基于一种新型趋近律的自适应滑模控制

首先, 利用特殊幂次函数和反双曲正弦函数构造一种新型滑模变结构控制趋近律; 然后, 采用该趋近律设计一种自适应滑模控制律, 并证明滑模控制系统误差渐近收敛. 仿真实验表明: 在存在时变转动惯量和摩擦力矩扰动的情况下, 该自适应滑模控制系统具有较高的位置和速度跟踪精度, 并有效减弱了控制输入信号的高频震颤现象; 同时, 采用反双曲正弦函数的自适应律能较好地平滑系统转动惯量估计值, 减小控制输入信号的幅值.

     

一类不确定非线性系统的鲁棒自适应控制

针对一类MIMO不确定非线性系统的输出跟踪问题, 基于自适应反步法和滑模控制为其设计了鲁棒自适应控制器. 模型包含3种不确定性: 1) 参数不确定性; 2) 输入增益的不确定性; 3) 代表系统未建模动态和干扰的不确定函数, 该函数有界. 以非完整移动机械臂的输出跟踪控制为目标, 对其进行仿真实验, 实验结果表明所提出的控制算法是正确有效的.     

网络环境下非线性切换系统的固定时间控制

本文针对一类不确定非线性切换系统, 在控制系数和量化器参数未知的情况下, 研究系统的自适应固定时间控制问题. 首先, 文章利用增加幂次积分法和共同Lyapunov函数设计带有可调参数的自适应控制器. 然后, 基于改进的固定时间控制理论, 文章提出有效的参数调节律, 从而实现闭环系统的固定时间稳定性. 最后, 通过仿真实验验证所提控制算法的有效性     

高阶非线性不确定系统的自适应实际输出跟踪控制

本文研究了一类高阶非线性不确定系统的自适应实际输出跟踪控制问题, 该问题在未知控制系数的下界精确知道的假设下已经得到了研究. 基于新的鲁棒自适应控制和连续控制思想, 成功去除该假设条件. 进而应用增加幂次积分的方法, 给出了构造连续自适应实际输出跟踪控制器的系统化方法. 该控制器确保闭环系统的所有状态全局稳定, 并且经过有限时间后, 跟踪误差可以被某一事先给定的任意正数界定. 最后, 通过一个仿真算例验证了理论结果的正确性.     

一种变论域模糊控制自适应算法

针对受控系统非线性、时变性、复杂性和不确定性的特点, 文中采用一种基于模糊控制的变论域自适应控制算法, 将变论域引入模糊控制的隶属函数中, 设计了输入函数的自适应律, 并通过李雅普诺夫函数进行了稳定性分析. 仿真结果表明在有干扰的情况下, 算法能很好的跟踪系统输入, 使系统跟踪误差小且能够保证系统的稳定性.该算法结构简单, 具有良好的鲁棒性和动态性能, 同时克服了系统参数变化对稳定性造成的不利影响. 仿真实例表明了本算法的正确性和有效性.     

基于广义双曲正切模型的机器人鲁棒模糊自适应控制

利用广义模糊双曲正切模型的全局逼近特点,设计一种模糊自适应控制器用于机器人轨迹跟踪控制.广义双曲正切模型利用输入变量的平移能以任意精度逼近系统的不确定动态.对于系统不确定外界干扰和模糊系统的逼近误差,通过求解一个线性矩阵不等式来保证闭环系统的鲁棒稳定性.对比传统的模糊基函数,在保证系统跟踪精度的前提下,双曲正切模糊基函数的自适应调整参数大大减少,仿真表明该控制算法具有较强的鲁棒性能和较好的跟踪性能.     

具有指定性能和全状态约束的多智能体系统事件触发控制

针对一类非严格反馈的非线性多智能体系统一致性跟踪问题,在考虑全状态约束和指定性能的基础上提出了一种事件触发自适应控制算法.首先,通过设计性能函数,使跟踪误差在规定时间内收敛于指定范围.然后,在反步法中引入Barrier Lyapunov函数使所有状态满足约束条件,结合动态面技术解决传统反步法产生的"计算爆炸"问题,并利用径向基函数神经网络（Radial basis function neural networks,RBF NNs）处理系统中的未知非线性函数.最后基于Lyapunov稳定性理论证明系统中所有信号都是半全局一致最终有界的,跟踪误差收敛于原点的有界邻域内且满足指定性能.仿真结果验证了该控制算法的有效性.     

间接自适应极点配置控制算法的跟踪能力研究

为了研究现有间接自适应极点配置控制算法的跟踪能力,提出了一般形式的间接自适应 极点配置控制算法.分析了这种算法用于已知和未知系统时的跟踪能力,并分别给出了渐近 跟踪参考输出的充要条件.利用上述结果,可以对现有间接算法的跟踪能力进行逐一研究.研 究表明,现有间接自适应极点配置控制算法的跟踪能力是有限的,它们至多能实现对一类参考 输出的渐近跟踪.     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

一类不确定非线性系统的鲁棒白适应控制

针对一类MIMO不确定非线性系统的输出跟踪问题,基于自适应反步法和滑模控制为其设计了鲁棒自适应控制器。模型包含3种不确定性：1）参数不确定性;2）输入增益的不确定性;3）代表系统未建模动态和干扰的不确定函数,该函数有界。以非完整移动机械臂的输出跟踪控制为目标,对其进行仿真实验,实验结果表明所提出的控制算法是正确有效的。     

具有预设暂态性能的一类不确定非线性系统渐近状态跟踪控制

研究一类不确定非线性时变系统的预设暂态性能渐近状态跟踪控制问题.在无需系统函数先验知识的条件下,本文采用漏斗控制技术和障碍李雅普诺夫函数方法,提出了一种新颖的鲁棒自适应状态反馈控制策略.所设计的控制器不仅能够确保状态跟踪误差渐近收敛到零点,而且满足其预先设定的性能要求.仿真实例验证了所提控制策略的有效性.     

Adaptive fuzzy robust tracking controller design via small gain approach and its application

An adaptive fuzzy robust tracking control (AFRTC) algorithm is proposed for a class of nonlinear systems with the uncertain system function and uncertain gain function, which are all the unstructured (or nonrepeatable) state-dependent unknown nonlinear functions arising from modeling errors and external disturbances. The Takagi-Sugeno type fuzzy logic systems are used to approximate unknown uncertain functions and the AFRTC algorithm is designed by use of the input-to-state stability approach and small gain theorem. The algorithm is highlighted by three advantages: 1) the uniform ultimate boundedness of the closed-loop adaptive systems in the presence of nonrepeatable uncertainties can be guaranteed; 2) the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed; and 3) the adaptive mechanism with minimal learning parameterizations can be obtained. The performance and limitations of the proposed method are discussed. The uses of the AFRTC for the tracking control design of a pole-balancing robot system and a ship autopilot system to maintain the ship on a predetermined heading are demonstrated through two numerical examples. Simulation results show the effectiveness of the control scheme.     

Predefined-time control for Euler–Lagrange systems with model uncertainties and actuator faults

A novel adaptive predefined-time tracking control algorithm is proposed for the Euler–Lagrange systems (ELSs) with model uncertainties and actuator faults. Compared with traditional finite-time and fixed-time studies, the system output tracking error under the proposed predefined-time controller converges to a small neighborhood of zero in finite time, whose upper bound is exactly a design parameter in the control algorithm. For the uncertain model, radial-based function neural network (RBFNN) is utilized to approximate the continuous uncertain dynamics. To deal with the actuator faults, an adaptive control law is involved in the fault-tolerant controller. In order to achieve the predefined-time bounded, a novel predefined-time sliding mode surface is designed. It is proved that the tracking error vector trajectory of closed-loop system is semi-globally uniformly ultimately predefined-time bounded, and the upper bounds of both the system settling time and the corresponding output tracking error can be adjusted with a simple parameter. Simulation examples finally demonstrate the effectiveness of the proposed control algorithm.     

Adaptive Fuzzy Finite-time Dynamic Surface Control for High-order Nonlinear System with Output Constraints

This paper studies the problem of finite-time fuzzy adaptive dynamic surface control (DSC) design for a class of single-input and single-output (SISO) high-order nonlinear systems with output constraint. Fuzzy logic systems (FLSs) are utilized to identify the unknown smooth functions. By adopting Barrier Lyapunov function (BLF), the problem of output constrain is handled. Combining adding a power integrator and adaptive backstepping recursion design technique, a novel fuzzy adaptive finite-time DSC algorithm is proposed. Based on finite-time Lyapunov stable theory, the developed control algorithm means that all the signals of the closed-loop system are semi-global practical finite-time stable (SGPFS) and the tracking error converges to a small neighborhood of origin in finite time. In addition, the output does not violate the given constrain bound. Finally, both numerical and practical simulation examples are given to illustrate the effectiveness of the proposed control algorithm.

     

Adaptive robust output tracking of uncertain strict-feedback nonlinear time-delay systems via control schemes with simple structure

The output tracking control problem is considered for a class of uncertain strict-feedback nonlinear systems with time-varying delays. In the paper, the time-varying delays are assumed to be any non-negative continuous and bounded functions, and it is not necessary for their derivatives to be less than one. It is also assumed that the upper bounds of nonlinear delayed state perturbations and external disturbances are unknown. On the basis of backstepping algorithm, a novel design method is proposed by which some simple adaptive robust output tracking control schemes are synthesised. The proposed design method can avoid the repeated differentiation problem which appears in using the conventional backstepping algorithm, and need not know all the nonlinear upper bound functions of uncertainties, which are repeatedly employed at each step of the backstepping algorithm. In particular, it is not necessary to know any information on the time-varying delays to construct our simple output tracking control schemes. It is also shown that the tracking error can converge uniformly exponentially towards a neighbourhood of the origin. Finally, a numerical example and its simulations are provided to demonstrate the design procedure of the simple method proposed in the paper.     

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator     

一种非线性系统的模糊自适应控制

针对一类非线性系统提出一种模糊自适应控制方案，设计中用模糊逻辑系统逼近非线性函数，骨于滑模原理及Ｌｙａｐｕｎｏｖ函数方法给出了闭环系统的稳定性分析。     

Neural network-based adaptive controller design of roboticmanipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies     

Sigma adaptive fuzzy sliding mode control of a class of nonlinear systems

Based on fuzzy approximators of nonlinear functions, a new adaptive fuzzy sliding mode control scheme is proposed for a class of nonlinear plants. In comparison with most existing methods, in which the parameter projection algorithm is often involved to prevent the estimated value of the input gain function from evolving into zero, the proposed control law has shown its success and simplicity in tackling the case when the value of the estimated input gain function becomes zero during online operations. A variant of adaptive law with dead-zone sigma-modification is introduced to help achieve this goal. The bounding parameters of the model approximation error and the external disturbance are all regarded as unknown constants in this paper, and adaptive laws for them are devised for tracking purposes. Based on Lyapunov's stability theory the proposed controller has been shown to render the tracking error arbitrarily close to zero. A comparably good tracking performance is obtained as illustrated by the simulation results for an inverted pendulum system.     

Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients

In this paper, a novel adaptive fuzzy control scheme is proposed for a class of uncertain single-input and single-output (SISO) nonlinear time-delay systems with the lower triangular form. Fuzzy logic systems are used to approximate unknown nonlinear functions, then the adaptive fuzzy tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the backstepping approach. The proposed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters is not more than the order of the systems under consideration. Finally, simulation studies are given to demonstrate the effectiveness of the proposed design scheme.