Electrostatically Actuated Robotic Fish: Design and Control for High-Mobility Open-Loop Swimming

This paper presents a project that aims at fabricating a biologically inspired robotic fish. The robotic fish is designed to be capable of propelling itself through oscillations of a flexible caudal fin, like a real underwater fish. In this paper, we describe the design features that underlie the operation of the robotic fish. These features include a unique actuator referred to as electrostatic film motor and a light and flexible power transmission system. The electrostatic film motor is made of two pieces of flexible printed circuit film and can be utilized as a new-type artificial muscle. The power transmission system permits reciprocating power to be converted to periodic oscillations and distributed to the caudal fin. Based on several design considerations inspired by biological concepts, we propose several open-loop swimming control strategies for the constructed robotic fish to accomplish fish-like motion (i.e., cruising, turning, and diving). Experiments of Seidengyo I, the first prototype of our electrostatic fish family, are carried out to confirm the validity of the original design and control. We further design Seidengyo II to improve on Seidengyo I and show the results of the experiments.     

Real-Time Models of Electrostatically Actuated Cantilever Probes With Integrated Thermal Sensor for Nanoscale Interrogation

Microcantilevers with integrated thermal sensor for topography measurement, which can be electrostatically actuated, are well suited for a highly parallel dynamic-mode operation where multiple cantilevers scan the media. Interpretation of data in dynamic-mode operation utilizing such cantilevers is complex because of diverse forces acting on the cantilever that include electrostatic, interatomic, structural, thermal, and, possibly, magnetic forces. In addition, the thermal sensor introduces new dynamics making interpretation of measured data challenging. In this paper, tractable models that are suited for real-time purposes, which can quantitatively predict the cantilever motion and the thermal-sensor measurement, are presented. Furthermore, it is demonstrated that all parameters of the model can be estimated solely from thermal-sensor data. This paper also provides a comprehensive understanding of the dynamics of the thermal sensor.$hfill$ [2009-0041]      

Robust Control of Spacecraft: Application to an Actuated Simulator

International Journal of Control, Automation and Systems - In this article, a hardware-in-the-loop implementation of two robust controllers based on high-order sliding mode and μ-synthesis...     

一种自适应混沌同步方法及其在信息传输中的应用

基于自适应误差反馈策略,利用同步误差对系统参数进行调整,并采取策略对信道噪声进行一定程度的补偿,实现了两个具有参数失配的混沌系统的同步。并在此基础上利用参数调制和参数估计策略,实现了信息传输。     

桁架自适应结构振动控制仿真实验研究

基于MATLAB／dSPACE综合仿真实验环境,在有限元结构分析的基础上,在MATLAB／Simulink中进行桁架自适应结构的数学模型搭建和控制方案设计仿真,由dSPACE软硬件系统实现自适应结构的主动元件驱动和传感器数据采集,从而实现半物理仿真。实验表明基于MATLAB／dSPACE综合实验环境桁架振动受到很好的抑制,证明MATLAB／dSPACE试验环境高效简洁,控制方法行之有效。     

Internal Preload Control of Redundantly Actuated Parallel Manipulators—Its Application to Backlash Avoiding Control

Redundant actuation of parallel manipulators can lead to internal forces without generating end-effector forces (preload). Preload can be controlled in order to prevent backlash during the manipulator motion. Such control is based on the inverse dynamics. The general solution of the inverse dynamics of redundantly actuated parallel manipulators is given. For the special case of simple overactuation an explicit solution is derived in terms of a single preload parameter. With this formulation a computational efficient open-loop preload control is developed and applied to the elimination of backlash. Its simplicity makes it applicable in real-time applications. Results are given for a planar 4RRR manipulator and a spatial heptapod.     

补偿模糊神经网络自适应逆控制的仿真研究

该文应用的补偿模糊神经网络（CFNN）是结合补偿模糊逻辑和神经网络的混合系统。由于引入补偿神经元使网络容错性更高,系统更稳定;同时模糊运算采用动态的、全局优化运算,并在神经网络学习算法中动态优化补偿模糊运算,使网络更适应,训练速度更快。将补偿模糊神经网络与白适应逆控制原理结合应用到某位置伺服系统噪声消除控制中,并同用BP网络,传统PID控制和常规模糊神经网络控制效果比较来证明此方法的优越性。仿真结果表明补偿模糊神经网络自适应逆控制在缩短训练时间,提高控制精度等方面都有显著改善。     

A Phenomenological Discrete Brittle Damage-Mechanics Model for Fatigue of MEMS Devices With Application to LIGA Ni

Fatigue initiation and failure of various microelectromechanical systems (MEMS) is of significant importance as they gain widespread acceptance in sensors and electronics. This paper presents an approach for utilizing available experimental fatigue data to evaluate the fatigue lives of MEMS components. The approach is based on a phenomenological discrete material representation in which a domain is represented by a collection of rigid elements that interacts via springs along their boundaries. The principles of continuum damage mechanics are used to degrade the spring stiffnesses as brittle damage occurs when the domain is subjected to fatigue loading. The model utilizes experimental stress–life data for LIGA Ni to identify the material properties used in the model. The proposed model captures the statistical distribution of material properties and geometrical randomness of the microstructure commonly observed in a wide variety of MEMS. Consequently, simulations that account for the variability in fatigue life can be readily performed. The model is applied to a dog-bone-shaped specimen to evaluate the influence of material heterogeneity and material flaws on fatigue crack initiation life and scatter. The ability of the model to predict the fatigue life of different types of MEMS devices and loading conditions is also demonstrated by simulating the fatigue stress–life behavior of a MEMS resonator support beam. $hfill$[2008-0087]      

模糊控制在摆缸位置伺服系统中的应用

由于摆动气缸的摩擦转矩、空气可压缩性、比例阀的压力特性等非线性因素的存在,要得到摆缸伺服系统精确的数学模型十分困难,而且气动系统易受周围环境因素的影响,当采用传统PID控制时,很难使系统长时间保持良好的控制效果,尤其是当有一定的外界干扰之后会导致系统工作不稳定.利用Matlab实时仿真模块的功能,把模糊自适应整定PID的控制算法引入到伺服系统的控制器设计中,搭建的半实物仿真模型,对系统进行半实物仿真实验,并通过不断地在线修改控制规则,提高控制器的控制性能,实验结果表明,模糊PID控制效果良好.     

无刷直流电机模糊自适应PID控制研究与仿真

针对传统的PID控制方式在对无刷直流电机系统控制时,存在精度低、抗干扰能力弱等不足,提出一种基于参数自适应模糊PID集成控制策略。首先,分析了无刷直流电机的数学模型,建立了基于双闭环调速系统的无刷直流电机控制系统模型,并对无刷直流电机双闭环系统转速进行模糊PID控制;然后,详细分析了建立该模糊自适应PID控制器的设计方法,提出一种优化模糊算子的优化方法,并运用仿真软件Matlab/Simulink实现了系统的设计和仿真;最后,在相同环境下,对比传统PID控制和模糊自适应PID集成控制两种控制策略的仿真结果。仿真结果表明,模糊自适应PID集成控制算法能使无刷直流电机双闭环控制系统具有更好的动、静态性能及较强的自适应能力。     

自适应光学波前控制算法的脉动阵列结构

提出了一种用于自适应光学波前控制算法的脉动阵列结构.该结构将波前控制算法分为递归运算和卷积运算两部分后,采用规范映射方法将其分别映射到脉动阵列,再将两个阵列链接以实现单路的波前控制运算.同时将处理单元共享引入阵列,并设计了以队列方式进行数据传递的脉动通信模式,将n路控制电压以分时复用的方式在一个阵列中串行计算以提高处理单元的利用率,减少硬件资源占用.实验表明,该结构具有控制流、数据流简单,硬件资源少等优点,且实时性强、模块化程度高.     

Application of Model Reference Adaptive Control to Industrial Robot Impedance Control

The paper deals with the application of model reference adaptive control to robot impedance control, which is actually a technique of steering the end-effector on a prescribed path and satisfying a prescribed dynamic relationship between the force and the end-effector position. Due to unknown parameters of the environment (stiffness, exact position), a model reference algorithm is proposed which differs from classical algorithms in its method of excitation. The results of the proposed procedure are illustrated by implementation on the ASEA IRb 6 industrial robot.     

约束自适应反步法及其在飞行控制中的应用

为解决系统含有参数不确定性和(状态)执行器受限条件下的鲁棒自适应控制问题,将SISO系统指令滤波器推广到MIMO系统,提出一种基于指令滤波的自适应块控反步方法。在设计(虚拟)控制律时引入指令滤波器,在克服标准反步法虚拟控制律导数计算膨胀问题的同时,能够施加系统状态和执行器的限制条件,并通过引入扩展跟踪误差,有效解决了约束条件下参数估计过程的稳定性和跟踪误差收敛问题。给出了Lyapunov闭环稳定性证明,并将该方法应用于含有未知气动参数和输入饱和约束的飞行器非线性姿态控制器设计。6自由度飞行仿真表明,该控制器具有良好的指令跟踪能力和较强的鲁棒性。     

Adaptive Critic Designs for Discrete-Time Zero-Sum Games With Application to H∞ Control

In this correspondence, adaptive critic approximate dynamic programming designs are derived to solve the discrete-time zero-sum game in which the state and action spaces are continuous. This results in a forward-in-time reinforcement learning algorithm that converges to the Nash equilibrium of the corresponding zero-sum game. The results in this correspondence can be thought of as a way to solve the Riccati equation of the well-known discrete-time H_infin optimal control problem forward in time. Two schemes are presented, namely: 1) a heuristic dynamic programming and 2) a dual-heuristic dynamic programming, to solve for the value function and the costate of the game, respectively. An H_infin autopilot design for an F-16 aircraft is presented to illustrate the results     

MATLAB在综合模糊自适应控制系统仿真中的应用

该文介绍了用MATLAB(SIMULINK) 语言针对一类非线性系统把模糊控制,模糊逻辑逼近及模糊滑模控制相结合的一种综合自适应模糊滑模控制方法,进行仿真的步骤和方法.直接和间接自适应模糊控制器,它们只能利用模糊控制规则或模糊描述信息.而综合自适应模糊控制器能利用上述两种信息.仿真表明算法的有效性.可以看出SIMULIK是控制系统动态仿真的有力工具,而且相对于以往任何一种仿真方法,MATLAB 语言更方便、高效,具有无可比拟的优越性.     

A Discrete-Time Periodic Adaptive Control Approach for Time-Varying Parameters With Known Periodicity

A periodic adaptive control approach is proposed for a class of nonlinear discrete-time systems with time-varying parametric uncertainties that are periodic, and the only prior knowledge is the periodicity. The new adaptive controller updates the parameters and the control signal periodically in a point-wise manner over one entire period, and in the sequel, achieves the asymptotic tracking convergence. The result is further extended to a scenario with mixed time-varying and time-invariant parameters, and a hybrid classical and a periodic adaptation law is proposed to handle the scenario more appropriately. The extension of the periodic adaptation to systems with unknown input gain, higher order dynamics, and tracking problems is also discussed.     

基于MEMS CAD加工工艺仿真研究

在MEMS设计中需要解决工艺设计的问题,基于MEMS CAD软件对MEMS关键加工工艺进行仿真研究,对于MEMS工艺水平的提高有着重要的意义.本文首先探讨了MEMS加工工艺种类和工艺建模,然后以单晶硅各向异性腐蚀为例,借助MEMS CAD软件ACES对硅的湿法腐蚀工艺情况进行了仿真,其仿真结果对于基于MEMS CAD加工工艺进一步研究有一定的参考价值.     

采用单神经元自适应控制高精度空调系统仿真

暖通空调领域中的被控对象(空调房间)大多具有大滞后、慢时变、非线性特点,且受各种不确定因素影响,经典控制方法难以实现精确控制。该文正是针对上述实际,将具有自学习、自适应功能的神经元控制算法引入高精度空调控制领域,并通过MATLAB仿真,考察了系统的控制效果。仿真结果表明此控制方法具有超调小、抗干扰能力强、控温精度高的优点,从而为空调系统的高精度控制提出了一个新的途径。     

A Combined Adaptive Law for Fuzzy Iterative Learning Control of Nonlinear Systems With Varying Control Tasks

To deal with the iterative control of uncertain nonlinear systems with varying control tasks, nonzero initial resetting state errors, and nonrepeatable mismatched input disturbance, a new adaptive fuzzy iterative learning controller is proposed in this paper. The main structure of this learning controller is constructed by a fuzzy learning component and a robust learning component. For the fuzzy learning component, a fuzzy system used as an approximator is designed to compensate for the plant nonlinearity. For the robust learning component, a sliding-mode-like strategy is applied to overcome the nonlinear input gain, input disturbance, and fuzzy approximation error. Both designs are based on a time-varying boundary layer which is introduced not only to solve the problem of initial state errors but also to eliminate the possible undesirable chattering behavior. A new adaptive law combining time- and iteration-domain adaptation is derived to search for suitable values of control parameters and then guarantee the closed-loop stability and error convergence. This adaptive algorithm is designed without using projection or deadzone mechanism. With a suitable choice of the weighting gain, the memory size for the storage of parameter profiles can be greatly reduced. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. Moreover, the norm of tracking state error vector will asymptotically converge to a tunable residual set even when the desired tracking trajectory is varying between successive iterations.