一类非线性系统的加速度规划输出跟踪动态控制

针对一类严格反馈形式的非线性二阶多输入多输出系统,提出一种带有加速度规划的输出跟踪动态控制策略.引入一个代替时间变量的路径参数用以规划路径跟踪时的加速度,回避了设计内环加速度控制回路的常规方法,简化了控制器的设计过程.对二阶系统的控制项求导进行系统扩维,基于新的增广系统,设计了使系统输出收敛于期望路径的反馈线性化动态控制律.再对加速度跟踪误差基于梯度法设计更新律使其渐近收敛于零,最后通过调节期望加速度实现定常速度控制.理论分析表明,误差闭环系统一致渐近稳定,速度误差有界.动力定位船舶循迹控制仿真结果表明了所提出控制器的有效性.     

高阶不确定系统的线性串级自抗扰控制

自抗扰控制是我国著名学者韩京清原创的先进控制技术,本文针对自抗扰控制(ADRC)在高阶系统应用中控制器设计和参数整定问题,提出了串级自抗扰控制(CADRC). CADRC把高阶被控对象分解为含确定性部分和含总扰动的低阶部分的串联组合,采用由内环和外环组成的串级控制系统来完成控制.该CADRC方案的内环采用内模控制,外环采用经典ADRC.外环ADRC的被控对象是一个等效的低阶系统,可以采用带宽法进行整定,而内环的内模控制采用高阶低通滤波器进行回路成形设计和参数整定.仿真研究表明,所提出的方法是有效的,具有良好的工程应用前景.     

基于干扰抑制控制的飞行器姿态跟踪

本文针对飞行器姿态跟踪控制问题, 考虑系统的内部模型不确定性和外界扰动, 设计了使跟踪误差一致最终有界的控制器. 以四元数为姿态参数, 建立系统的非线性误差模型; 将控制系统分为内环观测器和外环控制器分别设计, 其中, 线性扩张状态观测器作为系统内环将实际系统补偿为标称模型, 而外环非线性控制器则用于镇定非线性标称系统. 最后, 利用Lyapunov理论得到了一致最终有界的稳定性结论, 并基于频域理论, 分析了线性扩张状态观测器阶次对系统性能的影响. 姿态跟踪实验表明, 本文设计的控制系统能够有效实现飞行器的姿态跟踪控制.     

基于线性化反馈的滑模变结构重装空投纵向控制律设计

针对重装空投过程中,重型货物持续移动及瞬间离机严重影响载机的安全性等问题,提出了基于线性化反馈和滑模变结构控制相结合的控制律设计方法,利用非线性多输入多输出反馈线性化完成系统解耦线性化,在此基础上采用滑模变结构控制设计系统内环速度与俯仰姿态跟踪控制器,保证了系统鲁棒性,结合外环PID高度保持控制器完成整个飞行控制系统的设计.最后,仿真验证了该控制器鲁棒性强,且满足空投任务战技指标要求.     

四旋翼飞行器多模式稳定控制

针对四旋翼飞行器的飞行稳定问题,提出了一种多模式的控制策略.在系统建模时引入未知扰动.系统采用内外环控制的方式,外环为位置控制环,采用自适应RBF神经网络对扰动进行预测,并采用非奇异终端滑模控制器实现对未知扰动的补偿和位置跟踪,有效提高了系统的鲁棒性;内环为姿态控制环,采用具备3种姿态角解算模式的超螺旋非奇异终端滑模控制器,使姿态角保持在安全区间内,使飞行器更加稳定和安全.通过李雅普诺夫方程法证明系统的稳定性,并证明了系统误差可有限时间收敛.最后,通过仿真实验验证了所提出控制策略的有效性.     

具有输入和输出约束的高阶随机系统神经网络控制

针对高阶非线性系统,开展自适应神经网络跟踪控制器设计,系统受到随机扰动的影响.首次把输入和输出约束问题引入到高阶系统的跟踪控制中,并假定系统动态是未知.首先借用高斯误差函数表达连续可微的非对称饱和模型以实现输入约束,和障碍Lyapunov函数保证系统输出受限;其次,针对高阶非线性系统,径向基函数(RBF)神经网络用来克服未知系统动态和随机扰动.在每一步的backstepping计算中,仅用到单一的自适应更新参数,从而克服了过参数问题;最后,基于Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明设计的控制器能保证所有闭环信号半全局最终一致有界,并能使跟踪误差收敛到零值小的邻域内.仿真研究进一步验证了提出方法的有效性.     

严格反馈非线性时变系统的迭代学习控制

针对一类含未知时变参数的严格反馈非线性系统, 提出一种实现有限作业区间轨迹跟踪控制的迭代学习算法. 基于Lyapunov-like方法设计控制器, 回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求. 以反推设计(Backstepping)方法设计控制器, 为使得虚拟控制项可导, 引入一级数收敛序列; 将时变参数展开为有限项多项式形式, 在控制器设计中采取双曲正切函数处理余项对于系统跟踪性能的影响. 理论分析表明, 闭环系统所有信号有界, 并能够实现系统输出完全收敛于理想轨迹.     

无线网络环境下数据驱动混合选别浓密过程双率控制方法

无线网络环境下赤铁矿混合选别浓密过程控制问题是以底流矿浆泵频率为内环输入,以底流矿浆流量为内环输出外环输入,以底流矿浆浓度为外环输出的非线性串级工业过程控制问题.其外环反馈回路存在丢包,且模型参数难以辨识,故本文利用工业运行过程的在线数据,设计不依赖模型参数的跟踪控制器.首先,利用浓密过程运行在工作点附近的特点进行线性化,对流量过程设计Q-学习控制器,保证流量过程能够跟踪给定的流量设定值;然后采用提升技术,得到统一时间尺度的以底流矿浆流量设定值为输入,以底流矿浆浓度为输出的被控对象;最后,考虑到在无线网络环境下浓度过程存在反馈丢包,当前的状态可能无法获得,故采用史密斯预估器的思想,利用历史的数据估计系统当前的状态,设计丢包Q-学习设定值控制器为流量过程提供最优设定值.通过仿真实验验证所提算法的有效性.     

积分加纯滞后系统的双预测PI控制及其应用

Smith预估控制器控制积分加大纯滞后过程时,鲁棒稳定性差,输出存在静态余差,无实际应用价值.提出了一类基于该过程的双预测PI控制器:具有内环和外环两种预测PI控制器.内环将系统稳定,外环消除输入干扰的影响和改善控制系统的动态性能.这种控制器结构简单,可调参数少,参数的调节方便、直观.仿真和实际应用表明:在干扰和模型失配的情况下,此类控制器仍然具有良好的控制性能和鲁棒稳定性能,是一种值得在实际工程中推广应用的新型控制器.     

运输机重装空投二阶终端滑模控制方法研究

针对重装空投过程中,货物的持续移动及瞬间出舱影响空投任务完成性、威胁飞行安全等问题,提出了一种二阶终端滑模纵向飞行控制方法;该方法利用非线性多输入多输出反馈线性化完成系统解耦线性化,在此基础上采用二阶终端滑模变结构控制设计系统内环速度与俯仰姿态跟踪控制器,保证了系统鲁棒性,结合外环PID高度保持控制器完成了整个飞行控制系统的设计;数值仿真结果表明,该系统具有良好的响应特性,且对系统不确定性具有较强的鲁棒性。     

Robust‐decentralized tracking control for a class of uncertain MIMO nonlinear systems with time‐varying delays

A new control design method based on signal compensation is proposed for a class of uncertain multi‐input multi‐output (MIMO) nonlinear systems in block‐triangular form with nonlinear uncertainties, unknown virtual control coefficients, strongly coupled interconnections, time‐varying delays, and external disturbances. By this method, the controller design is performed in a backstepping manner. At each step of backstepping procedure, a nominal virtual controller is first designed to get desired output tracking for the nominal disturbance‐free subsystem, and then a robust virtual compensator is designed to restrain the effect of the uncertainties, delays involved in the subsystem, and the couplings among the subsystems. The designed controller is linear and time‐invariant, so the explosion of complexity in the control law is avoid. It is proved that robust stability and robust practical tracking property of the closed‐loop system can be ensured, and the tracking errors can be made as small as desired. Copyright © 2013 John Wiley & Sons, Ltd.     

一类不确定非线性系统的指数鲁棒输出跟踪

研究一类不确定非线性系统的鲁棒输出跟踪控制问题。应用输入／输出反馈线性化法和李亚普诺夫方法,提出一种基于不确定项上界的连续型鲁棒输出跟踪控制器设计方法。该控制器不仅可确保闭环系统的状态一致最终有界,使系统输出按指数规律跟踪期望输出,而且计算简单,更易实现。仿真结果证明了该方法的可行性与有效性。     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Adaptive tracking control of MIMO nonlinear nonminimum phase system with unknown input nonlinearity

This paper proposes a nonlinear adaptive control for output tracking of multi‐input multi‐output nonlinear nonminimum phase system with input nonlinearity. The parameters of the input nonlinearity are assumed to be unknown. This problem is challenging, not only because of the unstable internal dynamics of nonminimum phase system, but also the existence of the unknown input nonlinearity. The partially linearized model of the original system is obtained through input/output linearization, and a states tracking model is constructed based on the computed ideal internal dynamics. A nonlinear adaptive controller, which can guarantee the bounded of output tracking error in the existence of unknown input nonlinearity, is proposed. Finally, a numerical simulation on vertical takeoff and landing aircraft is given to show the effectiveness of the proposed control methods.     

Robust nonlinear ship course‐keeping control by H∞ I/O linearization and μ‐synthesis

In this paper, the H_∞ input/output (I/O) linearization formulation is applied to design an inner‐loop nonlinear controller for a nonlinear ship course‐keeping control problem. Due to the ship motion dynamics are non‐minimum phase, it is impossible to use the ordinary feedback I/O linearization to resolve. Hence, the technique of H_∞ I/O linearization is proposed to obtain a nonlinear H_∞ controller such that the compensated nonlinear system approximates the linear reference model in I/O behaviour. Then a μ‐synthesis method is employed to design an outer‐loop robust controller to address tracking, regulation, and robustness issues. The time responses of the tracking signals for the closed‐loop system reveal that the overall robust nonlinear controller is able to provide robust stability and robust performance for the plant uncertainties and state measurement errors. Copyright © 2002 John Wiley & Sons, Ltd.     

Two‐stage stability control for strict‐feedback systems with input delays and input nonlinear uncertainties

In this paper, a two‐stage control procedure is proposed for stabilization of a class of strict‐feedback systems with unknown constant time delays and nonlinear uncertainties in the input. A nominal controller is first designed to compensate input time delays without considering input nonlinear uncertainties. Extended from backstepping algorithm, input delay compensation is realized by means of predicted states that are computed through integration of cascaded system dynamics, making the nominal closed‐loop system asymptotically stable. Based on the nominal controller presented for the input delay system, a multi‐timescale system is subsequently developed to estimate the unknown input nonlinearity and make the estimate approach the nominal control input as fast as possible. It is proved that the proposed control scheme can make states of the strict‐feedback systems converge to zero and all the signals of the closed‐loop systems are guaranteed to be bounded in the presence of input time delays and nonlinear uncertainties. Simulation verification is carried out to illuminate the effectiveness of the proposed control approach.     

Observer‐based decentralized adaptive control for large‐scale pure‐feedback systems with unknown time‐delayed nonlinear interactions

This paper presents an approximation design for a decentralized adaptive output‐feedback control of large‐scale pure‐feedback nonlinear systems with unknown time‐varying delayed interconnections. The interaction terms are bounded by unknown nonlinear bounding functions including unmeasurable state variables of subsystems. These bounding functions together with the algebraic loop problem of virtual and actual control inputs in the pure‐feedback form make the output‐feedback controller design difficult and challenging. To overcome the design difficulties, the observer‐based dynamic surface memoryless local controller for each subsystem is designed using appropriate Lyapunov‐Krasovskii functionals, the function approximation technique based on neural networks, and the additional first‐order low‐pass filter for the actual control input. It is shown that all signals in the total controlled closed‐loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, simulation examples are provided to illustrate the effectiveness of the proposed decentralized control scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive fault tolerant control for a class of input and state constrained MIMO nonlinear systems

In this work, we present a novel adaptive fault tolerant control (FTC) scheme for a class of control input and system state constrained multi‐input multi‐output (MIMO) nonlinear systems with both multiplicative and additive actuator faults. The input constraints can be asymmetric, and the state constraints can be time‐varying. A novel tan‐type time‐varying Barrier Lyapunov Function (BLF) is proposed to deal with the state constraints, and an auxiliary system is designed to analyze the effect of the input constraints. We show that under the proposed adaptive FTC scheme, exponential convergence of the output tracking error into a small neighbourhood of zero is guaranteed, while the constraints on the system state will not be violated during operation. Estimation errors for actuator faults are bounded in the closed loop. An illustrative example on a two degree‐of‐freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed FTC scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

二阶动态滑模控制在移动机械臂输出跟踪中的应用

针对移动机械臂的输出跟踪问题,结合高阶滑模控制和动态滑模控制的设计思想为其设计了一种二阶动态滑模控制器.首先给出了包括驱动电机动态特性的移动机械臂的简化动态模型,然后通过微分同胚和输入变换将其分解为四个低阶子系统,并给出了其输出跟踪的二阶动态滑模控制器的设计方法.仿真结果表明,所设计的二阶动态滑模控制器不仅能很好地跟踪给定轨迹,而且能有效地削弱滑模控制系统的抖振.