Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot

Ankle rehabilitation robots have recently attracted great attention since they provide various advantages in terms of rehabilitation process from the viewpoints of patients and therapists. This paper presents development and evaluation of a fuzzy logic based adaptive admittance control scheme for a developed 2-DOF redundantly actuated parallel ankle rehabilitation robot. The proposed adaptive admittance control scheme provides the robot to adapt resistance/assistance level according to patients' disability level. In addition, a fuzzy logic controller (FLC) is developed to improve the trajectory tracking ability of the rehabilitation robot subject to external disturbances which possibly occur due to human-robot interaction. The boundary scales of membership functions of the FLC are tuned using cuckoo search algorithm (CSA). A classical proportional-integral-derivative (PID) controller is also tuned using the CSA to examine the performance of the FLC. The effectiveness of the adaptive admittance control scheme is observed in the experimental results. Furthermore, the experimental results demonstrate that the optimized FLC significantly improves the tracking performance of the ankle rehabilitation robot and decreases the steady-state tracking errors about 50% compared to the optimized PID controller. The performances of the developed controllers are evaluated using common error based performance indices indicating that the FLC has roughly 50% better performance than the PID controller.     

Disturbance observer based multi-variable control of ball mill grinding circuits

Ball mill grinding circuits are essentially multi-variable systems characterized with couplings, time-varying parameters and time delays. The control schemes in previous literatures, including detuned multi-loop PID control, model predictive control (MPC), robust control, adaptive control, and so on, demonstrate limited abilities in control ball mill grinding process in the presence of strong disturbances. The reason is that they do not handle the disturbances directly by controller design. To this end, a disturbance observer based multi-variable control (DOMC) scheme is developed to control a two-input-two-output ball mill grinding circuit. The systems considered here are with lumped disturbances which include external disturbances, such as the variations of ore hardness and feed particle size, and internal disturbances, such as model mismatches and coupling effects. The proposed control scheme consists of two compound controllers, one for the loop of product particle size and the other for the loop of circulating load. Each controller includes a PI feedback part and a feed-forward compensation part for the disturbances by using a disturbance observer (DOB). A rigorous analysis is also given to show the reason why the DOB can effectively suppress the disturbances. Performance of the proposed scheme is compared with those of the MPC and multi-loop PI schemes in the cases of model mismatches and strong external disturbances, respectively. The simulation results demonstrate that the proposed method has a better disturbance rejection property than those of the MPC and PI methods in controlling ball mill grinding circuits.     

立方体机器人自抗扰平衡控制方法

针对立方体机器人动力学模型多变量、强耦合的问题,提出一种基于自抗扰控制的平衡控制器设计方法.引入虚拟控制量,并在控制量与输出向量之间并行地嵌入多个自抗扰控制器,从而实现对多变量系统的解耦控制,将系统的动态耦合和外部扰动视为各自通道上的自抗扰控制器的总扰动,在为期望姿态安排过渡过程基础上,设计扩张状态观测器对总扰动进行估计并实时补偿.综合采用经验试凑法和带宽法对控制器参数进行整定,对自抗扰控制器系统进行稳定控制、姿态跟踪、抗扰性和鲁棒性实验,并与PID控制系统进行定量对比分析.仿真结果表明,所设计的自抗扰控制器不仅能有效实现立方体机器人的平衡控制,而且较PID控制器具有更好的响应速度、控制精度和强鲁棒性.     

不确定遥操作系统带干扰观测器的自适应控制

遥操作系统受到不同类型的不确定性因素影响, 这些不确定性会降低系统的透明性, 甚至会使得系统不稳定. 本文提出了一种带干扰观测器的自适应控制器(adaptive controller with disturbance observer, ACWDO) 用来处理遥操作系统中同时受到的外部干扰和内部动力学参数不确定性. 首先建立了受外部干扰的遥操作系统的非线性动力学模型; 然后分别对主机器人和从机器人设计非线性干扰观测器用来对外部干扰进行估计和补偿; 之后在干扰观测器基础之上分别对主机器人和从机器人设计自适应控制器用来处理内部不确定的动力学参数; 最后再将所设计的ACWDO融入到四通道遥操作系统结构中. 理论分析和仿真结果表明, 所设计的控制器可以取得良好的位置跟踪和力跟踪效果, 确保了遥操作系统的透明性.     

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     

Design and evaluation of disturbance observer algorithm for cable-driven parallel robots

Recently, a cable-driven parallel robots (CDPRs) has been applied to radio telescope as the accurate actuator, that is the five-hundred-meter aperture spherical telescope. It can be affected by the disturbances such as wind, earthquake and so on. Therefore, this paper developed a disturbance observer (DOB)-based control suitable for CDPR. The propose of the control is to reduce the effects of disturbance while the end-effectors maintains the same position even though the disturbance affects it. The key component of the DOB controller is a disturbance observer, which includes inverse nominal plant for each cable. So, a system identification test was carried out firstly. Then, the simulation and experiment were also carried out to evaluate the performance of the algorithm in CDPR. The results showed that the designed DOB algorithm could effectively reduce disturbances.

     

软体机械臂水下自适应鲁棒视觉伺服

水下仿生软体机器人在水底环境勘测,水下生物观测等方面具有极高的应用价值.为进一步提升仿章鱼臂软体机器人在特殊水下环境中控制效果,提出一种自适应鲁棒视觉伺服控制方法,实现其在干扰无标定环境中的高精度镇定控制.基于水底动力学模型,设计保证动力学稳定的控制器;针对柔性材料离线标定过程繁琐、成本高,提出材料参数自适应估计算法;针对水下特殊工作条件,设计自适应鲁棒视觉伺服控制器,实现折射效应的在线补偿,并通过自适应未知环境干扰上界,避免先验环境信息的求解.所提算法在软体机器人样机中验证其镇定控制性能,为仿生软体机器人的实际应用提供理论基础.     

油浸式变压器内部检测球形机器人的深度悬停控制研究

为实现设计的油浸式变压器内部检测机器人在实际作业过程中能针对深度方向某具体点进行观测,对机器人的深度悬停控制进行研究.通过对机器人控制策略的分析,根据水下机器人动力学理论,建立机器人在变压器油特殊介质的动力学模型.基于鲁棒反演控制方法及滑模自适应控制理论,提出一种鲁棒反演滑模控制方法,采用模糊控制器设计滑模面切换增益,以削弱不确定干扰带来的系统抖振,并通过Lyapunov理论分析证明控制器稳定性.解决了机器人在变压器油中因耦合、外界扰动等造成的深度悬停定点过程自旋及抖动问题,仿真及实验表明了所提出控制器的有效性.     

Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.     

基于干扰观测器的水下机器人预定性能控制

针对受外界干扰的水下机器人,提出一个预定性能控制器.首先,针对水下机器人的外界干扰,设计一个干扰观测器并且估计误差在有限时间内收敛至零;然后,利用干扰观测器进行前馈补偿,基于一种指数型障碍李雅普诺夫函数设计一个非奇异快速积分终端滑模控制器,使得水下机器人的轨迹跟踪误差在有限时间内收敛至零并且满足预定的性能要求;最后,严...     

基于线性自抗扰的开关磁阻电机调速控制研究

开关磁阻电机调速系统是复杂的非线性时变系统,负载扰动大,变量之间耦合严重,针对上述系统的性能特点提出采用线性自抗扰控制策略对系统进行控制的方法。首先为克服负载扰动变化,电机磁链呈非线性以及电流、位置等参数耦合的内外部干扰问题,设计扩张状态观测器对系统内扰和外扰进行准确估计并实时补偿。然后设计PD（比例-微分）控制器抑制系统给定与扩张状态观测器反馈的观测对象状态变量之间的跟踪误差。最后在仿真平台上对设计的控制系统进行试验并与传统PID控制方案进行对比,结果显示,对于给定的阶跃信号线性自抗扰控制器只需0.09s即可达到稳态且无超调,而PID控制器需要3s才能实现稳定跟踪。因此相比于传统PID控制,线性自抗扰控制器拥有更优的动静态性能,并且系统在外部负载扰动和内部模型参数变化的情况下也有良好的控制效果,表现出了很好的鲁棒特性。     

采用改进型扰动观测器的控制方法

提出一种适用于非最小相位对象的改进型扰动观测(DOB)结构.通过新增两个控制器,该结构不但对标称性能控制器输出进行动态补偿,而且还补偿了系统输出反馈信号.因此,基于新DOB的控制结构不但能在低频段很好的抑制外部干扰,消除实际被控模型摄动对闭环系统的输出性能的影响,更重要的是提高了抑制高频测量噪声的能力.最后,讨论了该控制结构的内部稳定性和鲁棒稳定性.     

Multivariable sliding mode backstepping controller design for quadrotor UAV based on disturbance observer

This paper deals with the tracking control problem of quadrotor unmanned aerial vehicles (QUAVs) with external disturbances. First, because the QUAV model contains two non-integrity constraints, the dynamic model of the QUAV is decomposed into two subsystems which are independently controlled, so as to reduce controller design complexity. Secondly, the nonlinear disturbance observer (DOB) technique is integrated into a backstepping control method to design the controller for the first subsystem, in which a DOB is applied to estimate the lumped uncertainty. Based on the double power reaching law and the DOB, a multivariable sliding mode control (MSMC) scheme is developed for the second subsystem. Thirdly, based on Lyapunov theory, the closed-loop system is proved to be asymptotically stable. Finally, our comparative simulation results demonstrate that the presented control scheme behaves better in terms of tracking performance than the adaptive backstepping control (ABC) approach.     

基于神经网络PID控制的柔性微机器人系统

根据尺蠖蠕动的原理,研制了一种三自由度微型机器人内窥镜诊疗系统；该机器人由空气压橡胶驱动器驱动，通过两个气囊钳位．建立了机器人的动态模型．基于BP神经网络PID控制策略，设计了电—气脉宽调制伺服系统控制机器人的移动．用系统输出的预测值来代替实测值，计算权系数的修正量，实时改变控制参数以提高控制效果．软件仿真和实验结果都证实该方法弥补了传统PID控制方法的不足，显著改善了系统的静动态特性，是一种理想的气动微型蠕动机器人控制方法．     

Design of robot control systems

An expository discussion on practical aspects of the design problems of robot control systems is presented. The first section discusses the present status of robot control methodology based on the so-called 'teaching and playback' control scheme. It is pointed out that PTP (point to point) control is still central in practice because only a sort of pulse-incremental servo controller is implemented for each joint actuator in actual industrial robots. The second section points out that servo controllers of this kind perform approximately as a PD or PID controller, and demonstrates that such PD and PID control schemes can work well even if the robot dynamics are non-linear and have strong couplings between the joint variables. The third section deals with path-tracking and trajectory-tracking control problems when teaching by human operators is not possible. This is then followed by a final but substantial section on recent results on learning control and adaptive control. An example of learning control for robot motions is given and its potential applicability in robotic systems is discussed.     

Comparison and application of DDC algorithms for a heat exchanger

In recent years, several algorithms for Direct Digital Control (DDC) have been proposed in literature. Although some of these, such as PID or cascade controllers, are very commonly used in industrial applications, the more recent ones like optimal state feedback controllers using an observer or parameter adaptive controllers have rarely been applied to a real plant. The primary difficulty behind this application has been perhaps the lack of testing such algorithms on a pilot plant. Moreover, there has been no serious attempt to make a comparative study of the merits of such algorithms for an existing plant under actual operating conditions. In this paper, seven DDC algorithms are applied to the temperature control of a heat exchanger. These algorithms are: PID, cascade, compensation (pole assignment), deadbeat, half-proportional, adaptive and optimal state feedback controller using an observer. The system performance and sensitivity with respect to changes of the plant parameters, disturbances and set point variations are investigated for the heat exchanger using these algorithms. The results indicate that the more sophisticated algorithms, e.g. optimal state feedback, compensation and adaptive controllers, requiring more computer time and memory, yield relatively less improvement when applied to a low-order plant than do the simple algorithms such as PID or cascade. It was deduced that the PID controller with anti-windup is the most suitable one.     

Autonomous mobile robots navigation using RBF neural compensator

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.     

Robust adaptive control of a bio-inspired robot manipulator using bat algorithm

This paper proposes a novel adaptive fractional order PID sliding mode controller (AFOPIDSMC) using a Bat algorithm to control of a Caterpillar robot manipulator. A fractional order PID (FOPID) control is applied to improve both trajectory tracking and robustness. Sliding mode controller (SMC) is one of the control methods which provides high robustness and low tracking error. Using hybridization, a new combined control law is proposed for chattering reduction by means of FOPID controller and high trajectory tracking through using SMC. Then, an adaptive controller design motivated from the SMC is applied for updating FOPID parameters. A metaheuristic approach, the Bat search algorithm based on the echolocation behavior of bats is applied for optimal design of the Caterpillar robot in order to tune the parameter AFOPIDSMC controllers (BA-AFOPIDSMC). To study the effectiveness of Bat algorithm, its performance is compared with five other controllers such as PID, FOPID, SMC, AFOPIDSMC and PSO-AFOPIDSMC. The stability of the AFOPIDSMC controller is proved by Lyapunov theory. Numerical simulation results completely indicate the advantage of BA-AFOPIDSMC for trajectory tracking and chattering reduction.     

Polynomial family of PD-type controllers for robot manipulators

This paper addresses the problem of position control for robot manipulators. A new polynomial family of PD-type controllers with gravity compensation for the global position of robots manipulators is presented. The previous results on the linear PD controller are extended to the proposed polynomial family. The classical PD controller can be found among this large class of controllers when its proportional gain is a diagonal matrix. The main contribution of this paper is to prove that the closed-loop system composed by full nonlinear robot dynamics and the proposed family of controllers is globally asymptotically stable in agreement with Lyapunov's direct method and LaSalle's invariance principle. Besides the theoretical results, a real-time experimental comparison is also presented to illustrate the performance of the proposed family with other well-known control algorithms such as PD and PID schemes on a three degrees of freedom direct-drive arm.     

A disturbance observer enhanced composite cascade control with experimental studies

The presence of strong disturbances usually causes great performance degradation of industrial process control systems. A disturbance observer (DOB) enhanced composite cascade control consisting of model predictive control (MPC), proportional-integral-derivative (PID) control, and DOB is proposed in this paper. DOB is employed here to estimate the severe disturbances and the estimated values are applied for feed-forward compensation, forming a composite control together with MPC. To evaluate the efficiency and validity of the proposed control structure, the simulation as well as experimental studies have been carried out for a level tank process which represents a typical first-order plus dead-time (FODT) industry process. Both the simulation and experimental results show that the proposed composite control method significantly improves the disturbance attenuation property of the MPC scheme in controlling such a typical industrial process.