控制增益未知的船舶航向非线性自适应跟踪控制

针对参数不确定的船舶运动非线性控制系统控制方向未知的困难,将逆推算法与Nussbaum增益方法相结合,提出一种新的自适应非线性控制策略,从而实现船舶运动航向跟踪控制.首先,从理论上证明了所设计的自适应控制器保证最终的控制系数符号未知的参数不确定船舶运动非线性系统中所有信号一致有界,船舶的实际航向全局自适应地渐近跟踪期望的参考航向.对两条船舶数学模型的仿真实验结果表明,所设计的自适应非线性跟踪控制器具有良好的适应性及鲁棒性.     

船舶航向控制的多滑模鲁棒自适应设计

针对带有未知虚拟控制增益和常参数不确定的非匹配不确定船舶航向非线性控制问题,设计了一种新的多滑模鲁棒自适应控制算法.该算法利用神经网络来逼近系统模型的不确定性;应用逐步递推的多滑模控制算法降低了控制器的复杂性;尤其是采用Nussbaum函数处理系统中符号未知的问题,避免了可能存在的控制器奇异值问题;然后借助Lyapunov稳定性分析方法,理论分析证明了所得闭环系统全局一致最终有界,且跟踪误差收敛到零.仿真试验结果表明,该方法具有较好的控制效果.     

基于状态观测器的非仿射非线性系统鲁棒自适应H∞跟踪控制

针对一类非仿射非线性系统，提出了基于状态观测器的鲁棒自适应H∞跟踪控制结构．文中利用高斯径向基神经网络（RBF神经网络）在线抵消非线性模型误差，利用高增益观测器估计不能直接测量的输出导数．利用李亚普若夫稳定理论导出了系统的控制律，包括固定结构的控制律和自适应控制律两个部分，并给出了详细的理论分析和证明：在系统没有扰动时，确保跟踪误差渐近趋于零且系统的所有信号有界；存在扰动时，取得了预期的H∞跟踪性能．     

控制方向未知的全状态约束非线性系统的鲁棒自适应跟踪控制

针对一类控制方向未知的含有时变不确定参数和未知时变有界扰动的全状态约束非线性系统,本文提出了一种基于障碍Lyapunov函数的反步自适应控制方法.障碍Lyapunov函数保证了系统状态在运行过程中始终保持在约束区间内;Nussbaum型函数的引入解决了系统控制方向未知的问题;光滑投影算法确保了不确定时变参数的有界性.障碍Lyapunov函数、Nussbaum型函数及光滑投影算法与反步自适应方法的有效结合首次解决了控制方向未知的全状态约束非线性系统的跟踪控制问题.所设计的自适应鲁棒控制器能在满足状态约束的前提下确保闭环系统的所有信号有界.通过恰当地选取设计参数,系统的跟踪误差将收敛于0的任意小的邻域内.仿真结果表明了控制方案的可行性.     

船舶航向的鲁棒自适应控制器的设计和仿真

针对带参数不确定性,未知有界扰动及未建模动态的船舶航向跟踪控制问题,本文介绍了一种鲁棒自适应算法。此算法能保证系统的全局稳定,使闭环系统的所有变量一致有界。并通过仿真对两种算法进行比较,可以看出,依照本文算法设计的控制器具有很好的鲁棒性。     

一类非线性系统的神经网络L2鲁棒自适应控制

对于一类具有三角结构的单输入单输出不确定非线性系统的跟踪控制问题,用反步法和动态面控制方法设计了一种神经网络L2鲁棒自适应控制器.控制器设计中没有直接解HJI(Hamilton-Jacobi-Isaac)不等式,而是合理地选择了L2增益性能指标,将被控系统各个状态变量的跟踪误差和神经网络各权值的跟踪误差看作整个控制系统的各个状态变量,并用李亚普诺夫定理和HJI不等式证明了使用提出的摔制器后,这些状态变量具有小于等于事先规定的正实数y的L2增益,并且当所考虑的干扰向量为零向量时,提出的控制器在原点大范围渐近稳定.仿真研究结果表明所提出的控制器具有很好的跟踪性能和很强的鲁棒性.     

不确定串联非线性系统H∞鲁棒自适应控制

针对一类含有未知参数和干扰的非最小相位串联非线性系统,结合H∞控制和自适应控制方法并利用李雅普诺夫函数递推设计方法设计了状态反馈H∞自适应控制器,避免了求解Hamilton-Jacobi-Isaacs不等式设计控制器的困难.该控制器不仅保证闭环系统ISS(input-to-state)稳定,而且使得系统对于所有允许的参数不确定从干扰输入到可控输出的 L 2增益不大于给定的值.最后,给出了一个仿真例子,仿真结果充分表明了所设计的控制器的可行性和有效性.     

基于单网络评判学习的非线性系统鲁棒跟踪控制

针对一类具有不确定性的连续时间非线性系统,提出一种基于单网络评判学习的鲁棒跟踪控制方法.首先建立由跟踪误差与参考轨迹构成的增广系统,将鲁棒跟踪控制问题转换为镇定设计问题.通过采用带有折扣因子和特殊效用项的代价函数,将鲁棒镇定问题转换为最优控制问题.然后,通过构建评判神经网络对最优代价函数进行估计,进而得到最优跟踪控制算法.为了放松该算法的初始容许控制条件,在评判神经网络权值更新律中增加一个额外项.利用Lyapunov方法证明闭环系统的稳定性及鲁棒跟踪性能.最后,通过仿真结果验证该方法的有效性和适用性.     

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况,设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面,引入神经网络对船舶模型不确定部分进行逼近,设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计,并应用一种非线性增益函数构造动态面控制律,选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快、精度高,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

不确定非线性系统的模糊鲁棒跟踪控制

提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性.     

Adaptive nonlinear ship tracking control with unknown control direction

This paper is to suggest a novel design for ship tracking control of the benchmark Norrbin nonlinear ship model under relaxed conditions. The challenges of the problem under investigation include the uncertain or absent information of the model time constant, the uncertain reference course, and the intrinsic environmental disturbances. We formulate the ship tracking control problem in terms of a nonlinear output regulation problem with unknown control direction and uncertain exosystems. To solve the problem, we first adopt a novel internal model candidate to convert the output regulation problem into a stabilization problem for an augmented system. Then, a Nussbaum‐gain‐based stabilizer is developed for the augmented system to complete the design. Simulation results are presented to show the effectiveness of the proposed controller.     

Adaptive neural tracking control for a class of stochastic nonlinear systems

This paper investigates the problem of adaptive neural control design for a class of single‐input single‐output strict‐feedback stochastic nonlinear systems whose output is an known linear function. The radial basis function neural networks are used to approximate the nonlinearities, and adaptive backstepping technique is employed to construct controllers. It is shown that the proposed controller ensures that all signals of the closed‐loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. The salient property of the proposed scheme is that only one adaptive parameter is needed to be tuned online. So, the computational burden is considerably alleviated. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

Robust adaptive control of a class of nonlinear strict-feedback discrete-time systems with exact output tracking

In this paper, adaptive control is studied for a class of single-input–single-output (SISO) nonlinear discrete-time systems in strict-feedback form with nonparametric nonlinear uncertainties of the Lipschitz type. To eliminate the effect of the nonparametric uncertainties in an unmatched manner, a novel future states prediction is designed using states information at previous steps to compensate for the effect of uncertainties at the current step. Utilizing the predicted future states, constructive adaptive control is developed to compensate for the effects of both parametric and nonparametric uncertainties such that global stability and asymptotical output tracking is achieved. The effectiveness of the proposed control law is demonstrated in the simulation.     

基于反步法的移动机器人鲁棒跟踪控制

以四轮移动机器人为研究对象,建立了机器人完整的数学模型,包括运动学模型、动力学模型以及驱动电机模型。在机器人数学模型的基础上,采用反步法的思想设计具有全局收敛特性的鲁棒轨迹跟踪控制器,设计中考虑了驱动电机模型使控制器更符合实际控制要求,并将其分解为运动学控制器、动力学控制器以及电机控制器三部分,降低了控制器设计的难度。构造了系统的李雅普诺夫函数,证明了该类型移动机器人在所得控制器作用下,能实现对给定轨迹的全局渐近追踪。仿真实验结果表明基于反步法的控制器是有效的。     

Robust tracking control for a class of MIMO nonlinear systems with measurable output feedback

This paper proposes a robust output feedback controller for a class of nonlinear systems to track a desired trajectory. Our main goal is to ensure the global input-to-state stability (ISS) property of the tracking error nonlinear dynamics with respect to the unknown structural system uncertainties and external disturbances. Our approach consists of constructing a nonlinear observer to reconstruct the unavailable states, and then designing a discontinuous controller using a back-stepping like design procedure to ensure the ISS property. The observer design is realized through state transformation and there is only one parameter to be determined. Through solving a Hamilton–Jacoby inequality, the nonlinear control law for the first subsystem specifies a nonlinear switching surface. By virtue of nonlinear control for the first subsystem, the resulting sliding manifold in the sliding phase possesses the desired ISS property and to certain extent the optimality. Associated with the new switching surface, the sliding mode control is applied to the second subsystem to accomplish the tracking task. As a result, the tracking error is bounded and the ISS property of the whole system can be ensured while the internal stability is also achieved. Finally, an example is presented to show the effectiveness of the proposed scheme. Copyright © 2007 John Wiley & Sons, Ltd.     

Robust adaptive tracking control of robotic systems with uncertainties

To deal with the uncertainty factors of robotic systems, a robust adaptive tracking controller is proposed. The knowledge of the uncertainty factors is assumed to be unidentified; the proposed controller can guarantee robustness to parametric and dynamics uncertainties and can also reject any bounded, immeasurable disturbances entering the system. The stability of the proposed controller is proven by the Lyapunov method. The proposed controller can easily be implemented and the stability of the closed system can be ensured; the tracking error and adaptation parameter error are uniformly ultimately bounded （UUB）. Finally, some simulation examples are utilized to illustrate the control performance.     

基于QCNN的非线性跟踪问题研究

针对如何快速准确地跟踪到非线性系统的状态问题, 研究了量子细胞神经网络(QCNN)在非线性跟踪中的应用。在满足Lyapunov函数指数收敛的条件下, 设计了一种新型参数形式的控制器, 在此基础上, 对三种非线性系统即确定性非线性运动、参数和运动规律未知的非线性数据系统以及典型蔡氏电路进行了QCNN跟踪研究。仿真结果表明, 在QCNN系统中, 通过设计合理的控制器可以实现非线性问题状态的有效跟踪, 且实验结果为QCNN系统复杂度与跟踪的及时性之间关系提供了参考依据和有力的说明。设计的新型控制器及对实际问题处理方法为QCNN的理论及应用研究具有借鉴意义。     

输入输出受限船舶的轨迹跟踪自适应递归滑模控制

针对输入输出受限,模型部分不确定和受到未知海洋干扰的全驱动船舶的轨迹跟踪问题,提出一种基于时变非对称障碍李雅普诺夫函数的最小参数自适应递归滑模控制策略.该策略首先设计障碍李雅普诺夫函数约束船舶轨迹在有限区域内,利用最小参数法神经网络逼近模型不确定项,降低系统的计算复杂度,然后采用指令滤波器对输入信号进行幅值约束,同时避免对因反步法导致的微分爆炸问题,综合考虑船舶位置以及速度误差间的关系设计递归滑模控制律,提高系统的鲁棒性,采用双曲正切函数和Nussbaum函数补偿由输入饱和引起的非线性项,提高系统稳定性.最后通过Lyapunov理论分析证明了全驱动船舶闭环系统中所有信号是一致最终有界的.仿真结果表明,本文所设计的船舶轨迹跟踪控制方案能有效处理船舶模型不确定部分以及未知外界干扰的问题,能够实现船舶在输入受限的情况下在有限区域内航行并准确的跟踪期望轨迹,具有较强的鲁棒性.