基于免疫函数的输入死区未知严格反馈非线性系统投影自适应指令滤波反步控制

为解决含有未知项以及输入死区的严格反馈非线性系统跟踪控制问题,提出一种基于免疫函数的投影自适应指令滤波有限时间控制方法.该方法使用免疫函数构造扩张状态观测器对具有输入死区控制系统中的未知项进行逼近,并使用指令滤波解决反步法中微分爆炸问题,建立滤波误差补偿机制降低滤波误差对跟踪精度的影响,同时使用投影算子保证了自适应参数的有界性.与现有文献中基于障碍李雅普诺夫函数的自适应反步约束控制相比较,文章可同时约束系统状态、补偿跟踪误差以及自适应参数在预设的范围内,保证了闭环系统中所有信号有界,结合有限时间控制加快了控制系统的收敛速度.最后仿真结果表明了文章控制方法的有效性.     

约束关联大系统的自适应模糊容错控制

讨论了一类输出受限的非线性关联大系统的自适应分散容错控制问题.提出了非线性映射的方法处理系统受约束的输出变量,从而将约束量的初值选取区间扩大为整个约束空间;用有界的参考信号代换未知关联函数的自变量,再用模糊逼近器处理这些未知函数,并结合自适应技术处理代换误差和逼近误差,从而获得了全局稳定性的结果;所有4种类型的执行器故障均可得到在线补偿.仿真结果进一步验证了文章方法的有效性.     

一类分数阶超混沌系统修正函数投影同步的滑模控制

对一类具有未知参数的分数阶超混沌系统的修正函数投影同步进行研究.通过设计响应系统的补偿器,进而得到修正函数投影同步的误差系统.基于自适应滑模控制理论和分数阶微分系统的稳定性理论,设计了一种自适应同步的控制方案.通过选取自适应滑模控制器以及参数自适应控制率,最终实现了驱动系统和响应系统修正函数投影同步,并可以对不确定参数进行估计.最后针对结论,以分数阶超混沌L(u|¨)系统为例,利用Adams-Bashfortlh-Moultom算法进行数值仿真,其结果说明了该方法的有效性和可行性.     

基于RBF神经网络的一类非线性时滞系统自适应控制器设计

针对一类带有未知非线性函数的严格反馈非线性时滞系统,设计了一种自适应神经网络控制器.选择径向基函数神经网络逼近未知的非线性函数.所提出的控制方案能保证闭环系统的所有信号是全局一致最终有界的.证明了跟踪误差信号将收敛于一个小紧集内.仿真实例验证了所提出方法的有效性.     

一类带输入时滞的线性时滞系统的新型自适应控制

针对一类带输入时滞且状态时滞常数未知的线性时滞系统,基于LMI方法,采用一种Lyapunov-Krasovskii函数和新型带记忆的状态反馈控制,突破以往自适应控制对估计值的大小限制,研究了一种新型的对该类系统未知状态时滞常数进行自适应控制的控制器设计问题,并且通过带记忆控制器中的常数的适当选取使得带记忆控制始终能反映未知时滞,仿真例子显示了该种方法的有效性.     

一类非线性系统的自适应反步控制

研究一类带有未知常数参量的非线性系统的镇定及自适应控制器设计问题,提出了一类非线性系统参数估计器设计及自适应反步控制器设计的新方法.构造出Lyapunov函数, 并给出闭环系统全局渐近稳定的新的充分条件.例子表明了所获方法的有效性.     

受扰不确定混沌系统的双路组合函数投影同步

研究了具有未知参数和外界扰动的多个混沌系统之间的双路组合函数投影同步问题.首先给出了由四个混沌驱动系统和两个混沌响应系统组成的双路组合函数投影同步系统的定义,然后以Lyapunov稳定性理论和不等式变换方法为分析依据,设计了鲁棒自适应控制器和参数自适应律,使得两路同步系统中的响应系统和驱动系统按照相应的函数比例因子矩阵实现同步,并有效克服未知有界干扰和未知参数的影响.相应的理论分析和数值仿真证明了该同步方案的可行性和有效性.     

带积分环节的隶属度函数中心自适应模糊控制器设计

自适应变论域模糊控制是改变模糊控制性能的主要方法之一,针对自适应变论域模糊控制器的稳定性设计这一难题,将输入和输出的隶属度函数分别用论域值表示成解析形式;针对典型模糊控制系统没有考虑积分环节带来的稳态误差问题,设计一种积分器,并从减小控制规则数目的角度出发设计了针对积分环节的控制规则库;引进Lyapunov函数,设计了输入隶属度函数论域值的自适应律和输出隶属度函数中心取值的自适应律,最后针对Duffing非线性系统仿真,结果表明设计的变论域模糊控制器是可行的。     

超混沌系统的函数投影同步与参数辨识

研究了一参数未知超混沌系统的函数投影同步问题．基于李雅谱诺夫稳定性理论,设计了实现混沌系统函数投影同步的有效非线性控制器,可以快速实现超混沌系统的加速函数投影同步,同时设计了参数控制律,有效的辨识了系统的未知参数,数值仿真验证了理论分析和数值计算的正确性．     

PMSM系统的参数稳定性分析和自适应控制

针对含未知参数的永磁同步电机(PMSM)系统,研究其参数稳定性和自适应控制问题.首先,分析了参数变化对PMSM系统平衡点的影响.然后,通过设计自适应控制器使得闭环系统具有耗散Hamiltonian结构,使闭环系统的能量分布随着未知参数的漂移而自动的改变,且能量函数总在未知参数对应的平衡点处取得极小值,从而实现自适应参数...     

Adaptive fuzzy backstepping control for a class of MIMO switched nonlinear systems with unknown control directions

In this article, an adaptive fuzzy output tracking control approach is proposed for a class of multiple‐input and multiple‐output uncertain switched nonlinear systems with unknown control directions and under arbitrary switchings. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. A Nussbaum gain function is introduced into the control design and the unknown control direction problem is solved. Under the framework of the backstepping control design, fuzzy adaptive control and common Lyapunov function stability theory, a new adaptive fuzzy output tracking control method is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed‐loop system are bounded and the tracking error remains an adjustable neighborhood of the origin. A numerical example is provided to illustrate the effectiveness of the proposed approach. © 2015 Wiley Periodicals, Inc. Complexity 21: 155–166, 2016     

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

This paper deals with the problem of adaptive fuzzy tracking control for a class of switched uncertain nonlinear systems. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and the adaptive backstepping and dynamic surface control techniques are adopted. First, a new state-dependent switching method is proposed. By introducing convex combination technique and designing a state-dependent switching law, only the solvability of the adaptive tracking control problem for a convex combination of the subsystems is necessary. Second, a new common Lyapunov function with switched adaptive parameters is constructed to reduce the conservatism. Third, to avoid Zeno behavior, a modified state-dependent switching law with dwell time is proposed. It is shown that under the proposed control and switching laws, all the signals of the closed-loop system are bounded and all the state tracking errors can converge to a priori accuracy, even if some subsystems are uncontrollable. Finally, the effectiveness of the proposed method is illustrated through two simulation examples.     

Adaptive neural dynamic surface control with fixed-time prescribed performance for uncertain nonstrict-feedback stochastic switched systems

An adaptive neural dynamic surface control (DSC) problem with fixed-time prescribed performance (FTPP) is investigated for a class of nonstrict-feedback stochastic switched systems. Differently from the existing works for FTPP problem, the stochastic switched systems with nonstrict-feedback form and completely unknown systems are considered in this paper, and the unknown functions are approximated by some radial basis function (RBF) neural networks (NNs). The desired adaptive neural controller is designed by using common Lyapunov function method and defining fixed-time prescribed performance function (PPF). And based on the adaptive DSC scheme with the nonlinear filter, the “explosion of complexity” problem is avoided. Besides, the constructed fixed-time PPF just need to meet the requirement of second derivative exists. According to the Lyapunov stability theory, the FTPP of output tracking error is achieved, and all signals of closed-loop system remain bounded in probability. Finally, simulation results are presented to verify the availability of the designed control strategy.     

Adaptive-observer-based output-constrained tracking of a class of arbitrarily switched uncertain non-affine nonlinear systems

This paper addresses an adaptive output-feedback tracking problem of arbitrarily switched pure-feedback nonlinear systems with time-varying output constraints and unknown control directions. In this work, the tracking problem of switched non-affine nonlinear systems with output constraints is transformed into the stabilization problem of switched unconstrained affine systems. The main contribution of this paper is to present a universal formula for constructing an adaptive state-observer-based tracking controller with only two adaptive parameters by using the common Lyapunov function method. These adaptive parameters in the proposed control scheme are derived using the function approximation technique and a priori knowledge of the signs of control gain functions is not required. The theoretical analysis is presented for the Lyapunov stability and the constraint satisfaction of the resulting closed-loop system in the presence of arbitrary switchings.     

Distributed adaptive formation tracking using quantized feedback communication for networked mobile robots with unknown wheel slippage

In this study, we consider a quantized-feedback-communication-based control design problem for the distributed adaptive formation tracking of multiple nonholonomic mobile robots with unknown slippage constraints under capacity-limited network control environments. Uniform-hysteretic quantizers are employed to quantize all the inputs and states of robots and the quantized position information of each robot is only transmitted to neighboring robots through directed networks. Compared with existing literature related to the robot formation, the primary contribution of this paper lies in establishing a novel local adaptive control design methodology to deal with the discontinuity problem caused by using the quantized states of each follower and the quantized position communication of neighboring robots. In the proposed strategy, the communication of the orientations and velocities of neighboring robots is not required for the local control design of follower robots. Moreover, quantized-states-based adaptive compensation schemes are constructed for the effects of signal quantization and wheel slippage. Based on the analysis of quantization errors, the practical stability strategy of the overall closed-loop formation system is derived with the convergence of local tracking errors. Simulation results clarify the proposed formation strategy.     

一类不确定混沌系统的自适应跟踪控制

对一类不确定混沌系统 ,讨论了系统的自适应跟踪控制问题 .基于 Lyapunov函数方法 ,构造出了一类新的自适应控制器 .该控制器的构造简单 ,并能控制着混沌系统的状态全局渐近跟踪预先给定的任何有界轨线 .仿真实例验证了所得控制器的有效性 .     

卫星姿态跟踪的间接自适应模糊预测控制

对含模型不确定性和未知干扰的卫星姿态系统提出了具有间接自适应模糊补偿的广义预测跟踪控制方法. 首先基于卫星姿态动力学模型设计了非线性广义预测控制律, 再利用自适应模糊系统逼近预测控制律中的模型不确定项, 使得所得到的预测控制算法可实施.证明了当卫星姿态模型中不确定项满足一定条件时, 所设计的控制律可使卫星姿态跟踪误差收敛到原点的小邻域内,并仿真结果验证了所提出方法的有效性.     

Adaptive stabilization of high order nonholonomic systems with strong nonlinear drifts

This paper investigates the problem of adaptive stabilization control design for a class of high order nonholonomic systems in power chained form with strong nonlinear drifts, including unmodeled dynamics, and dynamics modeled with unknown nonlinear parameters. A parameter separation technique is introduced to transform the nonlinear parameterized system into a linear-like parameterized system. Then, by the use of input-state scaling technique and adding a power integrator backstepping approach, an adaptive state feedback controller is obtained. The adaptive control based switching strategy is proposed to eliminate the phenomenon of uncontrollability. Global asymptotic regulation of the closed-loop system states and the boundedness of other signals are guaranteed. Simulation examples demonstrate the effectiveness of the proposed scheme.     

Quantized-states-based adaptive control against unknown slippage effects of uncertain mobile robots with input and state quantization

An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.     

一类死区非线性输入系统的自适应模糊控制

针对一类具有死区非线性输入的非线性系统,基于滑模控制的基本原理,利用II型模糊逻辑系统对未知函数进行在线逼近,提出了一种具有监督器的自适应模糊滑模控制方法。该方法通过监督控制器保证闭环系统所有信号有界,并通过引入最优逼近误差的自适应补偿项来消除建模误差的影响。通过理论分析,证明了跟踪误差收敛到零。