动态不确定多输入多输出系统的变结构鲁棒控制

本文仅利用输入输出数据给出了一种动态不确定多输入多输出系统的变结构控制器设计机制，控制系统的设计中引入了一个严格正实的模型．应用适当逻辑切换的状态变量滤波器实现变结构控制．证明了所提方案能保证全局输出有界稳定并能实现滑模控制和渐近跟踪参考模型的输出．该设计方案对结构不确定性具有一定的鲁棒性．仿真结果表明了所提方案的有效性．     

动态不确定输入多输出系统的变结构鲁棒控制

本文仅利用输入输出数据给出了一种动态不确定多输入多输出系统的变结构控制器设计机制。控制系统的设计中引入了一个严格正实的模型，应用适当逻辑切换的状态变量滤波器实现变结构控制。证明了所提方案能保证全局输出有界稳定并能实现滑模控制和渐近跟踪参考模型的输出。该设计方案对结构不确定性具有一定的鲁棒性。仿真结果表明了所提方案的有效性。     

卫星控制系统离散积分滑模容错控制

卫星控制系统是采样控制系统,对它来讲,不确定性离散系统容错控制方法非常重要.本文提出了一种离散积分滑模容错控制方案,用于调节动量轮有故障的卫星姿态,其主要部分是设计控制器和控制分配方法.本文对含有匹配和不匹配的不确定性的离散多输入多输出系统,设计了一种离散积分滑模控制器,分析了匹配的和不匹配的不确定性对闭环系统稳定性的影响,得出了系统状态的界.应用可达集方法确定控制分配方案时,本文改变可达集表面的搜索顺序,提高了求解速度.以五动量轮卫星模型为例,将本文方法应用于含多故障、健康因子不准确、匹配不确定性和不匹配不确定性的卫星控制系统中,理论和仿真的结果一致.     

基于未知输入集员滤波器的不确定系统故障诊断

针对一类具有参数不确定性和未知扰动的线性系统,提出了一种新的执行器故障诊断方法.将指定执行器故障视为未知输入,利用全对称多胞形近似状态边界,本文设计了一种未知输入全对称多胞形集员滤波器,以估计测量输出的上下边界.在此基础上,提出了一种利用一组未知输入滤波器的故障检测与分离策略.通过一个飞行控制系统的数值仿真验证了所提出方法的有效性.     

非匹配不确定系统的显式反步变结构控制

研究非线性多输入多输出不确定系统在非匹配条件下的鲁棒输出跟踪问题.基于相对阶的假设,给出了多输入多输出不确定系统的标准型;进而结合反步设计方法和变结构控制方法,提出一种非匹配条件下非线性不确定系统鲁棒输出跟踪问题的显式反步变结构控制器设计方法.所给跟踪算法不仅只由不确定性的变化范围直接确定,而且有效地克服了现有反步设计算法因隐式递推关系和复杂偏导数计算给实时控制带来的困难.     

多变量系统控制器的设计简化成单输入单输出问题

言单输入单输出线性控制系统的设计,无论在理论和实践上都是很成熟的。本文介绍观测器—控制器的一种设计方法,利用这个方法可以使多输入多输出系统的设计问题简化为单输入单输出系统的情况来设计,在本文中有阐明这一方法之应用的数值例子。     

连续系统极点配置设计方法的研究

介绍了单输入单输出连续系统极点配置设计的几种方法.证明了多输入多输出系统可以转化为单输入单输出系统,这样多输入多输出系统极点配置可以参照单输入单输出系统的极点配置方法进行设计.     

MIMO网络控制系统的控制器设计

研究了长时延有界的情况下,多输入多输出网络控制系统发生传感器故障时的控制器设计问题。首先,利用容错控制的思想建立了多输入多输出网络控制系统发生传感器故障时的模型。在此基础上,借助切换系统的理论,分析了闭环网络控制系统的稳定性,用线性矩阵不等式（LMI）形式给出了多输入多输出网络控制系统渐近稳定的充分条件和控制器的设计形式。仿真结果表明,该方法可以对多输入多输出网络控制系统中传感器故障问题进行有效的容错控制。     

基于多环QFT的高超声速飞行器鲁棒控制

阐述了定量反馈理论(QFT)的基本原理和设计方法,针对超燃冲压发动机不同工作状态时高超声速飞行器不确定性模型,应用多环QFT设计了高超声速飞行器纵向飞行控制系统;仿真结果表明,运用QFT方法设计的控制系统不仅具有良好的跟踪性能和抗干扰性能,而且能够很好地解决飞行控制系统由于模型参数具有不确定性而造成的控制系统鲁棒性设计问题,并从工程应用角度为高超飞行器纵向飞行控制系统提供了一种鲁棒控制设计方案。     

自主平行泊车系统的二型模糊控制器设计

针对颠簸路面上自主平行泊车系统中存在的系统不确定性问题,提出了一种基于二型T-S模糊模型的控制系统设计方案,设计过程采用了并行分布式补偿法,并利用李雅普诺夫方法分析了系统的稳定性.该方案应用二型模糊系统在处理多重不确定性问题方面的优势,同时解决了由系统的非线性特征以及路面颠簸状况引起的系统不确定性,即在对系统进行分段线性化以建立模糊规则的同时利用二型模糊隶属函数对由路面颠簸状况引起的系统参数的不确定性进行描绘,并在模糊降型以及解模糊的过程中进行模糊融合以达到使系统输出精确化的目的.实例仿真表明,二型T-S模糊控制系统可以同时掌控由系统的非线性特征以及路面颠簸状况引起的不确定性,仿真结果验证了该控制器的稳定性和有效性.     

Adaptive tracking control of uncertain MIMO nonlinear systems with time-varying delays and unmodeled dynamics

In this paper, adaptive neural tracking control is proposed based on radial basis function neural networks (RBFNNs) for a class of multi-input multi-output (MIMO) nonlinear systems with completely unknown control directions, unknown dynamic disturbances, unmodeled dynamics, and uncertainties with time-varying delay. Using the Nussbaum function properties, the unknown control directions are dealt with. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown upper bound functions of the time-varying delay uncertainties are compensated. The proposed control scheme does not need to calculate the integral of the delayed state functions. Using Young s inequality and RBFNNs, the assumption of unmodeled dynamics is relaxed. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded.     

Robust tracking design based on adaptive fuzzy control of uncertain nonlinear MIMO systems with time delayed states

It is proposed here to use a robust tracking design based on adaptive fuzzy control technique to control a class of multi-input-multi-output (MIMO) nonlinear systems with time delayed uncertainty in which each uncertainty is assumed to be bounded by an unknown gain. This technique will overcome modeling inaccuracies, such as drag and friction losses, effect of time delayed uncertainty, as well as parameter uncertainties. The proposed control law is based on indirect adaptive fuzzy control. A fuzzy model is used to approximate the dynamics of the nonlinear MIMO system; then, two on-line estimation schemes are developed to overcome the nonlinearities and identify the gains of the delayed state uncertainties, simultaneously. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear system functions with an online update law. The adaptive fuzzy scheme uses a Variable Structure (VS) scheme to resolve the system uncertainties, time delayed uncertainty and the external disturbances such that H_∞ tracking performance is achieved. The control laws are derived based on a Lyapunov criterion and the Riccati-inequality such that all states of the system are uniformly ultimately bounded (UUB). Therefore, the effect can be reduced to any prescribed level to achieve H _∞ tracking performance. A two-connected inverted pendulums system on carts and a two-degree-of-freedom mass-spring-damper system are used to validate the performance of the proposed fuzzy technique for the control of MIMO nonlinear systems.     

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.     

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA).With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.     

控制增益符号未知的MIMO时滞系统自适应控制

针对一类带有死区模型并具有未知函数控制增益的不确定MIMO非线性时滞系统,基于滑模控制原理和Nussbaum函数的性质,提出了一种稳定的自适应神经网络控制方案.该方案放宽了对函数控制增益上界为未知常数的假设,并通过使用Lyapunov-Krasovskii泛函抵消了因未知时变时滞带来的系统不确定性.理论分析证明,闭环系统是半全局一致终结有界.仿真结果表明了该方法的有效性.     

Improved fuzzy sliding mode control for a class of MIMO nonlinear uncertain and perturbed systems

In this paper, a stable adaptive fuzzy sliding mode based tracking control is developed for a class of nonlinear MIMO systems that are represented by input output models involving system uncertainties and external disturbances. The main contribution of the proposed method is that the structure of the controller system is partially unknown and does not require the bounds of uncertainties and disturbance to be known. First, a fuzzy logic system is designed to estimate the unknown function. Secondly, in order to eliminate the chattering phenomenon brought by the conventional variable structure control, the signum function is replaced by an adaptive Proportional Derivative (PD) term in the proposed approach. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis, so that convergence to zero of tracking errors and boudedness of all signals in the closed-loop system can be guaranteed. Finally, a mass-spring-damper system is simulated to demonstrate the validity and the effectiveness of the proposed controller.     

Robust adaptive tracking control of MIMO nonlinear systems in the presence of actuator hysteresis

Adaptive tracking control of a class of MIMO nonlinear system preceded by unknown hysteresis is investigated. Based on dynamic surface control, an adaptive robust control law is developed and compensators are designed to mitigate the influences of both the unknown bounded external uncertainties and the unknown Prandtl–Islinskii hysteresis. By adopting the low-pass filters, the explosion of complexity caused by tedious computation of the time derivatives of the virtual control laws is overcome. With the proposed control scheme, the closed-loop system is proved to be semi-globally ultimately bounded by the Lyapunov stability theory, and the output of the controlled system can track the desired trajectories with an arbitrarily small error. Finally, numerical simulations are given to verify the effectiveness of the proposed approach.     

状态与输入受限的二阶多输入多输出非线性系统自适应控制

针对含有状态和输入受限的二阶多输入多输出非线性系统的控制问题,提出了一种自适应控制策略.通过综合利用障碍Lyapunov函数和动态面控制方法的特性,使得系统的状态满足约束条件而且能够减少计算量.此外,为了处理输入约束和系统中的不确定性的影响,分别设计了辅助系统和自适应算法.通过理论分析表明,闭环系统的所有状态都是有界的,而且系统的状态和输入都满足约束条件.最后,通过一个数值仿真算例和一个实际的航天器姿态控制系统的仿真来验证所提出的自适应控制策略的有效性.     

Robust stability of MIMO nonlinear uncertain systems using operator-based right coprime factorisation

In this paper, a class of multi-input–multi-output (MIMO) nonlinear systems with uncertainties is considered by using operator-based right coprime factorisation. First, based on proposed quotient operators, coupling effect of the MIMO nonlinear systems is discussed. Second, based on the operator-based right coprime factorisation approach, sufficient conditions for guaranteeing robust stability of the MIMO nonlinear systems with uncertainties are proposed by using a new unimodular operator. Finally, a simulation example is shown to illustrate the proposed design scheme for the MIMO nonlinear systems with uncertainties.     

Output feedback control of a class of discrete MIMO nonlinear systems with triangular form inputs

In this paper, adaptive neural network (NN) control is investigated for a class of discrete-time multi-input-multi-output (MIMO) nonlinear systems with triangular form inputs. Each subsystem of the MIMO system is in strict feedback form. First, through two phases of coordinate transformation, the MIMO system is transformed into input-output representation with the triangular form input structure unchanged. By using high-order neural networks (HONNs) as the emulators of the desired controls, effective output feedback adaptive control is developed using backstepping. The closed-loop system is proved to be semiglobally uniformly ultimate bounded (SGUUB) by using Lyapunov method. The output tracking errors are guaranteed to converge into a compact set whose size is adjustable, and all the other signals in the closed-loop system are proved to be bounded. Simulation results show the effectiveness of the proposed control scheme.