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.     

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.     

Orientation control of a differential mobile robot through wheel synchronization

This paper presents the orientation control of a differential mobile robot through the synchronization of the motions of two driving wheels, using an adaptive coupling control algorithm. The orientation error is usually caused by the synchronization error between two driving wheels and has the largest impact on the motion accuracy. The proposed controller incorporates the cross-coupling technology into an adaptive control architecture and guarantees asymptotic convergence to zero of the position tracking errors of two wheels as well as the synchronization error between them. Experiments demonstrate that the proposed control method exhibits better motion performance than traditional control methods especially in the orientation control.     

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.     

Integral resonant damping for high-bandwidth control of piezoceramic stack actuators with asymmetric hysteresis nonlinearity

This paper presents a novel control scheme for high-bandwidth control of piezoceramic stack actuators (PSAs). For this purpose, we first characterize and compensate for the asymmetric hysteresis nonlinearity of the PSA. A linear integral resonant controller is then designed as a means for damping the resonant modes of the dynamic system with the hysteresis compensation. Finally, a tracking controller and feedforward input are developed to minimize the tracking errors and improve the closed-loop tracking bandwidth. To verify the effectiveness and efficiency of the proposed control scheme, a PSA-actuated positioning platform is built and comparative experiments are conducted. Experimental results demonstrate that the proposed controller achieves robust broadband nanopositioning of the PSA by improving the tracking bandwidth from 22 Hz (with an integral controller) to 657 Hz.     

Design of M-G modal space sliding mode control for lower limb exoskeleton robot driven by electrical actuators

Lower limb exoskeleton robot (LLER) can help patients with lower limb paralysis to carry out effective rehabilitation training. However, LLER is a kind of nonlinear system with the strong dynamic coupling between joints and the parameter perturbation following different poses of the robot. They will damage the control performance in the process of trajectory tracking. To solve these problems, a novel control strategy, Mass-Gravity modal space sliding mode control (M-GMSSMC), is proposed. The objective for this paper is to develop a novel decoupling control framework for an electrical actuators driven LLER to track a predefined gait trajectory. The controller design aims to improve trajectory tracking accuracy, reduce dynamic coupling between hip joint and knee joint and weaken the chattering phenomenon of the sliding mode controller. The decoupling condition and the robust stability condition are analyzed in this work. Experimental results validate the correctness of the presented conclusions and show the effectiveness of the proposed M-GMSSMC.     

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.     

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.     

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.     

Design and practical implementation of a Neural Network self-tuned Inverse Dynamic Controller for a 3-DoF Delta parallel robot based on Arc Length Function for smooth trajectory tracking

This paper proposes an online Neural Network self-tuned Inverse Dynamic Controller (IDC) for high-speed and smooth trajectory tracking control of a 3-DoF Delta robot. The foregoing approaches provides a suitable controller for a wide range of nonlinear paths and reduce the end-effector oscillations at high speed. To this end, a compact and accurate dynamic model of the system is derived by taking into account actuators and gearbox dynamics. In order to alleviate some drawbacks of a velocity-based controller, such as not being able to track highly dynamic paths, an Inverse Dynamic Controller (IDC) is designed which can perform fast maneuvers accurately. The proposed IDC controller is practically implemented on the robot in following nonlinear paths comparing to the velocity-based controller. Afterward, controller parameters are tuned by resorting to the so-called Arc Length Function (ALF) in order to improve the smoothness of tracking the prescribed path. After that, a Feedforward Neural Network (NN) is trained with the help of the system’s model and Arc Length Function (ALF) to adjust controller coefficients in real-time implementation adaptively. By comparing the Root Mean Square Error (RMSE) results, it can be inferred that the proposed methods can reduce the end-effector oscillations up to 60 percent in practical implementation compared to other dynamic and kinematic methods. As a result, RMSE error is reduced from 0.00603 for the kinematic controller to 0.00063 by applying the NN-IDC.     

Position control of shape memory alloy actuator based on the generalized Prandtl–Ishlinskii inverse model

Hysteresis and significant nonlinearities in the behavior of Shape Memory Alloy (SMA) actuators encumber effective utilization of these actuator. Due to these effects, the position control of SMA actuators has been a great challenge in recent years. Literature review of the research conducted in this area shows that using the inverse of the phenomenological hysteresis models can compensate the hysteresis of these actuators effectively. But, inverting some of these models, such as Preisach model, is numerically a complex task. However, the generalized Prandtl–Ishlinskii model is analytically invertible, and therefore can be implemented conveniently as a feedforward controller for compensating hysteresis nonlinearities effects in SMA actuators. In this paper a feedforward–feedback controller is used to control the tip deflection of a large deflected flexible beam actuated by an SMA actuator wire. The feedforward part of the control system is based on the generalized Prandtl–Ishlinskii inverse model while a conventional proportional–integral feedback controller is added to the feedforward controller to increase the accuracy together with eliminating the steady state error in position control process. Experimental results show that the proposed controller performs well in terms of achieving small overshoot and undershoot for square wave tracking as well as small tracking errors for sinusoidal trajectory. It has also great capability for tracking hysteresis minor loops.     

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system, the dynamic performance of a robot control system can be guaranteed at the initial control stage, which is very important for enhancing system stability and adaptive learning. The discrete-time adaptive control composed of the NF dynamic-inversion and NF variable structure control (NF-VSC) is developed to stabilize the closed-loop system and ensure the high-quality tracking. The NF-VSC enhances the stability of the controlled system and improves the system dynamic performance during the NF learning. The system stability and the convergence of tracking errors are guaranteed by the Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. An example is given to show the viability and effectiveness of the proposed control approach     

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.     

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 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     

Gain scheduler middleware: a methodology to enable existing controllers for networked control and teleoperation-part II: teleoperation

This paper is the second of two companion papers. The foundation for the external gain scheduling approach to enable an existing controller via middleware for networked control with a case study on a proportional-integral (PI) controller for dc motor speed control over IP networks was given in Part I. Part II extends the concepts and methods of the middleware called gain scheduler middleware (GSM) in Part I to enable an existing controller for mobile robot path-tracking teleoperation. By identifying network traffic conditions in real-time, the GSM will predict the future tracking performance. If the predicted tracking performance tends to be degraded over a certain tolerance due to network delays, the GSM will modify the path-tracking controller output with respect to the current traffic conditions. The path-tracking controller output is modified so that the robot will move with the fastest possible speed, while the tracking performance is maintained in a certain tolerance. Simulation and experimental results on a mobile robot path-tracking platform show that the GSM approach can significantly maintain the robot path-tracking performance with the existence of IP network delays.     

Transputer control of hydraulic actuators and robots

The paper reports and discusses results from a Danish mechatronic research program focusing on intelligent actuators for intelligent motion control. A mechatronic test facility with a transputer controlled hydraulic robot suitable for real-time experimentation and evaluation of control laws and algorithms is described. Concepts of intelligent motion control and intelligent hydraulic actuators are proposed. Promising experimental path-tracking results obtained from model-based adaptive control algorithms are presented and discussed. The experiments confirm that transputers have significant advantages in intelligent control of actuators and robots for high-speed and high-precision tasks. Further, research on learning controllers and hybrid controller architecture, including real-time switching between control algorithms, benefits from applying transputer technology     

Model reference adaptive control of a soft bending actuator with input constraints and parametric uncertainties

In this article, model reference adaptive control of a pneumatically actuated soft robot has been studied in detail. To deal with the effects of system uncertainties, in the proposed control scheme, parametric uncertainties and input constraints are taken into account. To design such a controller, based on experimental analysis, the robot has been modeled as a second-order Linear Parameter Varying (LPV) system. Then, the dominant dynamics are presented as a Linear Time-Invariant (LTI) system, while uni-directional input constraint has been considered as a critical issue in the control scheme design. Furthermore, to compensate parametric uncertainties as well as unmodeled dynamics, adaptive laws are modified. Finally, the effectiveness is studied in different scenarios on an experimental platform to validate our claims. Moreover, to show the proposed approach capabilities and performance, the proposed controller has been compared with a PID and a recent sophisticated robust-adaptive controller, which presented a new formulation to achieve a better tracking performance with guaranteed stability in the presence of different constraints and unmodeled dynamics.     

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.     

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.