Development and control of a large range XYΘ micropositioning stage

The recent developments in micro/nano-positioning technologies have highlighted the demand for compact large range three-degrees-of-freedom (3-DOF) XYΘ mechanisms for applications such as sample positioning in nanoimprint lithography, scanning probe microscopy, precision machining, and many more. However, this type of mechanisms suffers from a large footprint, sensing difficulties, and low motion accuracy due to the cross-coupling errors. In this paper, a compact design is proposed to achieve large workspace and high motion accuracy. Prismatic-Prismatic-Revolute (PPR) joints were used to construct this mechanism to yield deterministic large range motions. Laser-based measurement technique based on retroreflectors is proposed to sense large translations and rotation simultaneously with nanometer resolution. A prototype of the proposed mechanism was fabricated to investigate the static and dynamic properties of its structure, and compare these with the computational results. The motion accuracy of the mechanism was improved by using a sliding mode controller based on a nonlinear disturbance observer. The cross-coupling effects and modelling uncertainties were estimated and compensated in this control scheme, which consequently improved the tracking performance. The experimental results showed that the proposed design achieved large workspace, high resolution, improved tracking performance, and required level of compactness as compared with other designs reported in the literature.     

Position control of a Stewart platform using inverse dynamics control with approximate dynamics

Configuration-dependent nonlinear coefficient matrices in the dynamic equation of a robot manipulator impose computational burden in real-time implementation of tracking control based on the inverse dynamics controller (IDC). However, parallel manipulators such as a Stewart platform have relatively small workspace compared to serial manipulators. Based on the characteristics of small motion range, nonlinear coefficient matrices can be approximated to constant ones. The modeling errors caused by such approximation are compensated for by H_∞ controller that treats the error as disturbance. The proposed IDC with approximate dynamics combined with H_∞ control shows good tracking performance even for fast tracking control in which computation of full dynamics is not easy to implement.     

Computed force and velocity control for spatial multi-DOF electro-hydraulic parallel manipulator

A novel dynamic trajectory tracking controller for spatial 6-DOF electro-hydraulic parallel manipulator considering system nonlinearity-computed force and velocity controller is proposed, with a view of improving the control performance with high computational efficiency of control algorithm. The dynamic model of electro-hydraulic parallel manipulator, both mechanical and hydraulic system, is described by using Kane and hydromechanics method. The requisite system states are estimated via forward kinematics based upon global Newton–Raphson with monotonic descent algorithms under the measured actuator position. The desired leg position and velocity required for the proposed controller are calculated by an analytical method corresponding to the desired generalized pose, and the desired driven force is computed with an effectively simplified inverse dynamics. Under feed-forward of the desired driven force and velocity, the computed force and velocity controller is developed with actual leg position as its feedback only, and the desired leg position, velocity and driven force as its input. The control performance of the proposed controller for multi-DOF parallel manipulator is evaluated in theory and experiment, especially for dynamic tracking performance. Experimental results show that the presented controller can greatly improve the dynamic trajectory tracking performance for high real time electro-hydraulic parallel manipulator.     

Precision tracking control of a biaxial piezo stage using repetitive control and double-feedforward compensation

This article presents precision tracking control of an XY piezo stage using repetitive control and double-feedforward compensation. The XY piezo stage is composed of two piezoelectric actuators within a leaf spring mechanism. The study applies two feedback controllers, a Proportional-Integral-Derivative controller and a repetitive controller, to achieve precision trajectory tracking and evaluate performance against benchmarks. Moreover, the investigation applies a double-feedforward compensation approach that integrates a Zero-Phase-Error-Tracking-Controller and an adaptive plant inversion compensator adapted by a Least-Mean-Square algorithm, based on an inverse Prandtl-Ishlinskii model, to improve tracking control performance further. Performance analysis and comparison of the experimental results demonstrate that the proposed control structure improves dynamic tracking accuracy of the XY piezo stage.     

Adaptive robust precision motion control of linear motors withnegligible electrical dynamics: theory and experiments

This paper studies the high performance robust motion control of an epoxy core linear motor, which has negligible electrical dynamics due to the fast response of the electrical subsystem. A discontinuous projection based adaptive robust controller (ARC) is first constructed. The controller theoretically guarantees a prescribed transient performance and final tracking accuracy in general, while achieving asymptotic tracking in the presence of parametric uncertainties. A desired compensation ARC scheme is then presented, in which the regressor is calculated using the reference trajectory information only. The resulting controller has several implementation advantages such as less online computation time, reduced effect of measurement noise, a separation of robust control design from parameter adaptation, and a faster adaptation rate. Both schemes are implemented and compared on an epoxy core linear motor. Extensive comparative experimental results are presented to illustrate the effectiveness and the achievable control performance of the two ARC designs     

Error analysis and control scheme for the error correction in trajectory-tracking of a planar 2PRP-PPR parallel manipulator

This paper presents the end-effector pose error modeling and motion accuracy analysis of a planar 2PRP-PPR parallel manipulator with an unsymmetrical (U-shape) fixed base. The error model is established based on the screw theory with considerations of both configuration (geometrical) errors and joint clearances. It also proposes a robust cascaded control scheme for the end-effector pose (task-space) error correction in trajectory-tracking of the manipulator due to mechanical inaccuracies. The proposed control scheme uses redundant sensor feedback, i.e., individual active joint displacements and velocities and, end-effector positions and orientation are obtained as feedback signals using appropriate sensors. To demonstrate the efficacy and show complete performance of the proposed controller, real-time experiments are accomplished on an in-house fabricated planar 2PRP-PPR parallel manipulator.     

Nonlinear computed torque control for a high-speed planar parallel manipulator

A new computed torque (CT)-type controller termed nonlinear CT (NCT) controller is developed and applied to a high-speed planar parallel manipulator. The NCT controller is designed by replacing the linear PD in the conventional CT controller with the nonlinear PD (NPD) algorithm. The stability of the parallel manipulator system with the NCT controller is proven using the Lyapunov theorem, and the proposed controller is further proven to guarantee asymptotic convergence to zero of both tracking error and error rate. The superiority of the proposed NCT controller is verified through the trajectory tracking experiments of an actual high-speed planar parallel manipulator, and the experiment results are compared with the CT controller.     

Dynamic modeling and control of a 3-DOF Cartesian parallel manipulator

Dynamic modeling is the basic element for controller design of mechanisms. In this paper, an effective dynamic equation of a 3-DOF translational parallel manipulator for control purpose has been derived by the Lagrange-D’Alembert formulation. The structural properties of the derived dynamic equation were proved so that the vast control strategies developed for the serial counterparts can be easily extended for controlling the CPM (Cartesian parallel manipulator). The derived model also leads to decoupling dynamic characteristics, by which the complexity of the controller design can be significantly reduced. Based on this approach, a model-based computed torque method for positioning control of the CPM is illustrated. Both simulated and experimental results show that the model-based controller can achieve high positioning performance. Furthermore, it is shown that the coupling forces from other limbs play significant roles in the force components of the total dynamics of the CPM.     

Application of robust iterative learning algorithm in motion control system

Robustness issue is considered to be one of the major concerns in application of the iterative learning control in motion control systems. The robustness in servo systems is related to parameter uncertainties and noise accumulation. In this paper, both parameter uncertainties and noise are considered in derivation of the error dynamic equation of the ILC algorithm. Based on the error dynamics, the H_∞ framework is utilized to design the robust learning controller. An optimization design process in selecting the proper learning gain and determining the learning function is proposed to ensure that both tracking performance and convergence condition are achieved. Simulations and experiments are conducted to validate the robust learning algorithm which can be applied efficiently to machine tools with the payload varying from 0 to 20 kg. The experimental results demonstrate that the proposed method improves the tracking and contouring performances significantly when performing a complex NURBS curve on a three-axis milling machine.     

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.     

Influence of design parameters on the effectiveness of friction isolators in mitigating pre-motion friction in mechanical bearings

Mechanical bearings (i.e., sliding and rolling bearings) are widely used for motion guidance in precision positioning stages due to their low cost, high off-axis stiffness and vacuum compatibility. However, mechanical-bearing-guided stages suffer from the presence of pre-motion (i.e., pre-sliding/pre-rolling) friction which adversely affects their positioning speed and motion precision. Friction isolator has been proposed as a low-cost and robust method for mitigating the undesirable effects of pre-motion friction. It has been experimentally demonstrated that a stage with friction isolator achieves significantly reduced settling times and motion errors in point-to-point positioning and tracking applications, respectively. This paper investigates the influence of design parameters on the effectiveness of friction isolators. Experiments are carried out using three isolators with different parameters to evaluate the settling time and in-position stability during point-to-point motions, and the accuracy and robustness of feedforward friction compensation during circular tracking motions. The experimental investigation is generalized through numerical simulation and frequency domain analysis using a simple model of a friction-isolated stage under the influence of pre-motion friction. Generalized design guidelines are provided based on the observed tradeoffs between different performance metrics (e.g., precision, speed).     

Robust motion controller design for high-accuracy positioningsystems

This paper presents a controller structure for robust high speed and accuracy motion control systems. The overall control system consists of four elements: a friction compensator; a disturbance observer for the velocity loop; a position loop feedback controller; and a feedforward controller acting on the desired output. A parameter estimation technique coupled with friction compensation is used as the first step in the design process. The friction compensator is based on the experimental friction model and it compensates for unmodeled nonlinear friction. Stability of the closed-loop is provided by the feedback controller. The robust feedback controller based on the disturbance observer compensates for external disturbances and plant uncertainties. Precise tracking is achieved by the zero phase error tracking controller. Experimental results are presented to demonstrate performance improvement obtained by each element in the proposed robust control structure     

Continuous Path Controller for the Remote Ultrasound Diagnostic System

A master-slave type remote ultrasound diagnostic system has been developed. The impedance controller has been implemented and reported for the positions of the master and slave manipulators to display and control the contact force between the ultrasound probe and the affected area. The present paper introduces an alternative orientation controller utilizing continuous path (CP) control in the remote ultrasound diagnostic system. The major contribution of the present paper is an introduction to the velocity-control-based CP controller for the master-slave type remote medical system, which realizes the continuous motion of the slave manipulator without a reduction in the master motion tracking performance. It is difficult to communicate information for control at high sampling rates between the master and slave site because the communication network between the master and slave site (local area network, integrated services digital network, asymmetric digital subscriber line, etc.) is limited. To cope with this problem, the CP controller was introduced to the orientation controller in special remote medical systems. The CP control realizes the continuous motion of the slave manipulator under the limited sampling rate of the orientation data transmitted by the master manipulator and high master motion tracking performance of the slave manipulator. This allows the slave manipulator safety to be improved and decreases the volume of the transmitted data. The experimental results demonstrate the accuracy of the path control of the slave manipulator using the CP control in the master--slave manipulation system, compared with the conventional point-to-point control.     

Dual Servo Control of a High-Tilt 3-DOF Microparallel Positioning Platform

This paper presents a precise positioning control of a microparallel positioning platform using a dual-stage servo system. The result of the research can be applied to dual-stage-type parallel machines for improving the positioning accuracy. The proposed platform adopts a dual-stage system that consists of three coarse actuators and three fine actuators to realize 3 degrees of freedom (DOF) motion. The 3-DOF motion of the end-effector is measured by a set of three linear sensors. Dynamic models for the coarse and fine actuators are derived by the system identification approach. The gain-scheduled multi-input multi-output (MIMO) controllers are synthesized based on the modeling. The MIMO controller is designed with a mixed-sensitivity criterion on tracking performance and positioning capability, and the design of the gain scheduler is based on the kinematics change. By integrating the controllers for two kinds of actuators, a dual servo controller can be developed based on the master-slave with decoupling structure. An antiwindup controller and a feedforward compensator are adopted to improve the performance. The successful performance of the synthesized dual servo controller is validated through experiments on tracking to guarantee submicrometer accuracy.     

Development of micromanipulator and haptic interface for networkedmicromanipulation

Telemicromanipulation systems with haptic feedback, which are connected through a network, are proposed. It is based on scaled bilateral teleoperation systems between different structures. These systems are composed of an original 6 degree of freedom (DOF) parallel link manipulator to carry out micromanipulation and a 6-DOF haptic interface with force feedback. A parallel mechanism is adopted as a slave micromanipulator because of its good features of accuracy and stiffness. The system modeling and control of the parallel manipulator system are conducted. Parallel manipulator feasibility as a micromanipulator, positioning accuracy and device control characteristics are investigated. A haptic master interface is developed for micromanipulation systems. System modeling and a model reference adaptive controller are applied to compensate friction force, which spoils free motion performance and force response isotropy of the haptic interface. These systems aim to make the micromanipulation more productive constructing a better human interface through the microenvironment force and scale expansion     

Adaptive robust motion control of single-rod hydraulic actuators:theory and experiments

High-performance robust motion control of single-rod hydraulic actuators with constant unknown inertia load is considered. The two chambers of a single-rod actuator have different areas, so the dynamic equations describing the pressure changes in them cannot be combined into a single load pressure equation. This complicates controller design since it not only increases the system dimension but also brings in the stability issue of the added internal dynamics. A discontinuous projection-based adaptive robust controller (ARC) is constructed. The controller takes into account not only the effect of parameter variations coming from the inertia load and various hydraulic parameters but also the effect of hard-to-model nonlinearities such as uncompensated friction forces and external disturbances. It guarantees a prescribed output tracking transient performance and final tracking accuracy in general while achieving asymptotic output tracking in the presence of parametric uncertainties. In addition, the zero error dynamics for tracking any nonzero constant velocity trajectory is shown to be globally uniformly stable. Experimental results are obtained for the swing motion control of a hydraulic arm and verify the high-performance nature of the proposed strategy. In comparison to a state-of-the-art industrial motion controller, the proposed algorithm achieves more than a magnitude reduction of tracking errors. Furthermore, during the constant velocity portion of the motion, it reduces the tracking errors almost down to the measurement resolution level     

Identification of dynamic and friction parameters of a parallel manipulator with actuation redundancy

The dynamic model is formulated in the joint space for a parallel manipulator with actuation redundancy. The established model is expressed in its linear matrix form with respect to the dynamic and friction parameters, and then the Weighted Least Square (WLS) method is applied to estimate the parameters. In order to get satisfying estimated results, the filters of joint angle and actuated torque are designed for the parallel manipulator. A simple and effective method is presented to design the excitation trajectory by analyzing the mechanism structure characteristics of the actual parallel manipulator. Another type of trajectory which is different from the excitation trajectory is adopted to verify the estimated parameters. The dynamic control experiments based on the identified model with the estimated parameters are implemented on an actual 2-DOF parallel manipulator with actuation redundancy, and the tracking accuracy of the identified model is compared with the result of the so-called nominal model.     

Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator

This paper presents the design, development and implementation of an adaptive recurrent neural networks (ARNN) controller suitable for real-time manipulator control applications. The unique feature of the ARNN controller is that it has dynamic self-organizing structure, fast learning speed, good generalization and flexibility in learning. The proposed adaptive algorithm focuses on fast and efficient optimization by weighting parameters of inverse recurrent neural models used in the ARNN controller. This approach is employed to implement the ARNN controller with a view to controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time. The performance of this novel proposed controller was found to be superior compared with a conventional PID controller. These results can be applied to control other highly nonlinear systems as well.     

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.