Dynamic switching based fuzzy control strategy for a class of distributed parameter system

In this work, a dynamic switching based fuzzy controller combined with spectral method is proposed to control a class of nonlinear distributed parameter systems (DPSs). Spectral method can transform infinite-dimensional DPS into finite ordinary differential equations (ODEs). A dynamic switching based fuzzy controller is constructed to track reference values for the multi-inputs multi-outputs (MIMO) ODEs. Only a traditional fuzzy logic system (FLS) and a rule base are used in the controller, and membership functions (MFs) for different ODEs are adjusted by scaling factors. Analytical models of the dynamic switching based fuzzy controller are deduced to design the scaling factors and analyze stability of the control system. In order to obtain a good control performance, particle swarm optimization (PSO) is adopted to design the scaling factors. Moreover, stability of fuzzy control system is analyzed by using the analytical models, definition of the stability and Lyapunov stability theory. Finally, a nonlinear rod catalytic reaction process is used as an illustrated example for demonstration. The simulation results show that performance of proposed dynamic switching based fuzzy control strategy is better than a multi-variable fuzzy logic controller.     

Unmanned bicycle balancing via Lyapunov rule-based fuzzy control

Guaranteed stability fuzzy controller for stabilization the motion of an unmanned bicycle is proposed. First, a fuzzy control system capable of automatically balancing an unmanned bicycle through tracking desired roll angle is developed. Fuzzy logic controller membership functions are defined utilizing scaling factors. To guarantee the stability of the closed loop system, similar to previous approaches reported in the literature, fuzzy If–Then rules are constructed based on Lyapunov stability criterion. It is indicated that the proposed fuzzy controller violates Lyapunov stability criterion. The reason of such a violation is argued in detail. To cope with this shortcoming, some modifications are made to the control strategy to assure stability. Through these modifications, the modified fuzzy controller is developed which simultaneously balances the bicycle and guarantees stability while minimizing roll angle tracking error and its derivative. It is indicated that the improved fuzzy controller can adapt to a variety of initial conditions. Moreover, robustness of the controller against parameter variation is verified through its implementation on different bicycle designs (different sets of bicycle parameters). Simulation results confirm the efficacy of the proposed fuzzy controller in terms of settling time and overshoot in comparison with previous studies. Sensitivity analysis of the controller efficiency with respect to system parameters is also assessed.     

基于自结构动态递归模糊神经网络的无人机姿态控制*

针对无人机非线性、强耦合等特点,提出了基于该自结构动态递归模糊神经网络的姿态控制系统,给出了基于Lyapunov函数的系统稳定性证明。对四层模糊神经网络进行了优化和改进,设计了自结构动态递归模糊神经网络,该网络可以根据系统状态在线更新权值、创建/删除节点、优化网络结构。仿真表明：该控制方法的突出优点是,在兼顾考虑了系统中的不确定性因素、非线性因素及外部干扰并存的情况下,保证系统的稳定性和跟踪性能;同时此网络结构比固定结构的模糊神经网络响应速度快,因此更具优越性。     

On Maximum Stability Margin Design of Nonlinear Uncertain Systems: Fuzzy Control Approach

This paper studies the maximum stability margin design for nonlinear uncertain systems using fuzzy control. First, the Takagi and Sugeno fuzzy model is employed to approximate a nonlinear uncertain system. Next, based on the fuzzy model, the maximum stability margin for a nonlinear uncertain system is studied to achieve as much tolerance of plant uncertainties as possible using a fuzzy control method. In the proposed fuzzy control method, the maximum stability margin design problem is parameterized in terms of a corresponding generalized eigenvalue problem (GEVP). For the case where state variables are unavailable, a fuzzy observer‐based control scheme is also proposed to deal with the maximum stability margin for nonlinear uncertain systems. Using a suboptimal approach, we characterize the maximum stability margin via fuzzy observer‐based control in terms of a linear matrix inequality problem (LMIP). The GEVP and LMIP can be solved very efficiently via convex optimization techniques. Simulation examples are given to illustrate the design procedure of the proposed method.     

一类大系统的间接自适应分散模糊控制

针对一类未知非线性大系统,将模糊控制、模糊逻辑系统及滑模控制相结合,提出了 一种间接自适应模糊控制策略,仿真结果证明了所提出的算法是有效的.     

机械手的模糊逆模型鲁棒控制

提出一种基于模糊聚类和滑动模控制的模糊逆模型控制方法,并将其应用于动力学 方程未知的机械手轨迹控制.首先,采用C均值聚类算法构造两关节机械手的高木-关野 (T-S)模糊模型,并由此构造模糊系统的逆模型.然后,在提出的模糊逆模型控制结构中, 离散时间滑动模控制和时延控制(TDC)用于补偿模糊建模误差和外扰动,保证系统的全局 稳定性并改进其动态和稳态性能.系统的稳定性和轨迹误差的收敛性可以通过稳定性定理来 证明.最后,以两关节机械手的轨迹跟随控制为例,揭示了该设计方法的控制性能.     

基于自适应模糊滑模观测器的永磁同步电机无传感器矢量控制

针对传统滑模观测器(SMO)存在的抖振及相位延迟问题,提出一种自适应模糊滑模观测器来实现永磁同步电机(PMSM)无传感器控制.根据Lyapunov稳定性定理构建自适应模糊滑模观测器,以保证系统的稳定性.通过分析滑模增益对系统抖振的影响设计模糊控制系统,从而实现对滑模增益的动态调整,削弱抖振现象,提高系统的鲁棒性.建立反电动势观测器代替低通滤波器,避免相位延迟,从而提高系统的稳定性及准确跟踪性.仿真结果验证了所提出方法的可行性.     

板球系统的间接模糊自适应控制

针对具有强耦合、不确定摩擦力的多变量非线性板球系统,利用Lyapunov稳定理论,设计一种间接模糊自适应控制器。该控制器可以在确保系统变量在有限范围内变动的同时保持收敛性,并且在系统的增益矩阵不可逆时,使得板球系统稳定并跟踪误差收敛到零邻域内。控制器是由监督、间接模糊自适应和自适应补偿3种控制算法结合的。仿真实验表明,所提出的控制方法能够确保板球系统跟踪控制的稳定性和收敛性。     

离散模糊系统分析与设计的模糊Lyapunov方法

研究离散T-S模糊控制系统基于模糊Lyapunov函数的稳定性分析及控制器设计问 题.首先,构造出离散型模糊Lyapunov函数,模糊Lyapunov函数是系数与T-S模糊系统的模糊 规则权重相对应的复合型Lyapunov函数.然后,得到了开环系统新的稳定性充分条件,与公共 Lyapunov方法的结果相比,这一条件更为宽松.进而,基于一系列线性矩阵不等式设计出模糊 控制器.最后,仿真实例说明了该方法的算法和本文条件的优越性.     

Limit-cycle analysis of dynamic fuzzy control systems

The main purpose of this study is to predict limit cycles of a dynamic fuzzy control system by combining a stability equation, describing function and parameter plane. The stability of a linearized dynamic fuzzy control system is then analyzed using stability equations and the parameter plane method, with the assistance of a describing function method. This procedure identifies the amplitude and frequency of limit cycles that are clearly formed by the dynamic fuzzy controller in the parameter plane. Moreover, the suppression of the limit cycle by adjusting control parameters is proposed. Continuous and sampled-data systems are addressed, and the proposed approach can easily be extended to a fuzzy control system with multiple nonlinearities. Simulations are performed to demonstrate the effectiveness of the proposed scheme.