Implementation of robust adaptive control for robotic manipulator using TMS320C30

A new adaptive digital control scheme for the robotic manipulator is proposed in this paper. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved Lyapunov second stability analysis based on the adaptive model reference control theory. The adaptive controller consists of the adaptive feedforward and feedback controller and PI type time-varying control elements. The control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.     

空间柔性伸杆机构振动抑制的半物理仿真试验

研究空间柔性伸杆机构的振动抑制问题,提出一种基于伸杆机构自适应指令整形(Adaptive input shaper, AIS)与航天器本体输出反馈控制相结合的复合控制策略,基于经验传递函数估计方法(Empirical transfer function estimate, ETFE),建立最优指令整形器(Optimal arbitrary input shaper, OAIS)的自调整机制,得到改进的自适应指令整形器。以金字塔构型的单框架控制力矩陀螺簇(Single gimbal control moment gyros, SGCMGs)为本体执行机构,详细设计基于实时仿真机、PMAC实时运动控制板卡、SGCMGs等实物的半物理仿真试验平台。在考虑环境干扰力矩以及执行机构控制受限的情况下对控制方法进行半物理仿真试验验证。结果表明：与基于OAIS 及无指令整形前馈控制的复合控制方法相比,提出的控制方法可有效抑制伸杆机构的低频振动,实现航天器本体姿态的精确跟踪和伸杆机构的高精度指向控制。     

Online payload estimation for the control of underactuated mechanical systems

This paper presents a payload estimation scheme for underactuated robotic manipulators with passive joints that are not driven by actuators. In the proposed scheme, only the payload, which can be quite uncertain when a robot performs various tasks, is estimated, because the manipulator’s electrical and other mechanical parameters are generally known in advance. In comparison to other adaptive schemes for underactuated robotic manipulators, the proposed scheme produces satisfactory transient performance and also reduces the computational burden in real-time implementation. The proposed estimation law is also based on the theory of Variable Structure Systems. In contrast to existing adaptation laws that have an integral form, the proposed law estimates uncertain payload using lowpass filtering of a switching signal that is always bounded, which avoids the parameter-drifting problem that is often encountered when using the previous integral laws. Real-time experiments are conducted using an inverted pendulum and the experimental results demonstrate the feasibility and effectiveness of the proposed scheme.     

基于自适应滑模控制器的机械臂运动控制方法

由于无法消除机械臂运动过程中存在的高频振动,导致运动控制方法存在跟踪精度低、控制稳定性差和控制性能差等问题。对此,提出一种基于自适应滑膜控制器的机械臂运动控制方法,在机械臂动力学模型的基础上设计非线性观测器,对机械臂控制系统中存在的干扰信号进行观测,设计自适应滑膜控制器对干扰信号进行补偿。将补偿器引入自适应滑膜控制器中,其主要作用是抑制机械臂在运动过程中存在的高频振动,以提高控制稳定性,通过 Lyapunov 函数设计自适应滑膜控制器的总控制律,根据总控制律利用改进后的自适应滑膜控制器完成机械臂的运动控制。实验结果表明,所提方法的跟踪精度高、稳定性好、控制性能高。     

Discrete-time direct adaptive control for robotic systems based on model-free and if–then rules operation

An adaptive controller for a class of nonlinear discrete-time systems is proposed for robotic systems under the assumption that the parameters and structure of system dynamics are all unknown. This controller is designed with the concept of model-free adaptive control requiring only the input–output of the unknown plant. The robotic system has been generalized to be a nonaffine discrete-time system under reasonable assumptions. The adaptive scheme called fuzzy rules emulated network (FREN) is implemented as a direct controller. The IF–THEN rules for FREN have been defined by the knowledge according to the relation between input and output of the robotic system without any compensator for the unknown mathematical model or nonlinearities. The underlying physical specifications of robotic system such as the operating range, maximum joint velocity, and so on have been considered to initialize the membership functions and adjustable parameters of FREN. The adaptation scheme is developed according to convergence analysis established for both adjustable parameters and the tracking error. The performance of the proposed controller is validated by the experimental system with a 7-degrees-of-freedom robotic arm operated in velocity-mode control.     

漂浮基柔性空间机械臂协调运动的非线性鲁棒自适应控制

针对具有外部扰动与参数不确定或未知的情况,讨论漂浮基柔性空间机械臂系统的协调控制与柔性振动抑制问题。基于假设模态法对柔性臂杆进行近似描述,并利用第二类拉格朗日方程与系统动量守恒关系,推导带外部扰动的漂浮基柔性空间机械臂系统的动力学方程。以此为基础,提出漂浮基柔性空间机械臂系统协调运动的非线性鲁棒控制与非线性鲁棒自适应控制方案。此两种控制方案均可克服外部扰动对系统的影响,控制漂浮基柔性空间机械臂系统载体姿态与机械臂各关节铰完成期望的协调运动,同时有效地抑制了臂杆的柔性振动。除此之外,非线性鲁棒自适应控制方案还可以解决系统惯性参数不确定或未知的问题。系统数值仿真结果证实了所提控制方案的有效性和可行性。     

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.     

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller.     

载体位置、姿态均不受控的飘浮基柔性空间机器人的Terminal滑模控制

讨论了载体位置、姿态均不受控的飘浮基柔性空间机器人的Terminal滑模控制问题。为讨论既有柔性关节又有柔性臂的欠驱动系统,以一个具有两个柔性关节和一个柔性臂的飘浮基空间机器人为例,首先利用拉格朗日方程并结合系统总质心定义,得到了系统的动力学方程,然后利用奇异摄动法,将柔性空间机器人系统分解为一个柔性空间机械臂子系统和一个柔性关节快变子系统。以此为基础,提出了一种包含柔性空间机械臂子控制项和柔性关节快变子控制项的组合控制器。其中,柔性空间机械臂Terminal滑模子控制项实现了机械臂关节铰期望轨迹的跟踪,柔性关节快变子控制项使得快变子系统稳定在由柔性空间机械臂子控制项产生的机械臂关节轨迹上。系统的数值仿真结果表明,该方法能在抑制柔性关节的柔性振动的同时跟踪上机械臂关节期望轨迹。该控制方案的显著优点为不需要测量、反馈载体的位置、移动速度、移动加速度,同时可保证机械臂关节铰跟踪误差在任意指定有限时间内收敛到零。     

Control of a high precision macro-micro robotic manipulator system

A controller for macro-micro robotic manipulator system in which kinematically independent two robotic sub-systems work together to improve the accuracy of the motion is proposed. A nonlinear feedback linearization scheme is employed as basic architecture for the controller and additional formulations about the controller structure are made to assure the robustness of the overall control action and to restrict the motion of micro sub-system close to its nominal position without causing saturation of joints associated with micro-robot.     

具有外部扰动的参数未知漂浮基柔性空间机械臂的对角递归神经网络控制

讨论载体位置和姿态均不受控情况下,存在外部扰动的参数未知漂浮基柔性空间机械臂系统关节运动的动力学控制问题。由假设模态法,利用拉格朗日方法建立漂浮基柔性空间机械臂的系统动力学方程。以此为基础,针对存在外部扰动和系统参数未知的情况,利用对角递归神经网络逼近柔性空间机械臂系统的逆动力学模型,设计基于对角递归神经网络的控制方案,以控制柔性空间机械臂的关节铰跟踪在关节空间的期望运动轨迹,同时能抑制柔性杆的振动。该方案由于没有专门设计主动控制器抑制柔性振动,因此不需要测量、反馈柔性振动模态,大大简化控制器的结构;无需预知控制对象的精确模型和系统参数,且能克服外部扰动的影响。计算机数值仿真证实所提方案的有效性和可行性。     

柔性基、柔性关节空间机械臂的运动混合控制

为解决具有不确定性柔性基、柔性关节空间机械臂的控制问题,提出一种自适应鲁棒终端滑模控制与自适应最优控制相结合的混合控制方案。首先,利用奇异摄动法将系统降阶为慢变子系统及快变子系统;其次,针对慢变系统设计一种由等效控制、鲁棒控制及自适应调节算法组成的轨迹跟踪自适应鲁棒终端滑模控制方案;之后,针对快变系统设计一类基于自适应状态观测的振动最优控制策略。仿真结果表明所提方案在系统刚性、柔性运动控制上的有效性。     

基于 CNN-GRU 的遥操作机器人操作者识别与自适应速度控制方法

传统空间遥操作系统中从端机械臂的运动速度完全取决于操作者的操作速度。为了提高空间遥操作系统的安全性,提出了一种基于操作者操作速度识别的自适应速度控制方法。结合深度学习的理论,提出了一种基于卷积神经网络(CNN)和门控循环单元(GRU)神经网络的融合模型来对操作者的速度进行识别分类。选取了九位受试者构建操作者速度样本库,将操作者的操作速度分为3类,最终识别准确率达到92.71%;并且在此基础上使用串级PID实现从端机械臂的自适应速度控制。实验表明:该模型对新操作者也可以准确识别,同时该模型准确性优于卷积神经网络和循环神经网络(RNN)的融合模型,实时性优于卷积神经网络和长短期记忆(LSTM)神经网络的融合模型;基于该模型的自适应速度控制可以在保证从端机械臂运动轨迹不变的前提下,降低机械臂的末端线速度,有助于提高空间遥操作系统的安全性。     

带滑移铰空间机械臂未端相对轨迹的自适应跟踪控制

讨论了载体位置、姿态均不受控制的带滑移铰空间机械臂的控制问题。结合系统动量守恒关系进行的系统运动学、动力学分析表明,可以得到一组欠驱动形式的空间机械臂系统动力学方程,它们可以表示为一组适当选择的惯性参数的线性函数,由此克服了空间机械臂系统动力学方程关于惯性参数呈非线性函数关系的难点。在此基础上,针对系统中机械臂相关参数未知的情况,设计了空间机械臂末端抓手相对运动轨迹跟踪的自适应控制方案。此控制方案具有不需要测量、反馈漂浮基的位置、移动速度及移动加速度的显著优点,适用于空间站舱内机械手控制系统设计。仿真运算证实了提出的控制方案的有效性。     

Performance analysis of two-degree of freedom fractional order PID controllers for robotic manipulator with payload

The robotic manipulators are multi-input multi-output (MIMO), coupled and highly nonlinear systems. The presence of external disturbances and time-varying parameters adversely affects the performance of these systems. Therefore, the controller designed for these systems should effectively deal with such complexities, and it is an intriguing task for control engineers. This paper presents two-degree of freedom fractional order proportional-integral-derivative (2-DOF FOPID) controller scheme for a two-link planar rigid robotic manipulator with payload for trajectory tracking task. The tuning of all controller parameters is done using cuckoo search algorithm (CSA). The performance of proposed 2-DOF FOPID controllers is compared with those of their integer order designs, i.e., 2-DOF PID controllers, and with the traditional PID controllers. In order to show effectiveness of proposed scheme, the robustness testing is carried out for model uncertainties, payload variations with time, external disturbance and random noise. Numerical simulation results indicate that the 2-DOF FOPID controllers are superior to their integer order counterparts and the traditional PID controllers.     

Robust adaptive motion/force control scheme for crawler-type mobile manipulator with nonholonomic constraint based on sliding mode control approach

In this paper, a robust adaptive motion/force control (RAMFC) scheme is presented for a crawler-type mobile manipulator (CTMM) with nonholonomic constraint. For the position tracking control design, an adaptive sliding mode tracking controller is proposed to deal with the unknown upper bounds of system parameter uncertainties and external disturbances. Based on the position tracking results, a robust control strategy is also developed for the nonholonomic constraint force of CTMM. According to the Lyapunov stability theory, the stability of the closed-loop control system, the uniformly ultimately boundedness of position tracking errors, and the boundedness of the force error and adaptive coefficient errors are all guaranteed by using the derived RAMFC scheme. Simulation and experimental tests on a CTMM with two-link manipulator demonstrate the effectiveness and robustness of the proposed control scheme.     

太空机械臂带故障运行控制研究

为了对出现故障的太空机械臂进行控制,本文先对太空机械臂故障后行为进行动力学分析。在此基础上,得出了太空机械臂带故障运行的控制方案。本文所提出的新控制方案是基于自由转动关节的,也就是说,故障关节完全失去了驱动力矩。为了验证方案的可行性,我们还利用平面两连杆机械臂进行了动力学仿真。     

Robust nonlinear control of a 6 DOF parallel manipulator : Task space approach

This paper presents a robust nonlinear controller for a 6 degree of freedom (DOF) parallel manipulator in the task space coordinates. The proposed control strategy requires information on orientations and translations in the task space unlike the joint space or link space control scheme. Although a 6 DOF sensor may provide such information in a straightforward manner, its cost calls for a more economical alternative. A novel indirect method based on the readily available length information engages as a potential candidate to replace a 6 DOF sensor. The indirect approach generates the necessary information by solving the forward kinematics and subsequently applying alpha-beta-gamma tracker. With the 6 DOF signals available, a robust nonlinear task space control (RNTC) scheme is proposed based on the Lyapunov redesign method, whose stability is rigorously proved. The performance of the proposed RNTC with the new estimation scheme is evaluated via experiments. First, the results of the estimator are compared with the rate-gyro signals, which indicates excellent agreement. Then, the RNTC with on-line estimated 6 DOF data is shown to achieve excellent control performance to sinusoidal inputs, which is superior to those of a commonly used proportional-plus-integral-plus-derivative controller with a feedforward friction compensation under joint space coordinates and the nonlinear controller under task space coordinates.     

并串联复合机器人的鲁棒控制

针对并联机构具有较高的刚度和精度,但工作空间小的特点,提出一种由3自由度平动并联机构和放大机构相结合的机构,从而组成新型并串联复合机器人。根据拉格朗日方法,建立并串联复合机器人的动力学方程, 并讨论动力学方程的性质。研究并串联复合机器人的鲁棒控制问题,根据动力学的性质在笛卡儿坐标系中设计一种鲁棒控制策略。控制器由两部分组成:一部分为基于标称模型设计的计算力矩控制器,其作用是镇定标称的机器人系统;另一部分为一种基于Lyapunov方法设计的鲁棒控制器,其作用是消除不确定性对跟踪性能的影响。理论分析与仿真结果表明,该控制器对改善并串联复合机器人的轨迹跟踪精度十分有效。     

Robust control of a 3-DOF hybrid robot manipulator

This paper introduces a 3-DOF hybrid robotic manipulator which is constructed by combining a parallel mechanism and a pantograph to increase the high stiffness, as well the as workspace. We analyze its kinematics and dynamics by applying Lagrange’s method. Subsequently, we propose a robust tracking controller based on the Lyapunov approach for the manipulator in the presence of uncertainty. The scheme consists of two parts. The first part is the model-based computed torque controller, which is used to stabilize the nominal robot system. The second part is a robust compensating controller, which is designed to handle the uncertainty in the system. We propose a controller designed in Cartesian coordinates. The proposed controller guarantees practical stability.