Intelligent feedforward control and payload estimation for a two-link robotic manipulator

Conventional model-based computed torque control fails to produce a good trajectory tracking performance in the presence of payload uncertainty and modeling error. The challenge is to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural-network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. An off-line trained feedforward (multilayer) neural network takes payload mass estimates from a fuzzy-logic mass estimator as one of the inputs to represent the inverse dynamics of the articulated robot. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experimental results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty.     

Adaptive synchronization control of a robotic manipulator operatingin an intelligent workcell

The formulation and implementation of a synchronization control scheme applicable to a robotic workcell is described. Such a workcell typically consists of a conveyor system that transports industrial workpieces, a binary camera to recognize the geometric and other characteristics of the workpiece, and a robotic manipulator that is suitably controlled to direct its end effector to achieve a synchronized rendezvous with the workpiece. Subsequent to a successful rendezvous, the robot may pick up the piece (in pick-an-place operations) or perform such other online operations as assembly, processing, or quality inspection. A methodology to ensure rapid rendezvous with accurate tracking is emphasized. Simulation and implementation results are compared and discussed     

Visual control of robotic manipulator based on neural networks

A control scheme for a robotic manipulator system that uses visual information to position and orient the end-effector is described. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position and orientation of the workplace and inverse kinematic calculation. The feature of the control scheme is the use of neural networks for the determination of the change in joint angles required in order to achieve the desired position and orientation. The proposed system is able to control the robot so that it can approach the desired position and orientation from arbitrary initial ones. Simulations for a robotic manipulator with six degrees of freedom are described. The validity and the effectiveness of the proposed control scheme are verified by computer simulations     

Offset-free model predictive control of a soft manipulator using the Koopman operator

The modeling and control of soft manipulators remain challenging due to their inherent complex kinematics and dynamics. This paper presents an offset-free Koopman operator-based model predictive control (OK-MPC) scheme that offers a practical data-driven approach to model and control the soft manipulator in the task space. In this scheme, the Koopman operator is used to derive a straightforward linear model to describe the complex dynamics of a soft manipulator. Then, an offset-free control strategy is incorporated with the Koopman operator to minimize the effect of modeling uncertainties and external disturbance, allowing the precise control of the soft manipulator. The proposed OK-MPC scheme was examined on both a soft pneumatic manipulator and a cable-driven origami manipulator through trajectory tracking experiments. The experimental results showed that the tracking errors decreased by 30.0% in free space and 45.4% in confined space, indicating that the OK-MPC has the capability to improve the control performance of a normal Koopman-based controller. Besides, the OK-MPC is readily available for the two different soft robotic prototypes, demonstrating its general applicability for the modeling and control in the field of soft robotics.     

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.     

Influence of electrode position on the characterization of artery stenotic plaques by using impedance catheter

Use of balloon impedance catheters (BIC) for the characterization of plaques in vessels can support an optimal medical treatment of plaques. The sensitivity of impedance diagnoses with BIC is related with the distribution of electric fields determined by the electrode configuration. Using the three-dimensional finite element method (FEM) simulation, it was estimated how the relative positions of electrode array to the lipid in the vessel affect on the total impedance magnitude. Further, the short-circuiting effect was investigated with respect to the separation distance on the angular axis between the electrode arrays of angular set. By aid of FEM simulations, it is possible to design the sets of multielectrode arrays which have an optimized resolution for individual vessels.     

Motion control of a tendon-based parallel manipulator using optimal tension distribution

This paper presents the motion control of a six degree-of-freedom tendon-based parallel manipulator, which moves a platform with high speed using seven cables. To control the motion of the platform along desired trajectories in space, nonlinear feedforward control laws in the cable length coordinates are used. Taking account of the effect of redundancy on actuation, the optimal tension distribution should be considered to the advantage of the control laws. Using a method based on the analysis of the workspace condition, tension constraints and limiting torque constraints of actuators, an analytical solution for optimum tension distribution was found and used to compute the force in each cable for compensation of dynamic errors. It is experimentally demonstrated that the proposed control laws reduce the energy consumption of the actuators and satisfy the path tracking accuracy.     

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input–output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward–feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized Prandtl–Ishlinskii model. An adaptive model reference temperature control system is cascaded to the inverse hysteresis model in order to estimate the SMA electrical current for tracking the reference signal. In addition, a closed-loop proportional–integral controller with position feedback is added to the feedforward controller to increase the accuracy as well as eliminate the steady state error in position control process. Experimental results indicate that the proposed controller has great accuracy in tracking some square wave signals. It is also experimentally shown that the suggested controller has precise tracking performance in presence of environmental disturbances.     

Active vibration control of a flexible one-link manipulator using a multivariable predictive controller

This article presents a new application of model-based predictive controller (MPC) for vibration suppression of a flexible one-link manipulator using piezoceramic actuators. Simulation and experimental studies were conducted to investigate the applicability of the MPC strategy to control vibration of the flexible structure having multiple inputs and multiple outputs (MIMO). The performance of the proposed technique was assessed in terms of level of vibration reduction. The results demonstrated that the proposed predictive control strategy is well suited for multivariable control of vibration suppression on flexible structures.     

Interaction control of an industrial manipulator using LPV techniques

This paper presents the design and implementation of a hybrid force/motion control scheme on a six-degrees-of-freedom robotic manipulator employing a gain-scheduled linear parameter-varying (LPV) controller. A nonlinear dynamic model of the manipulator is obtained and the unknown parameters are estimated. The manipulator is decomposed into an inner and a wrist submodel, and a practical way is proposed to investigate the coupling between them. The motion control part of the hybrid controller which is the main focus of this paper is formed by a combination of an LPV controller and a model-based inverse dynamics controller for the inner submodel and the wrist joints, respectively. A quasi-LPV model with a reduced number of scheduling parameters is derived for the inner submodel, and a polytopic LPV gain-scheduled controller is synthesized in a two-degrees-of-freedom structure including feedback and feedforward parts, which is augmented by a friction compensation term. A PD controller with a feedforward path is designed to control the interaction force. The proposed hybrid force/motion scheme is implemented on the 6-DOF CRS A465 robotic manipulator to perform a writing task. Comparison of the results with those of a hybrid force/motion controller with a plain model-based inverse dynamics motion control and the same force control shows that the proposed controller improves the position tracking performance significantly.     

Trajectory control of robotic manipulators using neural networks

The authors present a nonlinear compensator using neural networks for trajectory control of robotic manipulators. The neural networks are not used to learn inverse-dynamics but to compensate nonlinearities of robotic manipulators. The performance of the proposed neural network controller is compared with that of the adaptive controller proposed by J.J. Craig (1988), and the effectiveness of the proposed neural network controller in compensating the unstructured uncertainties is clarified. A learning scheme using a model of known dynamics of manipulators is also proposed. The model learning can be done offline and needs no data recording of actual manipulator operation     

Task based kinematic design of a two DOF manipulator with a parallelogram five-bar link mechanism

As the demand for modular manipulators or special purpose manipulators has increased, task based design to design an optimal manipulator for given tasks become more and more important. However, manipulator design problems usually are very complex due to a large number of design parameters, their nonlinear and implicit relationship, and so on. To achieve task based design successfully, it is necessary to develop a methodology that can solve the complexity. This paper addresses how to determine the kinematic parameters of a two degrees of freedom manipulator with a parallelogram five-bar link mechanism from a given task, namely, how to map a given task into the kinematic parameters. With a simplified example of designing a manipulator with a five-bar link mechanism, a methodology for task based design is presented. And it introduces formulations of task specifications and manipulator specifications, and presents a new dexterity measure as an optimality criterion for manipulator design. Also it finds out an optimal design solution for the manipulator by using a genetic algorithm that has robust search performance in complex spaces.     

基于激光测距的微型机械臂关节控制模型

为了更加深入探究并提升机械臂关节控制效果,提出一种基于激光测距的微型机械臂关节控制模型,通过激光测距技术对微型机械臂周边环境进行感知,同时进行数据点扫描,将全部数据点集进行直线分割以及拟合等相关操作,获取二维环境地图。在上述基础上,通过坐标变换矩阵获取微型机械臂的正运动学模型,利用正运动学模型得到机械臂逆运动学模型。将获取的动力学模型转换为仿射非线性系统的形式,利用输入输出反馈线性化方法选取合适的状态变换和反馈变换,通过滑膜控制方法构建微型机械臂关节控制模型,采用模型进行关节控制。仿真实验结果表明,所提模型可以获取理想的微型机械臂关节控制结果。     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

Tool-Center-Point control of the KAI manipulator using constrained QP optimization

The KAI manipulator is a four joint mobile manipulator, which will be used within the German road clearance package to investigate improvised explosive devices and ordnance from within an armored vehicle. To improve handling of the manipulator, a Tool-Center-Point (TCP) control is implemented. By using constrained quadratic optimization (cQP) it is possible to allow for the control of the manipulator within three different operating spaces. The QP is formulated to account for constraints in the joint angular rates and TCP velocities, as well as additional velocity constraints, e.g. on the movement of the center of mass of the manipulator. The proposed algorithm is able to handle redundant as well as non redundant manipulator kinematics. By using an efficient QP solver the algorithm can be used within a real-time trajectory generation scheme. The performance of the algorithm is demonstrated using simulation results and validated by measurements of the TCP control.     

Path tracking control of a manipulator considering torquesaturation

When the minimum-time trajectory of a manipulator along a geometrically prescribed path is planned taking into consideration the manipulator's dynamics and actuator's torque limits, at least one of the joints should be at the torque limit. The execution of such a trajectory by a conventional feedback control scheme results in torque saturation. Consequently, the tracking error cannot be suppressed and the manipulator may deviate from the desired path. In this paper, the author's propose a feedback control method for path tracking which takes the torque saturation into account. Based on the desired path, a coordinate system called path coordinates is defined. The path coordinates are composed of the component along the path and the components normal to the path. The equation of motion is described in terms of the path coordinates. Control of the components normal to the path is given priority in order to keep the motion of the manipulator on the path. Simulations of a two-degree-of-freedom manipulator show the effectiveness of this method     

Online electromyographic control of a robotic prosthesis

This paper presents a two-part study investigating the use of forearm surface electromyographic (EMG) signals for real-time control of a robotic arm. In the first part of the study, we explore and extend current classification-based paradigms for myoelectric control to obtain high accuracy (92-98%) on an eight-class offline classification problem, with up to 16 classifications/s. This offline study suggested that a high degree of control could be achieved with very little training time (under 10 min). The second part of this paper describes the design of an online control system for a robotic arm with 4 degrees of freedom. We evaluated the performance of the EMG-based real-time control system by comparing it with a keyboard-control baseline in a three-subject study for a variety of complex tasks.     

Dynamic feed-forward control of a parallel kinematic machine

This paper deals with the dynamic feed-forward control of a 6-UPS parallel kinematic machine (PKM). The control system consists of the closed-loop control subsystem and the dynamic control subsystem. The closed-loop control subsystem is constructed by using the PD control and low-pass filter. Moreover, zero phase error tracking control (ZPETC) is introduced to the closed-loop control subsystem to improve its response capability. Based on the rigid-body dynamic model of the PKM, the dynamic control subsystem is designed by using the computed torque control and feed-forward control. Since the phase lag of the closed-loop control subsystem is almost eliminated by ZPETC and PD control, the motion precision of the closed-loop control subsystem is improved largely. The influence of the dynamic characteristic of the PKM on the motion precision is decreased due to the introduction of rigid-body dynamic model into the control system. As a result, the motion precision of the moving platform is greatly improved.     

An onboard-eye-to-hand visual servo and task coordination control for aerial manipulator based on a spherical model

Compared with the unmanned aerial vehicle (UAV), the aerial manipulator extends the ability to interact with the environment by using the onboard manipulator. The complex dynamic characteristics of the combined system increase the challenges of accurately control the aerial manipulator to approximate the target object. In this paper, an onboard-eye-to-hand visual servo and task coordination control strategy is proposed for the aerial manipulator to enhance the accurate manipulation and flight stability. By establishing a new spherical coordinate error equation, the flying platform (quadrotor) and onboard manipulator can be controlled simultaneously. Meanwhile, the multi-task coordinated control scheme is adopted to achieve precise positioning and grasping of the target object, including estimating the object pose, dynamically compensating the change of the center of gravity of the aerial manipulator, and avoiding the onboard manipulator joint limitations. The stability of the closed-loop system is analyzed by the Lyapunov method. Finally, the feasibility of the proposed visual servo method is verified in simulations and real-world outdoor experiments, respectively. The strengths of the proposed method are demonstrated by comparing it with the conventional visual servo method.