Two-parameter robust repetitive control with application to a novel dual-stage actuator for noncircular Machining

This work presents a robust repetitive controller design for a novel dual-stage actuator system. The dual-stage actuator, which consists of an electrohydraulic actuator for 25-mm-gross motion and a piezoelectric actuator for 40-/spl mu/m fine motion, is designed for noncircular machining application. The controller is designed through a sequence of two single-input-single-output (SISO) designs by exploiting the triangular structure of the two by two actuator system dynamics. The tracking error from the first stage electrohydraulic actuator is used as reference for the second stage piezoelectric actuator. In this master-slave control arrangement, the overall sensitivity function is the product of two sensitivity functions from each actuator's servo loop. Thus, performance is improved at the frequencies where the sensitivity values are already well less than one. In the real-time control implementation, the effects of finite word length are analyzed and addressed via controller order reduction and realization. In an experiment of tracking an automotive cam profile at the rate of 10 cycles per second (600 rpm), the proposed dual-stage servo system generated tracking error of 4-/spl mu/m peak-to-valley and 0.80-/spl mu/m root-mean-square (RMS) value, showing a substantial improvement over the 16 micron peak-to-valley and 2.64-/spl mu/m RMS errors generated by the electrohydraulic servo system alone.     

Nested saturation based control of an actuated knee joint orthosis

Wearable robots have opened a new horizon for assistance and rehabilitation of dependent/elderly persons. The present study deals with the control of an actuated lower limb orthosis at the knee joint level. The dynamics of the shank–foot–orthosis system are expressed through a nonlinear second order model taking into account viscous, inertial and gravitational properties. Shank–foot–orthosis system parameters are identified experimentally. Since the underlying dynamic model is nonlinear, a robust control strategy is needed to guarantee an accurate and precise movement generation. The proposed control strategy ensures, at the same time, the stability of the closed-loop system. A bounded control torque is applied to guarantee the asymptotic stability of the shank–foot–orthosis. The generated control respects the physical constraints imposed by the system. The effectiveness of the proposed control strategy is shown in real-time in terms of stability, position tracking performances and robustness with respect to identification errors and external disturbances.     

Robust position control of a magnetic levitation system via dynamic surface control technique

This paper considers the position-tracking problem of a magnetic levitation system in the presence of modeling errors due to uncertainties of physical parameters. A robust nonlinear controller is designed to achieve excellent position-tracking performance. The recently developed dynamic surface control is modified and applied to the system under study, to over-come the problem of "explosion of terms" associated with the backstepping design procedure. Input-to-state stability of the control system is analyzed, and the advantages of the dynamic surface control technique over the conventional backstepping technique are verified through both theoretical and experimental studies.     

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.     

基于反推的永磁同步电动机伺服系统的位置跟踪控制

永磁同步电动机工作性能优越，在当前交流伺服系统的驱动控制当中起着越来越重要的作用。为了实现永磁同步电动机的精确位置跟踪，把一种新颖非线性控制方法Backstepping应用于永磁同步电动机伺服系统控制器的设计。Backstepping控制器的设计以保证系统的全局一致渐近稳定为原则，因此该控制器不但可以保证系统的全局一致渐近稳定，而且系统具有快速跟踪，定位精确的特点。系统的设计能够有效降低转矩变化对位置跟踪性能的影响。最后通过Matlab仿真验证了系统设计的有效性和可行性。     

Identification and control of continuous-time nonlinear systems via dynamic neural networks

In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.     

Fuzzy optimization techniques applied to the design of a digital PMSM servo drive

This paper presents a novel design approach by applying gradient optimization with fuzzy step-sizing techniques to the design of a digital permanent magnet synchronous motor (PMSM) servo drive. The servo specifications and design variables are specified and analyzed to formulate a controller optimization problem. The servo responses are then fed back to evaluate the overall system performances, which can be expressed as objective functions with respect to the servo control parameters. According to the objective functions and design specifications, the servo control parameters can be properly tuned toward their optimal values by using the proposed optimization techniques. In order to improve the convergent rate of the optimization process, a fuzzy-logic based step-size tuning strategy is presented. Because of the nonlinear property of the digital servo drives, the tuned servo control parameters may be only optimal for a particular operating point, therefore, once the optimum design is achieved, the proposed fuzzy optimizing controller can perform as an intelligent tuner for on-line gain adaptation under different loading conditions. The proposed fuzzy optimization servo tuner has been realized under a PC-MATLAB-based environment with an on-line controlled digital PMSM servo drive. Simulation and experimental results indicate that the control parameters of a digital PMSM servo drive can be optimized for its dynamic responses under various load conditions.     

Adaptive incremental sliding mode control for a robot manipulator

An adaptive incremental sliding mode control (AISMC) scheme for a robot manipulator is presented in this paper. Firstly, an incremental backstepping (IBS) controller is designed using time-delay estimation (TDE) to reduce dependence on the mathematical model. After substituting IBS controller into the nonlinear system, a linear system w.r.t. tracking errors is obtained while TDE error is the disturbance. Then, the AISMC scheme, including a nominal controller and an SMC, is developed for the resulted linear system to improve control performance. According to the equivalent control method, the SMC in the AISMC scheme is to handle TDE error. To receive optimal control performance at the sliding manifold, an LQR controller is selected as the nominal controller. The SMC is designed based on positive semi-definite barrier function (PSDBF) since it prevents switching gains from being over/under-estimated, and two practical problems are addressed in this paper: A new PSDBF is designed and conservative (large) setting bounds affecting tracking precision and/or system stability are avoided; An improved PSDBF based SMC is developed where the PSDBF and an adaptive parameter are used simultaneously to regulate switching gains, and the system is still stable when sliding variable occasionally exceeds the predefined vicinity. Moreover, finite-time convergence property of the sliding variable is strictly analyzed. Finally, real-time experiments are conducted to verify the effectiveness of the proposed control method.     

基于DBFNN的推设及其在电力系统励磁控制中的应用

本文采用后推设计算法为一类严格反馈系统设计了基于方向基函数神经网络(DBFNN)的自适应控制器.在后推算法中的每步都引入一积分型的Lyapunov函数来设计一个虚拟控制器,并在最后一步为闭环系统综合设计了神经网络控制器.网络权值的调整基于所选择的Lyapunov函数,于是设计方案能保证整个闭环系统是最终一致有界的.把所设计控制方案用于带有未知参数和外部干扰的电力系统励磁控制中.仿真结果表明了所设计控制器的有效性.     

Adaptive Backstepping Control Using Combined Direct and Indirect Adaptation

A new adaptation mechanism is proposed for the tuning functions based adaptive backstepping control for a class of nonlinear systems. The new scheme combines direct and indirect parameter update regimes in an effort to improve the speed and accuracy of the adaptation which in turn leads to increased tracking performance. The parameter update dynamics is driven both by the identification error and the tracking error defined in the backstepping control. The closed loop error dynamics is shown to be globally stable by using a recursive mechanism based on Lyapunov analysis with tracking error convergence along with identification error convergence. The proposed scheme is tested on two benchmark examples in order to demonstrate its performance increase compared to the tuning functions based adaptive backstepping scheme.     

基于DBFNN的后推设计及其在电力系统励磁控制中的应用

本文采用后推设计算法为一类严格反馈系统设计了基于方向基函数神经网络(DBFNN)的自适应控制器．在后推算法中的每步都引入一积分型的Lyapunov函数来设计一个虚拟控制器，并在最后一步为闭环系统综合设计了神经网络控制器．网络权值的调整基于所选择的Lyapunov函数，于是设计方案能保证整个闭环系统是最终一致有界的．把所设计控制方案用于带有未知参数和外部干扰的电力系统励磁控制中．仿真结果表明了所设计控制器的有效性．     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

Subsystem backstepping design for controlling a class of nonlinear SISO systems with cascade structure

This paper presents a controller design, referred to as the subsystem backstepping design (SSBD), for a class of nonlinear SISO mechatronic systems that comprise several cascaded subsystems. Compared with the conventional integrator backstepping design (CIBD) that deals with a first-order equation at each design step, the SSBD manages at each design step a subsystem that can be of high order. This both simplifies the design procedure and also makes controller parameters conveniently determined according to dynamic characteristics of each subsystem as in the conventional cascade control design with multiple feedback loops. However, in contrast to the conventional cascade control design, the SSBD does not require the inner feedback loop to respond much faster than the outer feedback loop, while guaranteeing system stability for a class of nonlinear systems. In addition, a variant of the SSBD, called internal model principle-based SSBD (IMP-SSBD), is presented to both further demonstrate the advantages of the SSBD procedure over the CIBD and also achieve robust tracking performance. The effectiveness of the proposed scheme is demonstrated through experimental studies of a harmonic drive system suffering from transmission compliance and periodic disturbances.     

Recurrent-neural-network-based adaptive-backstepping control for induction servomotors

This study is concerned with the position control of an induction servomotor using a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) system. The adaptive-backstepping approach offers a choice of design tools for the accommodation of system uncertainties and nonlinearities. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. Since the RNN has superior capabilities compared to the feedforward NN for dynamic system identification, it is utilized as the uncertainty observer. In addition, the Taylor linearization technique is employed to increase the learning ability of the RNN. Meanwhile, the adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Finally, simulation and experimental results verify that the proposed RNABC can achieve favorable tracking performance for the induction-servomotor system, even with regard to parameter variations and input-command frequency variation.     

Multi-cylinder electrohydraulic digital loading technology for reproduction of large load

To solve the problem of the control accuracy in electrohydraulic loading systems caused by load increment, this paper proposes a multi-cylinder electrohydraulic digital loading (MEDL) technology for accurate reproduction of large load. A traditionally used single cylinder loading (SCL) is replaced by a new hydraulic cylinders group that includes N hydraulic cylinders at each point, in which one is controlled by the electrohydraulic servo valve and the others (N-1) are controlled by the on-off valve. The areas of the on-off valve controlled (OVC) cylinders form an increasing geometric sequence with a common ratio of 2. In addition, the force of the servo valve controlled (SVC) cylinder can be regulated continuously, and the OVC cylinders have only two states of no force or maximum force. There should be no force tracking error caused by nonlinear factors for the OVC cylinders. Thus, a continuous accurate large loading can be achieved by changing the working area of the cylinders group. Moreover, an improved full closed-loop (FCL) control strategy is proposed to solve the load reverse sudden change caused by the asynchronous opening and closing of the servo valve and on-off valve. With a case of N = 4 for MEDL, AMESim simulation results illustrated that the tracking error of the 4-cylinder group was about 1/6 of the single cylinder under a case of 40 kN. Furthermore, extensive experiments conducted on a real full loading bench under the FCL control method indicated that compared with SCL, the tracking error of the 4-cylinder group with a multi-step signal and various-frequency sinusoidal signals were reduced by 73% and 46%, respectively. Both simulation and experimental results proved that the proposed MEDL technology improved the loading accuracy and optimized the dynamic performance of the system.     

Adaptive robust pressure control of variable displacement axial piston pumps with a modified reduced-order dynamic model

Variable displacement axial piston pumps (VDAPPs) are wildly used in mobile working machines and they play a key role in machine’s energy-saving load sensing (LS) systems. Typically, electric load sensing (ELS) systems utilize traditional linear control methods, which only can realize limited control flexibility and performance. This study proposes and experimentally verifies an adaptive robust pressure control strategy for a VDAPP system. To facilitate the model-based controller design, a modified reduced-order dynamic modeling of VDAPPs is proposed. Furthermore, an adaptive robust backstepping control strategy is designed to deal with the dynamic nonlinearities and parametric uncertainties of the VDAPP system for achieving accurate pressure tracking. The controller design consists of two steps, processing the pump pressure tracking and the axial angle control, respectively. Comparative experiments and simulations with different working conditions were performed to validate the advantages of the proposed control strategy. The proposed controller achieved higher pressure tracking accuracy and it showed great capability in dealing with dynamic nonlinearities, uncertainties, and time-varying disturbances.     

移动机器人全局轨迹跟踪的自适应滑模控制

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

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。     

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.     

基于动态面的死区输入系统跟踪控制算法

针对具有未知死区输入的一类非线性严反馈系统,文中提出了一种基于动态面技术的自适应渐进跟踪控制策略。 在处理虚拟控制器时,不同于传统动态面技术中采用的低阶滤波器,通过引入具有时变积分函数的非线性滤波器,不仅可以规避“微分爆炸”,降低计算负担,简化控制器,还可以很好地补偿动态面方法引起的边界层误差。 理论分析证明,所提出的控制方案能够消除死区非线性的影响,保证闭环系统的稳定性。以单力臂机械手的仿真为例,通过调节设计参数去提高系统的瞬态跟踪性能,实现了系统跟踪误差渐进收敛,验证了所提出控制策略的有效性。