利用模糊系统的自适应模糊控制器

针对非线性系统控制,设计了利用TSK(Takagi-Sugeno-Kang)模糊系统的自适应模糊控制器。所设计的自适应控制方法是参考模型自适应控制方法,而且利用Lyapunov函数保证了闭环系统的稳定性,同时推导了最优的自适应控制规律。首先,根据控制对象的输入输出数据建立TSK模糊模型,然后,由TSK模糊模型设计初期的TSK模糊控制器,并根据自适应规律随时调整模糊控制器参数。倒立摆系统的仿真实验验证了所设计的自适应模糊控制器的有效性。     

A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators

This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the H_infin theory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems.     

直升机的TSK模糊控制

针对直升机动力学为非线性的特点,且存在不确定因数和状态变化,利用线性控制很难得到好的控制结果,提出利用TSK(Takagi-Sugeno-Kang)模糊系统控制小模型直升机.所设计的TSK模糊控制器是一个基于TSK模糊模型的非线性控制器,能保证闭环控制系统的稳定性.直升机数学模型中线性部分设计状态反馈控制器非线性部分先求TSK模糊模型,然后设计TSK模糊控制器.仿真和实验结果表明,所设计的TSK模糊控制器比线性控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

卡车的TSK模糊控制

针对在保证一定精度的条件下,要求一次性完成卡车的倒车的情况,利用TSK (Takagi-Sugeno-Kang)模糊系统控制卡车,在TSK模糊控制器作用下让卡车从任意的初始位置一次性倒车到指定的位置。在卡车数学模型中,线性部分设计线性状态反馈控制器,非线性部分先求TSK模糊模型,然后设计基于TSK模糊模型的TSK模糊控制器。它是一个非线性控制器,可保证闭环系统的稳定性。仿真实验结果表明,所设计的TSK模糊控制器对卡车的倒车控制是非常有效的。     

Incremental Evolutionary Design of TSK Fuzzy Controllers

This paper proposes a novel method for the incremental design and optimization of first order Tagaki-Sugeno-Kang (TSK) fuzzy controllers by means of an evolutionary algorithm. Starting with a single linear control law, the controller structure is gradually refined during the evolution. Structural augmentation is intertwined with evolutionary adaptation of the additional parameters with the objective not only to improve the control performance but also to maximize the stability region of the nonlinear system. From the viewpoint of optimization the proposed method follows a divide-and-conquer approach. Additional rules and their parameters are introduced into the controller structure in a neutral fashion, such that the adaptations of the less complex controller in the previous stage are initially preserved. The proposed scheme is evaluated at the task of TSK fuzzy controller design for the upswing and stabilization of a rotational inverted pendulum. In the first case, the objective is a time optimal controller that upswings the pendulum in to the upper equilibrium point in shortest time. The stabilizing controller is designed as a state optimal controller. In a second application the optimization method is applied to the design of a fuzzy controller for vision-based mobile robot navigation. The results demonstrate that the incremental scheme generates solutions that are similar in control performance to pure parameter optimization of only the gains of a TSK system. Even more important, whereas direct optimization of control systems with more than 35 rules fails to identify a stabilizing control law, the incremental scheme optimizes fuzzy state-space partitions and gains for hundreds of rules.     

基于DSP和CPLD的开放式机器人控制器

设计了一种新型的基于DSP和CPLD的开放式机器人控制器。该控制器以IBM-PC为上位计算机,用一块DSP运动控制卡对PR教学机器人的4个关节运动进行伺服控制,采用PCI总线进行上、下位机之间的通讯,实现双速率运行,较好地实现了机器人的实时控制。同时,在控制卡中采用参数自调整模糊控制算法,以提高系统控制精度和动态性能。实际应用表明,该控制器实现了机器人控制系统的高速率实时控制,增强了系统的鲁棒性和抗干扰能力,取得了良好的效果。     

基于支持向量机的TSK模糊模型辨识与控制

研究非线性系统TSK模糊模型的辨识与控制,利用TSK模型,可以将线性控制理论应用于非线性系统控制。基于支持向量机和递推最小二乘法,辨识出TSK模糊模型,并且通过遗传算法优化隶属度函数参数,最小化辨识误差。针对TSK模型进行控制,控制器包括两个部分：权重最大子系统反馈控制及其监督控制,监督控制保证了系统的稳定性。辨识和控制仿真结果证明了算法的有效性。     

A Compact/Open Network-Based Controller Incorporating Modular Software Architecture for a Humanoid Robot

This paper presents the design of a compact/open network-based controller incorporating modular software architecture for various kinds of robot applications. Within the proposed controller scheme, a standardized real-time network like CAN connects the central motion control part and the servo control part. Thus, the size of the servo controller becomes small enough to be attached inside the robot body and the control software can be designed with an open and modular concept. The open/compact controller incorporating a modular software architecture offers benefits of reduced engineering costs. The proposed architecture has been implemented on a KIST humanoid robot controller platform and its performance has been verified through experimental tests.     

Stability Analysis of a Simple-Structured Fuzzy Logic Controller

The stability analysis for the fuzzy logic controller (FLC) has widely been reported. Furthermore some researches have been introduced to simplify the design process of FLCs. One of them is to decrease the number of parameters representing the antecedent part of the fuzzy control rule. So we briefly explain a simple-structured fuzzy logic controller (SFLC) which uses only a single variable at the antecedent part of a fuzzy control rule. We analyze that it is absolutely stable based on the sector bounded condition. We expand a nonlinear controlled plant into a Taylor series about a nominal operating point. The fuzzy control system is transformed into a Lure system with nonlinearities. We also show the feasibility of the proposed stability analysis through a numerical example of a mass-damper-spring system.     

仿人机器人控制系统研究及其关节控制器设计

论述了由嵌入式计算机组成的3层仿人机器人控制系统,并详细介绍了其中的关节控制器。控制系统实行逐级控制,任务分散,提高了机器人的智能化程度。关节控制器选用TM320F2811型数字信号处理器作为处理器,具有全数字型、集成度高、体积小、功耗低、可实现多轴运动控制的特点。关节控制策略采用模糊PD结合传统PI调节的算法,实验结果表明这种控制算法具有较强快速性、精确性、鲁棒性。     

未知环境中移动机器人实时导航与避障的分层模糊控制

为了解决单模糊控制器的“规则库爆炸”问题,设计了一种分层的模糊控制器,用于指导移动机器人通过未知环境到达指定的目标点．控制器根据8个超声传感器的信息和目标相对于机器人的方位确定机器人的运动．首先,每个超声传感器的信息被输入到危险度模糊控制器（DFC）中,产生关于周围环境中障碍物危险度的模糊向量．这些模糊向量经过融合与归一化处理后分别输入到上层的速度模糊控制器（VFC）和角速度模糊控制器（RFC）的推理机中．VFC根据目标的距离和障碍物的危险度控制机器人的前进速度．RFC根据目标的方向和障碍物的危险度控制机器人的转向,并采用最大隶属度法的反模糊化策略解决“对称不确定”问题．仿真与实验结果证明了所设计的模糊控制器简单而有效．     

面向未知环境基于智能预测的模糊控制器研究

提出了一种新的面向未知环境的智能预测算法，并将此算法应用于机器人力跟踪控制中．该方法利用机械手末端与未知受限环境产生的接触轨迹，通过模糊推理智能地预测阻抗控制模型中的参考轨迹，并根据力误差变化用参考比例因子对其进行调节，以适应未知环境刚度的变化．通过对阻抗模型参数进行模糊调节减少受限运动中的力误差，提高了全局的力控制效果．仿真结果证明了此算法的有效性．     

Attitude Controller for Steady Hover of a Rotary-Wing Flying Robot

This paper presents an attitude controller for steady hover of CMERI’s Rotary-Wing Flying Robot. The main objective is to control the dynamic behaviour of the robot, which is complex in shape and motion as nonlinear aerodynamic forces and gravity acts on the system. Due to limited accuracy of the dynamic model, the attitude dynamics is conditionally stable where a minimum amount of attitude feedback is required for system stability. To compensate for conditional stability with improved disturbance rejection, an attitude controller is developed adopting cascade control loop architecture. The INS system feedback is used for outer control loop while the gyro feedback is adopted for the inner control loop to attain a high bandwidth, ensuring attitude stability with accelerated response required for a steady hover. The defined controller has introduced corrective control to mitigate the disturbance as sensed by the gyros before they actually do affect the output as the cascade control loop is more responsive than simply the INS loop feedback. In the proposed approach, the robot is modelled using well known “NASA Minimum Complexity Math Model” where robot dynamics is decoupled into Single Input Single Output system. Kalman filter is used to estimate the attitude from the high frequency based gyros aided by INS system feedback data while a matched pole-zero method is used to perform discretization. The stability of the system is evaluated using closed loop identification. The provided solution is tested on Hirobo Scheadu50 model and the system performance is analyzed using the proposed controller.     

Design of a New Fuzzy Suction Controller Using Fuzzy Modeling for Nonlinear Boundary Layer

There are two types of fuzzy modeling: 1) imitating an expert experiment or fulfilling an engineering knowledge, and 2) modeling a complex or unknown system. In this paper, based on the first type of fuzzy modeling, a new fuzzy suction controller (NFSC) is proposed using its linguistic rules to design nonlinear boundary layer. Two kinds of nonlinear boundary layers are discussed. The first kind is designed by three rules derived according to a new interpretation of the switching conditions for a suction controller such that the new controller reduces chattering and spends less energy than a suction controller does. A design procedure summarizes the NFSC design. The second kind of nonlinear boundary layer is the linguistic rules designed to have sliding sectors to control a mobile robot for trajectory tracking. The discussion emphasizes the advantage of nonlinear boundary layers, compared with traditional suction controllers usually using linear boundary. In addition, the proposed NFSC provides a flexible way to adjust the controller functions using linguistic rules based on the first type of fuzzy modeling     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. 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. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.      

基于模糊控制理论的智能雨刷控制器

提出一种基于模糊控制理论的智能雨刷控制器,该系统通过雨量传感器检测雨量大小,然后对雨量传感器的非线性信号X处理,得到电压差分量AX和电压变化量E,作为模糊控制系统的两个输人量,经模糊控制系统输出控制信号U,调节F330单片机的PWM信号控制雨刷器的工作状态;通过在实际模型车中的应用说明了该算法在这类控制系统中是可行的;该系统采用模糊控制器取代传统的PID控制器,无需建立精确的数学模型,克服了非线性因素对系统造成的影响,能明显改善系统的稳态和动态性能.     

一种线性化模糊内模自适应控制算法

针对非线性对象，提出一种线性化模糊内模自适应控制算法。该算法以一组模糊规则作为非线性对象内部模型，一条模糊规则表示一个局部线性系统；根据对象输入与输出测量值，利用TSK建模方法在线辨识局部模糊内部模型；同时依据辨识模型设计局部H2最优模糊控制规则，所有规则构成H2最优模糊控制器。仿真实验显示：该算法适用于非线性对象的控制，具有较好的鲁棒性和抗干扰能力。     

Parameter Identification of Recurrent Fuzzy Systems With Fuzzy Finite-State Automata Representation

This paper presents the identification of nonlinear dynamical systems by recurrent fuzzy system (RFS) models. Two types of RFS models are discussed: the Takagi-Sugeno-Kang (TSK) type and the linguistic or Mamdani type. Both models are equivalent and the latter model may be represented by a fuzzy finite-state automaton (FFA). An identification procedure is proposed based on a standard general purpose genetic algorithm (GA). First, the TSK rule parameters are estimated and, in a second step, the TSK model is converted into an equivalent linguistic model. The parameter identification is evaluated in some benchmark problems for nonlinear system identification described in literature. The results show that RFS models achieve good numerical performance while keeping the interpretability of the actual system dynamics.