轮式机器人滑模轨迹跟踪控制器设计

轮式机器人是一个典型的非完整性系统。由于非线性和非完整特性,很难为移动机器人系统的轨迹跟踪建立一个合适的模型。介绍了一种轮式机器人滑模轨迹跟踪控制方法。滑模控制是一个鲁棒的控制方法,能渐近的按一条所期望的轨迹稳定移动机器人。以之为基础,描述了轮式机器人的动力学模型并在二维坐标下建立了运动学方程,根据运动学方程设计滑模控制器,该控制器使得机器人的位置误差收敛到零。     

Trajectory tracking control for a wheel mobile robot on rough and uneven ground

This paper investigates a dual closed-loop trajectory tracking control strategy for a non-holonomic wheeled mobile robot (WMR) with unknown external disturbances caused by rough and uneven ground. Firstly, a novel dynamic model without coupling is established by introducing a direct current motor model with uncertain parameters. Then a linear extended state observer (LESO) is presented to estimate unknown external disturbance of the WMR. And a dynamic controller based on sliding mode control method is provided in an inner loop to ensure exponential convergence of velocity error while compensating for observational error of the LESO. Considering that the robot’s center of mass and geometric center do not overlap, a kinematic controller is designed in an outer loop to make positional error asymptotically stabilized. Finally, experiments are given to verify performance of tracking and disturbance rejection of the WMR.     

Dynamic triangular neural controller for stepper motor trajectorytracking

We present a novel neural controller for a stepper motor. This controller is developed as follows. Modifying published results for nonlinear identification using dynamic neural networks (NNs), an NN identifier of triangular form is implemented. Then, based on this mode, a control law using sliding modes is derived. This neural identifier and the proposed control law allow trajectory tracking for stepping motors. Applicability of the approach is tested via simulations     

Adaptive neural network based controller for robots

A new Adaptive Neural Network (ANN) controller for robot trajectory trackingproblem is developed. A novel and efficient training algorithm for the proposed controller ispresented in this paper. The proposed training algorithm is based on updating the weights of thenetwork each step by minimizing the quadrant tracking errors and their derivatives.A simulation study is carried out on a polar robot manipulator to assure the effectivenessof the proposed trajectory tracking robot control system. The effects of the new controllerparameters and noisy external load disturbances on the control performance are studied. Thesimulation results of the proposed adaptive ANN controller are compared with those of aconventional ANN controller. The obtained results assured the robustness of the proposed ANNcontroller for: (i) uncertainties of the robot arm dynamic model and/or parameters, (ii) variousnoisy external load disturbances. Also, the simulation results assure the effectiveness of theproposed adaptive ANN controller against the conventional ANN one.     

Model identification and adaptive control of lower limb exoskeleton based on neighborhood field optimization

In lower limb exoskeletons, control performance and system stability of human–robot coordinated movement are often hampered by some model parametric uncertainties. To address this problem, Neighborhood Field Optimization (NFO) is proposed to identify the unknown model parameters of an exoskeleton for the model-based controller design. The excitation trajectory is designed by the NFO algorithm with motion constraints to improve the model identification accuracy. Meanwhile, the Huber fitness function is adopted to suppress the influence of the disturbance points in sampled dataset. Then an adaptive backstepping control scheme is constructed to improve the dynamic tracking performance of human–robot training mode in the presence of the identification error. Via Lyapunov technique and backstepping iteration, all the system state errors of the exoskeleton are bound and converge to zero neighborhood based on the assumption of bounded identified parameter error. Finally, the model identification results and comparative tracking performance of the proposed scheme are verified by an experimental platform of Two-degrees of freedom (DOF) lower limb exoskeleton with human–robot cooperative motion.     

滑模变结构在AUV航向控制中的应用

针对自主式水下机器人的控制特点,建立了机器人的动力学数学模型。利用运动解耦的方法完成了水下机器人完备控制量的构建。在滑模变结构控制理论的基础上,设计了水下机器人的分布式滑模控制系统,并在Simulink下完成滑模控制器的建模。预先设定了仿真过程中机器人的运动轨迹跟踪,结果表明,滑模控制能有效地控制AUV的航向,对外部扰动具有较强的鲁棒性。     

PID-type sliding mode-based adaptive motion control of a 2-DOF piezoelectric ultrasonic motor driven stage

This paper presents a new scheme of adaptive sliding mode control (ASMC) for a piezoelectric ultrasonic motor driven X–Y stage to meet the demand of precision motion tracking while addressing the problems of unknown nonlinear friction and model uncertainties. The system model with Coulomb friction and unilateral coupling effect is first investigated. Then the controller is designed with adaptive laws synthesized to obtain the unknown model parameters for handling parametric uncertainties and offsetting friction force. The robust control term acts as a high gain feedback control to make the output track the desired trajectory fast for guaranteed robust performance. Based on a PID-type sliding mode, the control scheme has a simple structure to be implemented and the control parameters can be easily tuned. Theoretical stability analysis of the proposed novel ASMC is accomplished using a Lyapunov framework. Furthermore, the proposed control scheme is applied to an X–Y stage and the results prove that the proposed control method is effective in achieving excellent tracking performance.     

Development and hybrid force/position control of a compliant rescue manipulator

Performing search and rescue tasks in the ruins after disasters demand rescue robots with slender and compliant structure to accommodate the complicated configurations under debris. This paper presents the structural design and system composition of a novel tendon-sheath actuated compliant rescue manipulator with slender and flexible body. The proposed robot can drill into the narrow space where rescuers and traditional rigid robots cannot get in because of size limitation or toxic environment. The self-sensing calibration, dynamic modeling, and hybrid force/position control trajectory of the compliant gripper with integrated position and force monitoring capabilities are analyzed and discussed. With the aim of regulating the gripper displacement and clamping force during operation, a hybrid force/position control strategy is proposed based on a cascaded proportional-integral-derivative (PID) controller and a fuzzy sliding mode controller (FSMC). Experimental setups mainly consisting of servo motor, tendon sheath transmission components, compliant gripper, and real-time control system are established to calibrate the strain gauge sensors and identify the dynamic model parameters. Further experimental investigations involving force tracking experiments, position tracking experiments, and object grasping experiments are carried out. The experimental results demonstrate the effectiveness of the developed self-sensing approach and control strategies during rescue operation.     

机械臂变指数趋近律滑模控制律设计

针对机械臂滑模控制中存在抖振的问题,采用改进趋近律的思想来设计滑模控制律,以达到有效抑制抖振的目的。在对滑模控制的特点和常用的指数趋近律进行分析的基础上,提出了一种变指数趋近律,并对其趋近性能进行了分析;结合机械臂动力学模型和改进的趋近律设计了相应的滑模控制策略,对其控制效果进行了验证。仿真结果表明,该控制策略不仅有效地抑制了系统的抖振,而且保证了机械臂系统对期望轨迹的快速跟踪性,进而提高了机械臂的工作性能。     

Sliding mode control combined with extended state observer for an ankle exoskeleton driven by electrical motor

A novel sliding mode control combined with extended state observer (ESO) is proposed for an ankle exoskeleton driven by electrical motor. During the process of assisting, it is necessary to design an effective controller for assisting torque of ankle exoskeleton. However, the parameter uncertainty of complex dynamics model and the irregular motion of human ankle may affect the torque control accuracy. For a high control precision of assisting torque when facing the modeling uncertainty, the sliding mode control is employed, but a large switching gain is usually needed in order to suppress the disturbance, which cause the control signal vibrate greatly. ESO can observe and suppress the disturbance and modeling uncertainty, but its tracking performance needs to be improved. Therefore, the proposed complex controller takes the advantages of sliding mode control and extended state observer, which can not only improve torque tracking performance but also overcome the disturbance force caused by the change of human joint angle without increasing chattering of control signal. Experimental studies are carried out to validate the effectiveness of the proposed control. The results show the presented controller have better torque tracking performance and robustness stability, and the proposed controller can reduce the chattering compared with the tradition sliding mode control.     

Neural network impedance force control of robot manipulator

The performance of an impedance controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve the controller robustness by applying the neural network (NN) technique to compensate for the uncertainties in the robot model. NN control techniques are applied to two impedance control methods: torque-based and position-based impedance control, which are distinguished by the way of the impedance functions being implemented. A novel error signal is proposed for the NN training. In addition, a trajectory modification algorithm is developed to determine the reference trajectory when the environment stiffness is unknown. The robustness analysis of this algorithm to force sensor noise and inaccurate environment position measurement is also presented. The performances of the two NN impedance control schemes are compared by computer simulations. Simulation results based on a three-degrees-of-freedom robot show that highly robust position/force tracking can be achieved in the presence of large uncertainties and force sensor noise     

Adaptive robust control of fully-constrained cable driven parallel robots

In this paper, adaptive robust control (ARC) of fully-constrained cable driven parallel robots is studied in detail. Since kinematic and dynamic models of the robot are partly structurally unknown in practice, in this paper an adaptive robust sliding mode controller is proposed based on the adaptation of the upper bound of the uncertainties. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The proposed controller not only keeps all cables under tension for the whole workspace of the robot, it is chattering-free, computationally simple and it does not require measurement of the end-effector acceleration. The stability of the closed-loop system with proposed control algorithm is analyzed through Lyapunov second method and it is shown that the tracking error will remain uniformly ultimately bounded (UUB). Finally, the effectiveness of the proposed control algorithm is examined through some experiments on a planar cable driven parallel robot and it is shown that the proposed controller is able to provide suitable tracking performance in practice.     

A hybrid fuzzy logic and neural network algorithm for robot motioncontrol

Robotic manipulators are multivariable nonlinear coupling dynamic systems. Industrial robots were controlled by using a traditional controller, the control performance of which may change with respect to operating conditions. Since the robotic manipulators have complicated nonlinear mathematical models, control systems based on the system model are difficult to design. In this paper, a model-free hybrid fuzzy logic and neural network algorithm was proposed to control this multi-input/multi-output (MIMO) robotic system. First, a fuzzy logic controller was designed to control individual joints of this 4-degree-of-freedom (DOF) robot. Secondly, a coupling neural network controller was introduced to take care of the coupling effect among joints and refine the control performance of this robotic system. The experimental results showed that the application of this control strategy effectively improved the trajectory tracking precision     

车式移动机器人轨迹跟踪控制方法

针对车式移动机器人轨迹跟踪这一典型控制任务,本文提出一种滑模轨迹跟踪控制方法.该方法采用PI型滑模面设计等效控制律,利用变速函数代替符号函数获得切换控制率,并运用Lyapunov理论证明系统的稳定性.仿真结果表明该方法不但能使机器人有效跟踪任意参考轨迹,而且能减小在控制中的抖振现象,即使在外界干扰影响的情况下,也具有良好的控制品质.     

Adaptive robot control using neural networks

This paper studies the trajectory tracking problem to control the nonlinear dynamic model of a robot using neural networks. These controllers are based on learning from input-output measurements and not on parametric-model-based dynamics. Multilayer recurrent networks are used to estimate the dynamics of the system and the inverse dynamic model. The training is achieved using the backpropagation method. The minimization of the quadratic error is computed by a variable step gradient method. Another multilayer recurrent neural network is added to estimate the joint accelerations. The control process is applied to a two degree-of-freedom (DOF) SCARA robot using a DSP-based controller. Experimental results show the effectiveness of this approach. The tracking trajectory errors are very small and torques expected at manipulator joints are free of chattering.<>     

Control of a 2-DOF ultrasonic piezomotor stage for grommet insertion

The treatment of a common disease called “Otitis Media with Effusion (OME)” involves the surgeon inserting a grommet in the eardrum to bypass the Eustachian tube for draining fluid when medication fails. In this paper, a novel device for myringotomy and grommet insertion is first designed and introduced. Due to the advantages of high precision and fast response, a 2-DOF ultrasonic piezomotor (USM) stage is chosen to provide the motion sequences of the device, especially a precise path tracking during the grommet insertion. This paper briefly presents the mechanical design of the device and the configuration and control of the 2-DOF USM stage. The model of the USM consisting of a linear and nonlinear term is built. A PID controller is used as the main controller and tuned with the help of LQR technique. Since there are nonlinear dynamics caused by friction and hysteresis existing in the system, a nonlinear compensation including a sign function and sliding mode control is designed to reject the nonlinearity. Moreover, a decoupling controller is designed to eliminate the coupling effects between the two USM stages. The experimental results show that the LQR-assisted PID controller with compensation can achieve very good system performance and the decoupling controller can further improve the performance.     

基于最优负载瞬态特性的单相全桥逆变器解耦设计

针对单相全桥工频逆变器设计过程欠缺规范的问题,该文提出了一种以动态设计指标为条件解耦设计主电路滤波参数和控制器参数的公式化分步设计法.以阻性负载跃变量为条件,分析了单相全桥工频逆变器在两种极端情况下的最优负载瞬态响应过程,推导了最优负载瞬态特性关于滤波参数的数学表达式,并给出了表达式适用范围-输出电压跌落量低于10%,且结合逆变器的动态设计指标提出了一种滤波器参数设计方法,再以滑模控制为例,结合动态设计指标给出了一种滑模控制器设计方法.仿真实验结果验证了最优负载瞬态响应过程的存在性,最优负载瞬态特性表达式的正确性和有效性,也验证了解耦设计法的正确性和有效性.该解耦设计法具有良好的理论价值和工程价值.     

Balancing control of a unicycle robot with double gyroscopes using adaptive fuzzy controller

This paper presents a unicycle robot which utilizes the precession effect of a double-gyroscope for lateral balancing and designs an adaptive fuzzy controller to realize the balance control according to its dynamic model. The double gyroscope structure of the unicycle robot can eliminate the pitch angle interference caused by the precession effect and improve the robot's lateral anti-interference ability. An adaptive fuzzy controller is designed based on the dynamic equations of the unicycle robot to improve its robustness. The adaptive controller part improves the anti-interference ability of the unicycle robot, and the fuzzy controller part is used as decoupling controller to reduce the interference of coupling. Simulation and experimental results to verify the anti-interference ability and decoupling effect of the designed controller.     

Trajectory tracking controller of the hybrid robot for milling

The paper presents a model predictive trajectory tracking controller for a five degree of freedom hybrid robot for milling. The construction of the manipulator is introduced, forward and inverse kinematics problems are solved in a closed analytical form, a simplified dynamic model, suitable for control synthesis is described. Control law of the computed torque type is proposed with modifications that improve the performance of trajectory tracking. The control algorithm is tested in experiments conducted on a robot material prototype without actual milling. The presented results are very good for the parallel part of the robot, but require improvement for the serial part. Finally, the conclusions and indications towards future investigations are presented.     

移动机器人全局轨迹跟踪的自适应滑模控制

讨论了两驱动后轮角速度为控制输入的移动机器人轨迹跟踪问题，针对含有未知参数的非完整移动机器人运动学模型．基于反演（backstepping）控制算法的思想设计了变结构控制的切换函数，并由此构造了具有全局渐近稳定的白适应滑模轨迹跟踪控制器。该方法设计过程简单并具有直观的稳定性分析，适用于移动机器人的全局轨迹跟踪控制。仿真结果表明了该方法的有效性和正确性。