形状记忆合金驱动器的自适应滑模反步控制

形状记忆合金(SMA)作为一种智能材料,相对于传统的驱动器具有功率重量比大、驱动电压低、质量轻、干净、无噪音等特性,被广泛应用在各领域.然而,非线性、迟滞、时变等因素影响了形状记忆合金的控制精度,限制了它的应用.为此,本文提出了自适应滑模反步控制方法,用于解决精确控制问题.文中首先搭建了实验装置,建立了形状记忆合金的机理模型;然后,采用最小二乘算法辨识了模型参数;最后,基于机理模型设计了自适应滑模反步控制器.实验结果表明,本文提出的方法具有动态响应快、跟踪精度高和抗干扰能力强的特性.     

Two-Axial Piezoelectric Actuator Controller Using a Multi-Layer Artificial Neural Network Featuring Feedback Connection for Tactile Displays

Although some compensation method is required when using a piezoelectric actuator because of hysteresis, a sensor feedback method is not suitable for an actuator array. In this study, we design a controller using a neural network to apply it to a tactile display composed of two-axial miniature actuators. This paper describes the two-axial miniature actuator, which is composed of two bimorph piezoelectric elements and two small links connected by three joints. A control system for the two-axial miniature actuator is designed based on a multi-layered artificial neural network to compensate for the hysteresis of piezoelectric elements. The output neuron emits the time derivative of voltage, a two-bit signal expressing increment or decrement condition is generated by two input neurons, and two input neurons calculate current values of voltage and displacement, respectively. The neural network is outfitted with a feedback loop including an integral element to reduce the number of neurons. In the experiment, if the result of the left piezoelectric element is compared to that of the right element, the displacement amplitudes and the inclinations coincide on the right and left piezoelectric elements. Although precise hysteresis characteristics such as loop width are considerably different, the present neural system can follow the difference.     

Dynamic cellular actuator arrays and the expanded fingerprint method for dynamic modeling

A key step to understanding and producing natural motion is creating a physical, well understood actuator with a dynamic model resembling biological muscle. This actuator can then serve as the basis for building viable, full-strength, and safe muscles for disabled patients, rehabilitation, human force amplification, telerobotics, and humanoid robotic systems. This paper presents a cell-based flexible actuator modeling methodology and the General Fingerprint Method for systematically and efficiently calculating the actuators’ respective dynamic equations of motion. The cellular actuator arrays combine many flexible ‘cells’ in complex and varied topologies for combined large-scale motion. The cells can have varied internal dynamic models and common actuators such as piezoelectric, SMA, linear motor, and pneumatic technologies can fit the model by adding a flexible element in series with the actuator. The topology of the cellular actuator array lends it many of its properties allowing the final muscle to be catered to particular applications. The General Fingerprint Method allows for fast recalculation for different and/or changing structures and internal dynamics, and provides an intuitive base for future controls work. This paper also presents two physical SMA based cellular actuator arrays which validate the presented theory and give a basis for future development.     

执行器不确定系统鲁棒预测动态控制分配策略

针对多操纵面级联飞行控制结构中执行器存在多面体不确定的问题, 提出了一种基于鲁棒预测控制理论的动态控制分配策略. 考虑位置约束和速率约束, 建立了多面体不确定冗余执行器的增广控制模型; 以执行器状态和虚拟指令跟踪误差为增广变量构造二次型李亚普诺夫函数, 将无穷时域Min-Max非线性规划转化为线性矩阵不等式凸优化问题, 设计了保守性小的鲁棒预测控制律. 各个控制指令汇集到一个混合优化控制分配器, 由它分派控制指令, 以最优地补偿执行器的不确定动态特性. 仿真结果表明, 该策略可综合补偿执行器的多面体不确定性, 在操纵面偏转范围内精确地跟踪虚拟指令, 保证了闭环系统的稳定性, 具有较好的鲁棒性.     

A new method for actuating parallel manipulators

This paper presents a new technique of actuating a parallel platform manipulator using shape memory alloy (SMA). This is a type of smart materials that can attain a high strength-to-weight ratio, which makes them ideal for miniature application. The work is mainly to develop a new SMA actuator and then incorporating the actuator in building the parallel manipulator prototype. The SMA used in this study is a commercial NiTi wire. The SMA wire provides an actuating force that produces a large bending and end displacement. A 3-UPU (universal–prismatic–universal) parallel manipulator using linear SMA actuators was developed. The manipulator consists of a fixed platform, a moving platform and three SMA actuators. The manipulator workspace was specified based on the restrictions due to actuator strokes and joint angle limits. System identification techniques were used to model both heating and cooling processes. An ON/OFF control was performed and the results showed closeness in simulation and experimental results. This study showed that shape memory alloy actuated beam can successfully be used to provide linear displacement. The built prototype indicates the feasibility of using SMA actuators in parallel manipulators.     

Swing-arm-type PZT dual actuator with fast seeking for optical disk drive

 In this paper, a swing-arm-type dual positioning mechanism using a voice coil motor (VCM) and bimorph PZT actuators is proposed for the possible application to the future optical disc drive actuator. A VCM is used as a coarse motion actuator, and a set of piezoelectric actuators is used for fine motion. The two pairs of PZT actuators are arranged in parallel and are carefully designed to deflect in `S' shape such that tension and compression forces are generated simultaneously and thus the hysteresis is minimized. The static and dynamic analyses are performed and the parameter studies on the key dimensions of the set of PZT actuators are investigated. For fast seeking motion, time optimal control scheme combined with PD algorithm is adopted for the fast seeking motion of VCM. Positive position feedback (PPF) control is used to suppress residual vibration for the PZT actuator beams by activating it at the end of VCM swing motion. The feasibility of the suggested actuator system and the control scheme is demonstrated through simulations and experiments. Received: 5 July 2001/Accepted: 21 December 2001     

基于神经网络PID控制的柔性微机器人系统

根据尺蠖蠕动的原理,研制了一种三自由度微型机器人内窥镜诊疗系统;该机器人由空气压橡胶驱动器驱动,通过两个气囊钳位．建立了机器人的动态模型．基于BP神经网络PID控制策略,设计了电—气脉宽调制伺服系统控制机器人的移动．用系统输出的预测值来代替实测值,计算权系数的修正量,实时改变控制参数以提高控制效果．软件仿真和实验结果都证实该方法弥补了传统PID控制方法的不足,显著改善了系统的静动态特性,是一种理想的气动微型蠕动机器人控制方法．     

Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

An IV-QR Algorithm for Neuro-Fuzzy Multivariable Online Identification

In this paper, a new algorithm for neuro-fuzzy identification of multivariable discrete-time nonlinear dynamic systems, more specifically applied to consequent parameters estimation of the neuro-fuzzy inference system, is proposed based on a decomposed form as a set of coupled multiple input and single output (MISO) Takagi-Sugeno (TS) neuro-fuzzy networks. An on-line scheme is formulated for modeling a nonlinear autoregressive with exogenous input (NARX) recurrent neuro-fuzzy structure from input-output samples of a multivariable nonlinear dynamic system in a noisy environment. The adaptive weighted instrumental variable (WIV) algorithm by QR factorization based on the numerically robust orthogonal Householder transformation is developed to modify the consequent parameters of the TS multivariable neuro-fuzzy network     

Robust Tracking Control Of The Variable Stiffness Actuator Based On The Lever Mechanism

This paper is concerned with the design of a robust adaptive tracking control scheme for a class of variable stiffness actuators (VSAs) based on the lever mechanisms. For these VSAs based on the lever mechanisms, the AwAS‐II developed at Italian Institute of Technology (IIT) is chosen as the study object, and it is an enhanced version of the original realization AwAS (actuator with adjustable stiffness). Firstly, for the dynamic model of the AwAS‐II system in the presence of parametric uncertainties, unknown bounded friction torques, unknown bounded external disturbance and input saturation constraints, by using the coordinate transformations and the static state feedback linearization, the state space model of the AwAS‐II system with composite disturbances and input saturation constraints is transformed into an uncertain multiple‐input multiple‐output (MIMO) linear system with lumped disturbances and input saturation constraints. Subsequently, a combination of the feedback linearization, disturbance observer, sliding mode control and adaptive input saturation compensation law is adopted for the design of the robust tracking controller that simultaneously regulates the position and stiffness of the AwAS‐II system. Under the proposed controller, the semi‐global uniformly ultimately bounded stability of the closed‐loop system has been proved via Lyapunov stability analysis. Simulation results illustrate the effectiveness and the robustness of the proposed robust adaptive tracking control scheme.     

Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application

A direct adaptive approach is developed for control of a class of multi-input multi-output (MIMO) nonlinear systems in the presence of uncertain failures of redundant actuators. An adaptive failure compensation controller is designed which is capable of accommodating uncertainties in actuator failure time instants, values and patterns. A realistic situation is studied with fixed grouping of actuators and proportional actuation within actuator groups. The adaptive control system is analyzed, to show its desired stability and asymptotic tracking properties in the presence of actuator failure uncertainties. As an application, such an adaptive controller is used for actuator failure compensation of a twin otter aircraft longitudinal model, with design conditions verified and control structure and adaptive laws developed for a nonlinear aircraft dynamic model. The effectiveness of adaptive failure compensation is demonstrated by simulation results.     

Adaptive actuator failure compensation for multivariable feedback linearizable systems

A feedback linearization‐based adaptive control scheme is developed for multivariable nonlinear systems with redundant actuators subject to uncertain failures. Such an adaptive controller contains a direct adaptive actuator failure compensator to compensate the uncertain actuator failure, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. The key new design feature is the estimation of both the failure patterns and the failure values, for direct adaptive actuator failure compensation, newly developed for multivariable feedback linearizable nonlinear systems. With direct control signal adaptation, the adaptive failure compensation design ensures closed‐loop stability and asymptotic output tracking in the presence of actuator failure uncertainties. Simulation results from an application to attitude control of a near‐space vehicle dynamic model are presented to verify the desired system performance with adaptive actuator failure compensation. Copyright © 2015 John Wiley & Sons, Ltd.     

执行器约束下基于数据驱动的参数化前馈控制器设计

针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.     

四旋翼无人机姿态系统的非线性容错控制设计

本文研究了四旋翼无人机执行器发生部分失效时的姿态控制问题.通过分析其动力学特性,将执行器故障以乘性因子加入系统模型,得到执行器故障情况下四旋翼无人机的姿态动力学模型.在同时存在未知外部扰动和执行器故障的情况下,设计了一种基于自适应滑模控制的容错控制器.利用基于Lyapunov的分析方法证明了所设计控制器的渐近稳定性.在四旋翼无人机实验平台上进行了实验,验证了该算法对存在未知外部扰动和执行器部分失效时四旋翼无人机的姿态控制具有较好的鲁棒性.     

一般严格反馈型非线性系统的自适应控制

研究一般严格反馈型非线性系统的控制问题．假设系统的对象模型、状态均未知,只有输出是可测的．应用自适应模糊神经推断系统辨识对象模型,状态观测器设计为Luenberger型,控制器由反步控制、变结构控制和3层神经网络直接控制综合而成．理论分析和仿真研究都说明此方案能够有效地控制只有输出可测的一般严格反馈型非线性系统．     

Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear systems.Both asymmetric output constraints and input saturation are considered.An asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints.A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear dynamics.To avoid the"explosion of complexity",the dynamic surface control(DSC)technique is employed to filter the virtual control signal of each subsystem.To deal with the actuator saturation,an additional auxiliary dynamical system is designed.It is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately bounded.Two simulation examples are conducted to verify the presented adaptive fuzzy controller design.     

Self-stabilizing somersaults

We investigate the open-loop stability of a planar biped robot performing a periodic motion of forward somersaults with alternating single-leg contacts. The robot has a trunk and two actuated telescopic legs with point feet which are coupled to the trunk by actuated hinges. There is compliance and damping in the hip and in the legs. The concept of open-loop control implies that all actuators of the system receive predetermined inputs that are never altered by any feedback interference. Only with the right choice of model parameters and actuator inputs is it possible to create such self-stabilizing motions exploiting the natural stability properties of the system. These unknowns have been determined using special-purpose stability-optimization methods. The resulting motion is not only stable, but also a more efficient form of forward motion than running for the investigated robot.     

An adaptive integral sliding mode FTC scheme for dissimilar redundant actuation system of civil aircraft

ABSTRACT

This paper proposed a new adaptive integral sliding mode FTC scheme to deal with the actuator faults and failure. The scheme combines integral sliding mode control, control allocation scheme and adaptive strategy. The unknown actuator faults are handled by adaptive modulation gain of nonlinear ISMC law. To cope with complete failure, control allocation scheme is integrated with the baseline controller to provide tolerance. The proposed strategy relies on the estimate of actuator effectiveness. Therefore, an adaptive sliding mode observer based fault reconstruction scheme is proposed in this paper. The proposed scheme is implemented on dissimilar redundant actuation system driven by hydraulic and electro-hydraulic actuators. In nominal and faulty conditions, both actuators are contributing to achieving the desired control surface deflection. However, when the actuator failure occurs, the control signals are reallocated to the redundant actuator. The problem of dynamics mismatch is addressed using fractional order controller designed in an inner loop. The comparison with the existing literature is also conducted in the simulation to validate the dominant performance.     

PDC控制器在车辆半主动悬架上的应用研究

讨论了整车八自由度模型并行分布补偿(PDC)控制器的设计,并求取了公共矩阵P及其对控制器稳定性的影响.基于自适应神经网络的神经网络模糊推理系统ANFIS的自学习和非线性逼近能力,提取模糊控制规则,增强了控制器对于不同路面的适应能力.在整车悬架T-S模糊动态模型的基础上,进行了Matlab软件仿真.仿真实验表明,该控制器对于悬架整体性能有所改善.     

轮式移动机器人的数据驱动轨迹跟踪滑模约束控制

针对有输入饱和约束的轮式移动机器人(WMR)的轨迹跟踪问题,提出一种抗饱和无模型自适应积分终端滑模控制方案.该方案基于紧格式动态线性化技术,构建WMR系统的在线数据驱动模型.在积分终端滑模控制器设计过程中,引入动态抗饱和补偿器,以解决WMR系统轨迹跟踪过程中执行器饱和问题.控制器设计仅利用控制系统的输入输出数据,与WMR系统模型信息无关.因此,针对不同类型的WMR系统,该方案均可实现.最后,通过仿真实验将所提出的方法与PID方法的控制效果进行对比,仿真结果表明,所提出的控制算法的跟踪误差更小且响应速度更快.