3-D Locomotion control for a biomimetic robot fish

This paper concerns with 3-D locomotion control methods for a biomimetic robot fish. The system architecture of the fish is firstly presented based on a physical model of carangiform fish. The robot fish has a flexible body, a rigid caudal fin and a pair of pectoral tins, driven by several servomotors. The motion control of the robot fish are then divided into speed control, orientation control, submerge control and transient motion control, corresponding algorithms are detailed respectively. Finally, experiments and analyses on a 4-link, radio-controlled robot fish prototype with 3-D locomotion show its good performance.     

Integrated Fuzzy Robust Controller for Flight Control Design

μ-synthesis is a practical design approach and has been applied successfully to achieve a nominal and robust performance objectives. However, this design method suffers from the complexity of its practical implementation and high computational demand due to its high order dynamics. To overcome this problem, the interaction between fuzzy logic control which is a part of intelligence control theory and μ-synthesis controller is carried out. This is called integrated fuzzy robust controller in this paper. It is obtained by coupling fuzzy pd with μ-synthesis controller through the outer loop. Using this design strategy, we can keep the system performance and robustness even a high order μ-synthesis controller is reduced into second order model. In order to test the effectiveness of this design method, the linear simulation results for a launch vehicle’s attitude control motion are presented at the end of this paper.     

An adaptive series control for an interior permanent magnet synchronous motor with actuator compensation

An adaptive series speed control system for an interior permanent magnet synchronous motor (IPMSM) drive is presented in this paper. This control system consists of a current and a speed control loop, and it is intended to improve the drive’s speed tracking performance as well as to compensate for voltage distortions caused by non-ideal characteristics of the drive’s actuator, which is a voltage source inverter (VSI). To achieve these goals, a simple model that captures these characteristics of the VSI is developed and embedded in the motor’s electrical model. Then, based on the resulting model, an adaptive proportional-integral (PI) control for the current loops is designed, allowing for state regulation and actuator compensation. Additionally, to improve the drive’s speed tracking performance, a proportional-model-reference adaptive controller (MRAC) is designed for the speed loop. Techniques from machine learning are used for designing the MRAC to effectively address nonlinearities and uncertainties in the speed dynamic. Finally, simulation results are presented to illustrate the outstanding performance of the proposed multi-loop controller.     

Fuzzy logic for large mining bucket wheel reclaimer motion control——from an engineer's perspective

The bucket wheel reclaimer(BWR) is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i.e.iron ore and coal) in places such as ports,iron-steel plants,coal storage areas,and power stations from stockpiles.BWRs are very large in size,heavy in weight,expensive in price,and slow in motion.There are many challenges in attempting to automatically control their motion to accurately follow the required trajectories involving uncertain parameters from factors such as friction,turbulent wind,its own dynamics,and encoder limitations.As BWRs are always heavily engaged in production and cannot be spared very long for motion control studies and associated developments,a BWR model and simulation environment closely resembling real life conditions would be beneficial.The following research focused mainly on the implementation of fuzzy logic to a BWR motion control from an engineer’s perspective.First,the modeling of a BWR including partially known parameters such as friction force and turbulence to the system was presented.This was then followed by the design of a fuzzy logic-based control built on a model-based control loop.The investigation provides engineers with an example of applying fuzzy logic in a model based approach to properly control the motion of a large BWR following defined trajectories,as well as to show possible ways of further improving the controller performance.The result indicates that fuzzy logic can be applied easily by engineers to overcome most motion control issues involving a large BWR.     

Decentralized Networked Control System Design Using Takagi-Sugeno (TS) Fuzzy Approach

This paper proposes a new method for control of continuous large-scale systems where the measures and control functions are distributed on calculating members which can be shared with other applications and connected to digital network communications.At first, the nonlinear large-scale system is described by a Takagi-Sugeno(TS) fuzzy model. After that, by using a fuzzy LyapunovKrasovskii functional, sufficient conditions of asymptotic stability of the behavior of the decentralized networked control system(DNCS),are developed in terms of linear matrix inequalities(LMIs). Finally, to illustrate the proposed approach, a numerical example and simulation results are presented.     

Adaptive walking control of quadruped robots based on central pattern generator （CPG） and reflex

This paper presents a central pattern generator (CPG) and vestibular reflex combined control strategy for a quadruped robot. An oscillator network and a knee-to-hip mapping function are presented to realize the rhythmic motion for the quadruped robot. A two-phase parameter tuning method is designed to adjust the parameters of oscillator network. First, based on the numerical simulation, the influences of the parameters on the output signals are analyzed, then the genetic algorithm (GA) is used to evolve the phase relationships of the oscillators to realize the basic animal-like walking pattern. Moreover, the animal’s vestibular reflex mechanism is mimicked to realize the adaptive walking of the quadruped robot on a slope terrain. Coupled with the sensory feedback information, the robot can walk up and down the slope smoothly. The presented bio-inspired control method is validated through simulations and experiments with AIBO. Under the control of the presented CPG and vestibular reflex combined control method, AIBO can cope with slipping, falling down and walk on a slope successfully, which demonstrates the effectiveness of the proposed walking control method.     

Type-2 fuzzy control for a flexible-joint robot using voltage control strategy

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.     

Adaptive sliding mode-like fuzzy logic control for nonlinear systems

Sliding mode-like fuzzy logic control （SMFC） algorithm for nonlinear systems is presented in this paper. Firstly dead zone parameters of sliding mode control （SMC） are selftuned by proper adaptive laws and then combined into fuzzy logic system （FLS） to compose the opportune fuzzy logic control （FLC）, which is equivalent to the predesigned SMC controller with self-tuning parameters. Robustness and invariance to the uncertainties of the closed-loop systems are improved and chattering of the SMC is eliminated. Finally simulation results of numerical examples show that the proposed control algorithm is efficient and feasible.     

应用信度分配的模糊CMAC实现非线性系统的容错控制

The adaptive fault-tolerant control scheme of dynamic nonlinear system based on the credit assigned fuzzy CMAC neural network is presented. The proposed learning approach uses the learned times of addressed hypercubes as the credibility, the amounts of correcting errors are proportional to the inversion of the learned times of addressed hypercubes. With this idea, the learning speed can indeed be improved. Based on the improved CMAC learning approach and using the sliding control technique, the effective control law reconfiguration strategy is presented. Thesystem stability and performance are analyzed under failure scenarios. The numerical simulation demonstrates the effectiveness of the improved CMAC algorithm and the proposed fault-tolerant controller.     

Sampled-Data Asynchronous Fuzzy Output Feedback Control for Active Suspension Systems in Restricted Frequency Domain

This paper proposes a novel sampled-data asynchronous fuzzy output feedback control approach for active suspension systems in restricted frequency domain.In order to better investigate uncertain suspension dynamics,the sampleddata Takagi-Sugeno(T-S)fuzzy half-car active suspension(HCAS)system is considered,which is further modelled as a continuous system with an input delay.Firstly,considering that the fuzzy system and the fuzzy controller cannot share the identical premises due to the existence of input delay,a reconstructed method is employed to synchronize the time scales of membership functions between the fuzzy controller and the fuzzy system.Secondly,since external disturbances often belong to a restricted frequency range,a finite frequency control criterion is presented for control synthesis to reduce conservatism.Thirdly,given a full information of state variables is hardly available in practical suspension systems,a two-stage method is proposed to calculate the static output feedback control gains.Moreover,an iterative algorithm is proposed to compute the optimum solution.Finally,numerical simulations verify the effectiveness of the proposed controllers.     

Development of a biomimetic robotic fish and its control algorithm

This paper is concerned with the design of a robotic fish and its motion control algorithms. A radio-controlled, four-link biomimetic robotic fish is developed using a flexible posterior body and an oscillating foil as a propeller. The swimming speed of the robotic fish is adjusted by modulating joint's oscillating frequency, and its orientation is tuned by different joint's deflections. Since the motion control of a robotic fish involves both hydrodynamics of the fluid environment and dynamics of the robot, it is very difficult to establish a precise mathematical model employing purely analytical methods. Therefore, the fish's motion control task is decomposed into two control systems. The online speed control implements a hybrid control strategy and a proportional-integral-derivative (PID) control algorithm. The orientation control system is based on a fuzzy logic controller. In our experiments, a point-to-point (PTP) control algorithm is implemented and an overhead vision system is adopted to provide real-time visual feedback. The experimental results confirm the effectiveness of the proposed algorithms.     

仿生机器鱼胸/尾鳍协同推进闭环深度控制

为改善机器鱼定深控制过程中的动态性能与稳态性能,根据深度误差的大小将定深控制过程分解为趋近阶段与巡游阶段,给出了一种基于中枢模式发生器（central pattern generator,CPG）与模糊控制相结合的闭环运动控制方法．为此,首先建立了以压力传感器信号为反馈输入,通过模糊控制器调节控制参数的CPG运动控制模型．在此基础上,针对误差较大的趋近阶段,采用胸/尾鳍协同方式,通过趋近模糊控制器改变摇翼关节的偏置量与幅值来使机器鱼快速到达期望深度;针对误差较小的巡游阶段,采用改变攻角方式,通过巡游模糊控制器改变胸鳍攻角来使机器鱼保持在期望深度．两阶段之间通过胸鳍CPG的启停实现切换．模糊控制器设计时利用了基于最小二乘法对实验数据拟合而得出的俯仰角变化率与控制参数的近似关系,提高了机器鱼趋向期望深度的速度并减小了在期望深度巡游时的稳态误差．仿真与实验结果验证了所提控制方法的有效性．     

下肢携行外骨骼系统模糊自适应位置控制研究

研究下肢携行外骨骼系统稳定性控制问题。下肢携行外骨骼系统支撑行走阶段位置控制存在稳定性、实时性不足,提高位置控制的速度及精度,使具有适应性和鲁棒性,为了克服具有实时重力补偿的传统方法,提出固定重力补偿的位置控制方法,并利用模糊推理逻辑可逼近非线性函数的特点,将模糊控制算法与具有固定重力补偿的传统PD控制算法相结合,提出了具有固定重力补偿的模糊自适应位置控制算法,应用到下肢携行外骨骼支撑行走阶段的位置控制中。仿真结果表明控制方法能够使外骨骼准确迅速跟踪人体运动,并能够显著减小人所施加的力矩,系统鲁棒性较强,能够适应负载的变化,证明是一种行之有效的控制方法。     

一种基于视觉的仿生机器鱼实时避障综合方法

提出了一种基于视觉的仿生机器鱼实时避障综合方法,该方法基于HIS颜色模型,利用MMX指令和SSE指令,采用并行处理算法实现图像的快速处理;然后,基于栅格法建立环境模型,利用势场法获得优化路径规划策略,在此基础上,采用模糊方法对机器鱼的运动方向进行控制,利用速度分布函数对机器鱼的运动速度进行控制,实验结果表明了所提方法的有效性和可达性。     

基于动态遗忘因子递推最小二乘算法的船舶航向模型辨识

为提高遗忘因子递推最小二乘（RLS）算法辨识船舶航向运动数学模型参数的快速性和鲁棒性,在分析遗忘因子大小对算法特性影响的基础上,提出一种基于模糊控制的动态遗忘因子RLS算法。该算法从理论模型输出与实际模型输出之间的残差入手来构造评估参数辨识误差大小的评价函数,并将评价函数及其变化率作为模糊控制器的输入,利用模糊控制器结合制定的规则表进行模糊推理并计算遗忘因子的修正量,从而实现遗忘因子的动态调整。仿真结果表明,与恒定遗忘因子RLS算法的对比,该算法能够根据参数辨识误差实时调整遗忘因子的大小,使算法在模型参数平稳时有更高的辨识精度,在模型参数突变时有更快的收敛速度,验证了所提算法的优越性。     

基于模糊控制的仿生机器鱼转向控制研究

针对一类微小型仿生机器鱼的转向控制问题,结合其自身运动的特点,提出一种基于模糊控制的转向控制算法.根据实验室经验建立模糊控制规则,运用Mamdani推理,构造出控制响应表.通过避障问题对算法进行验证,仿真结果表明,该方法对机器鱼的转向控制有效,可行且能满足实时性的要求.     

基于TwinCat的控制器在液位控制中的应用

介绍一种基于TwinCat控制平台的水箱智能控制系统,用以提高化工生产中水罐液位的控制速度.该系统主要利用TwinCat控制平台,完成下位机与上位机的数据通信;采用自学习模糊控制和PI控制相结合的智能控制算法.用VC++语言完成控制算法程序,采集下位机的监控数据,改变模糊控制规则,实现对水箱的液位控制.另外,通过编程完成了上位机监控界面的设计.实验结果表明,与模糊控制或PI控制相比,该设计控制效果较好,证明了算法的有效性.     

双轮自平衡机器人行走伺服控制算法

为解决双轮自平衡机器人行走伺服控制问题,针对其动力学特性,提出使用分层模糊控制的方法对双轮自平衡机器人运动进行控制,并且设计一种基于mamdani型模糊推理规则的模糊控制器.使用这种模糊控制器在双轮自平衡机器人硬件平台上完成了2个实验.一是以恒定倾斜角行走为控制目标的行走伺服控制;二是以恒定速率行走为控制目标的行走伺服控制.实验结果表明,设计的模糊控制器模糊规则简洁,可以很好地解决双轮自平衡机器人行走伺服控制问题.     

基于最优保留遗传算法的非线性模糊预测控制

针对传统的模型预测控制不能很好解决具有严重非线性、不确定性的对象或过程的控制问题，提出将模糊模型用于描述对象的非线性动态特性，通过将模糊模型的输出反馈作为模型输入，从而构成了模糊多步预测器。采用一种收敛精度高、速度快的具有最优保留特性遗传算法(EGA)，依据模型预测输出在线滚动求解控制律的非线性预测控制算法。仿真结果表明该算法对一类非线性系统具有较快的响应速度和较强的抗干扰能力。