to evaluate the actuator closed-loop performance, stability, and robustness properties. The results showed that the proposed cooling system and controller are able to improve the dynamic response of the actuator.     

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.