Control of switched reluctance motor torque for force controlapplications

The paper presents a method for controlling switched reluctance motor (SRM) torque for force control applications. SRMs are used in AdeptOne robots, and the authors perform experiments with two robots, controlled in coordination, in grasping and manipulation of various objects. The object and robot parameters are not exactly known, and adaptive methods are used to control the overall system. These methods are model-based control techniques which require high bandwidth torque control. This requirement is typical for high precision mechanisms. SRM characteristics are very nonlinear. In particular the torque ripple, friction, and the torque versus position and current relationships were analyzed in the context mentioned above, and specifically, for force control applications. The proposed method is based on a new commutation algorithm and a measured torque versus position and current relationship, used to smooth the SRM's torque ripple, hence generating a torque output nearly independent of position. Furthermore, the internal friction is estimated on-line, and compensated for. This renders a high accuracy torque tracking. The torque control method is based on feedback from the motor angular velocity, motor angle, armature current, and feedforward for friction compensation and cancellation of nonlinear effects. The method has been tested experimentally on Adept motors and the results were very encouraging. The method has been also used for adaptive control of two coordinated Adept robots     

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.     

A hybrid dynamic model for the AMBIDEX tendon-driven manipulator

Tendon-driven actuation allows for light and compact manipulator designs with enhanced safety features. One of the key challenges in model-based control of tendon-driven robots is the increased complexity of the dynamic model, due in large part to difficult-to-model behavior like nonlinear dynamic deformation and friction at the tendons. While purely data-driven modeling approaches, e.g., neural networks, free one from dealing with complex and often error-prone mechanics models, they usually do not generalize well to diverse tasks, and also do not offer the needed intuitive understanding or predictive power of traditional mechanics-based models. In this paper, we present a hybrid modeling approach for complex tendon-driven robots, which effectively complement the limitations of pure physics-based and data-driven learning-based approaches. Rigid multibody equations of motion are augmented with (i) a configuration-dependent viscous-Coulomb friction model and (ii) a recurrent neural network that captures the tendon dynamics, and estimates link joint angles from the motor positions, velocities, and torques. Experiments involving a two-dof tendon-driven parallel wrist mechanism and the 7-dof AMBIDEX tendon-driven manipulator validate the performance advantages of our hybrid model-based control framework.     

Nonlinear identification and optimal feedforward friction compensation for a motion platform

In this study, we present a method of nonlinear identification and optimal feedforward friction compensation for an industrial single degree of freedom motion platform. The platform has precise reference tracking requirements while suffering from nonlinear dynamic effects, such as friction and backlash in the driveline. To eliminate nonlinear dynamic effects and achieve precise reference tracking, we first identified the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function (HOSIDF) based system identification. Next, we present optimal feedforward compensation design to improve reference tracking performance. We modeled the friction using the Stribeck model and identified its parameters through a procedure including a special reference signal and the Nelder–Mead algorithm. Our results show that the RMS trajectory tracking error decreased from 0.0431 deg/s to 0.0117 deg/s when the proposed nonlinear identification and friction compensation method is utilized.     

GMDH-based modeling and feedforward compensation for nonlinear friction in table drive systems

This paper presents a novel mathematical model-based feedforward compensator design for the nonlinear friction in table drive systems using the Group Method of Data Handling (GMDH). In the proposed approach, the nonlinear friction can be autonomously modeled as a polynomial expression for appropriate control state variables according to the process of GMDH and, as a result, the complicated structural modeling and its parameterization, indispensable to conventional model-based strategies, can be completely eliminated. In addition, since the proposed GMDH-based model can achieve the generalization ability for table drive conditions, the robust compensation for friction can be attained against the change of drive conditions. Experimental verifications using a table drive system of actual machine tools show the significant performance improvement of the proposed algorithm in the trajectory control with velocity reversal motion.     

Friction identification of ball-screw driven servomechanisms through the limit cycle analysis

Friction degrades the positioning accuracy of servomechanisms. Friction compensators are required to fabricate high-performance servomechanisms. In order to compensate for the friction in the servomechanism accurately, identification of the friction is required first. This paper proposes a friction identification method of a ball-screw driven servomechanism in the frequency domain. A nonlinear friction model including static, Coulomb, and viscous friction as well as Stribeck effect is formulated by using describing functions. Friction elements are estimated through the limit cycle analysis in a velocity control loop. In order to increase the accuracy of the friction identification process, a Butterworth filter is incorporated into the velocity feedback loop. Validity of the proposed method is confirmed through the numerical simulation and experiment in a ball-screw driven servomechanism. In addition, a model-based friction compensator is applied as a feedforward controller to compensate for the nonlinear characteristics of the servomechanism and to verify the effectiveness of the proposed identification method.     

Adaptive control for enhancing tracking performances of flexible tendon–sheath mechanism in natural orifice transluminal endoscopic surgery (NOTES)

Tendon–sheath mechanism (TSM) has inherent advantages in the development of flexible robotic systems because of its simplicity, safety, flexibility, and ease of transmission. However, the control of TSM is challenging due to the presence of nonlinearities, namely friction, backlash-like hysteresis and the time-varying configuration of the TSM during its operations. Existing studies of TSM found in the literature only address tendon transmission under the assumption of fixed configuration and a complex inverse model of backlash is required. In order to flexibly use the system in a wider range of applications, the aforementioned nonlinear effects have to be characterized for the purpose of compensation. In this paper, we endeavor to address these issues by presenting a series of controller strategies, namely a feedforward control scheme under the assumption of known backlash-like hysteresis profile, and an adaptive control scheme to characterize the nonlinearities with unknown backlash hysteresis and uncertainties. The proposed control schemes do not require information of the tendon–sheath configurations, which is challenging to obtain in practice, in the compensation structures. In the absence of output position feedback, a simple direct inverse model-based feedforward has been used that efficiently reduce the tracking errors. The feedforward compensation does not require any complex algorithm for the inverse model. In the presence of output position feedback, a nonlinear adaptive controller has been developed to enhance the tracking performances of the TSM regardless of the random change in the tendon–sheath configurations during compensation. In addition, exact values of the model parameters are not required. They are estimated online during the operations. A dedicated experimental setup is introduced to validate the proposed control approaches. The results show that the proposed control schemes significantly improve the tracking performances for the TSM in the presence of uncertainties and time-varying configurations during the operations. There is a significant decrease of 0.0158 rad² (before compensation) to smaller value of 0.0012 rad² (use feedforward control) and 8.2815 × 10⁻⁵ rad² (use nonlinear controller) after compensation.     

Direct yaw moment control actuated through electric drivetrains and friction brakes: Theoretical design and experimental assessment

A significant challenge in electric vehicles with multiple motors is how to control the individual drivetrains in order to achieve measurable benefits in terms of vehicle cornering response, compared to conventional stability control systems actuating the friction brakes. This paper presents a direct yaw moment controller based on the combination of feedforward and feedback contributions for continuous yaw rate control. When the estimated sideslip exceeds a pre-defined threshold, a sideslip-based yaw moment contribution is activated. All yaw moment contributions are entirely tunable through model-based approaches, for reduced vehicle testing time. The purpose of the controller is to continuously modify the vehicle understeer characteristic in quasi-static conditions and increase yaw and sideslip damping during transients. Skid-pad, step-steer and sweep steer tests are carried out with a front-wheel-drive fully electric vehicle demonstrator with two independent drivetrains. The experimental test results of the electric motor-based actuation of the direct yaw moment controller are compared with those deriving from the friction brake-based actuation of the same algorithm, which is a major contribution of this paper. The novel results show that continuous direct yaw moment control allows significant “on-demand” changes of the vehicle response in cornering conditions and to enhance active vehicle safety during extreme driving maneuvers.     

A novel fuzzy friction compensation approach to improve theperformance of a DC motor control system

The compensation of friction nonlinearities for servomotor control has received much attention due to undesirable and disturbing effects that the friction often has on conventional control systems. Compensation methods have generally involved selecting a friction model and then using model parameters to cancel the effects of the nonlinearity. In this paper, a method using fuzzy logic for the compensation of nonlinear friction is developed for the control of a DC motor. The method is unique in that a single fuzzy rule is used to compensate directly for the nonlinearity of the physical system. As a result, the method introduces fewer adjustable parameters than a typical fuzzy logic approach while still incorporating many advantages of using fuzzy logic such as the incorporation of heuristic knowledge, ease of implementation and the lack of a need for an accurate mathematical model. The general approach, analysis and experimental results obtained for an actual DC motor system with nonlinear friction characteristics are presented and the effectiveness of the fuzzy friction compensation control technique is discussed. The smoothness of response, response times and disturbance rejection of a PI control system with and without the proposed fuzzy compensator are analyzed and discussed to illustrate the effectiveness of the proposed method     

Modeling,observation, and control of hysteresis torsion in elastic robot joints

Hysteresis torsion in elastic robot joints occurs as a coupled nonlinearity due to internal friction, backlash, and nonlinear stiffness, which are coactive inside of mechanical transmission assemblies. The nonlinear joint torsion leads to hysteresis lost motion and can provoke control errors in relation to the joint output at both trajectories tracking and positioning. In this paper, a novel modeling approach for describing the nonlinear input–output behavior of elastic robot joints is proposed together with the observation and control method, which aim to compensate for the relative joint torsion without load sensing. The proposed modeling approach includes the recently developed 2SEP dynamic friction model and Bouc–Wen-like hysteresis model, which is originated from structural mechanics, both arranged according to the assumed torque transmitting structure. The proposed method is evaluated with experiments using the laboratory setup which emulates a single rotary joint under impact of nonlinear elasticities, friction, and gravity.     

Comparison of model-free and model-based methods for time optimal hit control of a badminton robot

In this research, time optimal control is considered for the hit motion of a badminton robot during a serve operation. Even though the robot always starts at rest in a given position, it has to move to a target position where the target velocity is not zero, as the robot has to hit the shuttle at that point. The goal is to reach this target state as quickly as possible, yet without violating the limitations of the actuator. To find controllers satisfying these requirements, both model-based and model-free controllers have been developed, with the model-free controllers employing a Natural Actor-Critic (NAC) reinforcement learning algorithm. The model-based controllers can immediately achieve the desired motions relying on prior model information, while the model-free methods are shown to yield the desired robot motions after about 200 trials. However, in order to achieve this result, a good choice of the reward function is essential. To illustrate this choice and validate the resulting controller, a simulation study is presented in which the model-based results are compared to those obtained with two different reward functions.     

不确定非线性位置伺服系统自适应变结构控制

位置伺服系统中的各类非线性和不确定性,使得对系统进行精确控制变得相当困难。常规的、单一的控制方法很难适应高精度位置伺服系统的要求,考虑系统运算放大器饱和、非线性摩擦和传动链空回情况,将自适应原理和变结构控制相结合,利用反演方法设计了系统的位置控制器,仿真结果验证了该方法的有效性。     

Adaptive sliding controller with self-tuning fuzzy compensation for vehicle suspension control

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sliding mode control design. Here, a novel model-free adaptive sliding controller is proposed to suppress the position oscillation of the sprung mass in response to road surface variation. This control strategy employs the functional approximation technique to establish the unknown function for releasing the model-based requirement. In addition, a fuzzy scheme with online learning ability is introduced to compensate the functional approximation error for improving the control performance and reducing the implementation difficulty. The important advantages of this approach are to achieve the sliding mode controller design without the system dynamic model requirement and release the trial-and-error work of selecting approximation function. The update laws for the coefficients of the Fourier series functions and the fuzzy tuning parameters are derived from a Lyapunov function to guarantee the control system stability. The experimental results show that the proposed control scheme effectively suppresses the oscillation amplitude of the vehicle sprung mass corresponding to the road surface variation and external uncertainties, and the control performance is better than that of a traditional model-based sliding mode controller.     

Hierarchical optimal contour control of motion systems

Many motion control applications utilize multiple axes to traverse complex trajectories. The hierarchical contour control methodology proposed in this paper treats each axis as an individual subsystem and combines the Internal Model Principle with robust tracking and optimal hierarchical control techniques to track a desired trajectory. In this method the objectives are divided into two levels. Measurable goals of each subsystem are included in the bottom level and unmeasurable goals, which are estimated using the bottom level states, are considered in the top level where the subsystems are synchronized. The proposed methodology reduces system complexity while greatly improving tracking performance. The tracking error for each axis is considered in the bottom level where the Internal Model Principle is used to compensate for unmodeled nonlinear friction and slowly varying uncertainties. The top level goal (i.e., zero contour error) is propagated to the lower level by an aggregation relationship between contour error and physical linear axis variables. A controller is designed at the bottom level which simultaneously satisfies the bottom level goals (i.e., individual axis tracking) and the top level goal. Experimental results implemented on a table top CNC machine for diamond and freeform contours illustrate the performance of the proposed methodology. While this methodology was implemented for a two-axis motion system, it can be extended to any motion system containing more than two axes.     

基于模糊逼近的非线性多时延系统的自适应跟踪控制

针对一类多输入多输出非线性多时延系统,提出了基于模糊逼近的自适应跟踪控制方案.该方案构建了基于模糊T-S模型的自适应时延模糊逻辑系统,用来逼近未知非线性时延函数.从而实现了对非线性系统的建模.根据跟踪误差给出了时延模糊逻辑系统的参数自适应律.设计了H_∞补偿器来抵消模糊逼近误差和外部扰动.基于Lyapunov稳定性理论,提出的控制方案保证了闭环系统的稳定性并获得了期望的H_∞跟踪性能.机械臂的仿真结果表明了该方案的有效性.     

光电跟踪转台伺服控制策略研究

针对高性能光电跟踪转台负载重、摩擦大、跟踪精度要求高等特点,提出了基于复合控制的伺服控制策略,速度环路设计了带有扰动观测器的线性二次最优反馈控制器,并在前向通道增加了零相位误差跟踪控制器(ZPETC),提高速度环的跟踪性能,位置环采用非线性PID反馈控制方式降低超调,提高稳态精度;将低速率的位置给定信息分别进行插值细分和滤波,通过高增益微分器和卡尔曼预测滤波,对转台速度和加速度进行预测和估计,进行前馈实现复合控制,实践证明,这种策略可以有效提高大加速度下的跟踪精度。     

Control approaches for high-precision machine tools with airbearings

Numerous control strategies have been developed to compensate for the effects caused by friction in linear guideways of feed-drive systems. For ultraprecision machining applications, these offer a wide variety of powerful, nonlinear algorithms, but generally use rather complex computing algorithms, exhausting system resources. By the use of a special sophisticated aerostatic bearing technology, no friction and, therefore, no stick-slip exist within linear guideways. This entails a lack of damping in the feed direction, which is a considerable setback for the classic cascaded control concept usually implemented in commercially available servo systems. This paper presents an approach to obtain superior behavior of the controlled system by combining the cascaded concept with a control design in the state space. Both simulation and implementation results are presented, together with tuning methods and aspects     

Precise angular speed control of permanent magnet DC motors in presence of high modeling uncertainties via sliding mode observer-based model reference adaptive algorithm

The major concentration of this study is on developing a control scheme with parameter- and load-insensitive features capable of precise angular speed regulation of a permanent magnet (PM) DC motor in the presence of modeling uncertainties. Towards this objective, first, an appropriate nonlinear dynamic model of friction, the modified LuGre model, is opted and incorporated into the mathematical model of a PM DC motor. Then a sliding mode observer (SMO) is designed to estimate the state variable of the friction model. Next, a model reference adaptive control system into which estimated values of the friction state and parameters are fed is designed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction. Stability of the proposed SMO-based MRAC system is discussed via the Lyapunov stability theorem, and its asymptotic stability is verified. In addition to simulation studies, the algorithm is implemented on a new variable structure test-bed which gives us the ability to simulate desired parameter variations and external disturbance changes in experiment. The main contribution of the proposed scheme is the bounded estimation of the system’s friction parameter. While similar control solutions do estimate these parameters, there is no guarantee that they will estimate the correct value of friction parameters. However, in the proposed method, by properly choosing the design parameters, if certain criteria is satisfied, the estimated friction parameters will be in the bounded vicinity of their actual values. The obtained results show the effectiveness of the proposed tracking algorithm and its robustness against load and system parameters’ variations.     

Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks

This paper deals with a tracking control problem of a mechanical servo system with nonlinear dynamic friction which contains a directly immeasurable friction state variable and an uncertainty caused by incomplete parameter modeling and its variations. In order to provide an efficient solution to these control problems, we propose a composite control scheme, which consists of a friction state observer, a RFNN approximator and an approximation error compensator with sliding mode control. In first, a sliding mode controller and friction state observer are designed to estimate the unknown internal state of the LuGre friction model. Next, a RFNN is developed to approximate an unknown lumped friction uncertainty. Finally, an adaptive error compensator is designed to compensate an approximation error of RFNN. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are executed. Their results give a satisfactory performance of the proposed control scheme.