Nonlinear control techniques for an atomic force

Two nonlinear control techniques are proposed for an atomic force microscope system. Initially ,a learning- based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories. Specifically ,the control approach utilizes a learning- based feedforward term to compensate for periodic dynamics and high- gain terms to account for non-periodic dynamics. An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters. Simulation results are provided to illustrate the efficacy and performance of the control strategies.     

Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system.Initially,a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories.Specifically,the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics.An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters.Simulation results are provided to illustrate the efficacy and performance of the control strategies.     

Modeling and control of an atomic force microscope using a piezoelectric tuning fork for force sensing

This paper investigates modeling and control issues associated with an atomic force microscope which uses a piezoelectric tuning fork for atomic force sensing. In the modeling part, the dynamics of piezoelectric tuning fork and its atomic interaction with the test sample via the scanning tip are physically characterized. The modeling results explain not only the atomic force sensing mechanism but also the important characteristics observed in experimental frequency responses. In the control part, an LTR controller is designed to maximize the controller bandwidth and yet maintain robustness against unmodeled dynamics and different operating conditions. Scanning results indicate that the LTR controller exhibits superior performance than a conventional PI controller.     

原子力显微镜系统广义预测控制与成像

在原子力显微镜(atomic force microscope,AFM)扫描样品时,控制参数调节困难,依赖于操作经验.本文基于在线动态模型辨识,提出了一种AFM系统广义预测自校正控制与成像方法.首先,利用CARIMA(controlled autoregressive and moving-average)参数模型来描述局部线性化后的AFM系统模型,并通过在线动态模型辨识得到线性化模型的参数;基于该模型,采用基于GPC(generalized predictive control)的优化方法,在线求解类PI(proportional integral)控制器的参数,进而得到一种具有控制参数自动调整功能的AFM成像方法.为了验证本文方法的有效性,进行了仿真与实验测试.结果表明,在AFM扫描速度不同或PI参数选择不恰当的情况下,该方法能够自动地调整控制器参数,从而减小控制误差,提高成像精度.     

Robust nonlinear control of atomic force microscope via immersion and invariance

This paper reports an immersion and invariance (I&I)–based robust nonlinear controller for atomic force microscope (AFM) applications. The AFM dynamics is prone to chaos, which, in practice, leads to performance degradation and inaccurate measurements. Therefore, we design a nonlinear tracking controller that stabilizes the AFM dynamics around a desired periodic orbit. To this end, in the tracking error state space, we define a target invariant manifold, on which the system dynamics fulfills the control objective. First, considering a nominal case with full state measurement and no modeling uncertainty, we design an I&I controller to render the target manifold exponentially attractive. Next, we consider an uncertain AFM dynamics, in which only the displacement of the probe cantilever is measured. In the framework of the I&I method, we recast the robust output feedback control problem as the immersion of the output feedback closed‐loop system into the nominal full state one. For this purpose, we define another target invariant manifold that recovers the performance of the nominal control system. Moreover, to handle large uncertainty/disturbances, we incorporate the method of active disturbance rejection into the I&I output feedback control. Through Lyapunov‐based analysis of the closed‐loop stability and robustness, we show the semiglobal practical stability and convergence of the tracking error dynamics. Finally, we present a set of detailed, comparative software simulations to assess the effectiveness of the control method.     

Integrated design of the feedback controller and topography estimator for atomic force microscopy

In atomic force microscopy (AFM) the force between the measurement tip and the sample is controlled in a feedback loop to prevent damage of the tip and sample during imaging, and to convert the measurement of the tip–sample force into an estimate of the sample topography. Dynamical uncertainties of the system limit the achievable control bandwidth and the accuracy of the topography estimation. This paper presents an integrated approach to design a feedback controller and topography estimator, taking into account the dynamical uncertainties of the system. The proposed methodology is experimentally demonstrated on a commercial AFM system, showing a direct trade-off between the control bandwidth and the accuracy of the topography estimation.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton

Exoskeleton robots and their control methods have been extensively developed to aid post-stroke rehabilitation. Most of the existing methods using linear controllers are designed for position control and are not suitable for human-machine interaction （HMI） force control, as the interaction system between the human body and exoskeleton is uncertain and nonlinear. We present an approach for HMI force control via model reference adaptive impedance control （MRAIC） to solve this problem in case of index finger exoskeleton control. First, a dynamic HMI model, which is based on a position control inner loop, is for- mulated. Second, the theoretical MRAC framework is implemented in the control system. Then, the adaptive controllers are designed according to the Lyapunov stability theory. To verify the performance of the proposed method, we compare it with a proportional-integral-derivative （PID） method in the time domain with real experiments and in the frequency domain with simu- lations. The results illustrate the effectiveness and robustness of the proposed method in solving the nonlinear HMI force control problem in hand exoskeleton.     

Fabrication of improved piezoresistive silicon cantilever probes for the atomic force microscope

This paper describes an improved design for a monolithic silicon atomic force microscope (AFM) probe using piezoresistive sensing. The probe is V shaped, with a sharp tip at the free end and two piezoresistors at the root, and is fabricated using silicon-on-insulator (SOI) starting material. The maximum sensitivity of the AFM probe is measured to be 4.0(± 0.1) × 10⁻⁷ Å⁻¹, which is larger than that of the previous parallel-arm piezoresistive AFM probe. The measured results are in reasonable agreement with the values predicted by theory. The minimum detectable force and minimum detectable deflection of the AFM probes are predicted to be 1.0 × 10⁻¹⁰ N and 0.29 Å_r.m.s., respectively, using a Wheatstone bridge arrangement biased at a voltage of ± 5 V and bandwidth of 10 Hz–1 kHz.     

Atomic-scale friction control by vibration using friction force microscope

Manipulation of friction at the nanoscale has been traditionally approached by chemical means (lubrication). Recent friction force microscopy (FFM) experiments demonstrated that it can be done mechanically by applying vibration to accessible elements of the system. This paper provides analytic understanding on why vibration can reduce friction based on a 1D model imitating the FFM tip moving on a substrate. Open-loop stability is first studied, and a feedback vibration control is then designed using the accessible variable. Comparing to the open-loop system, friction force is significantly reduced in the closed-loop system. Numerical simulations show satisfactory performances.     

Adaptive trajectory control of microcantilever's tip utilised in atomic force microscopy-based manipulation

This article presents a hybrid distributed-parameters model and an adaptive control framework for microcantilevers utilised in atomic force microscope systems for controlled force manipulations. The model assumes a general nonlinear interaction force between the microcantilever's tip and the surface of the sample. This interaction force includes the sample's surface and probe's tip distance as well as the first and second derivatives of this force implicitly. Despite such detailed modelling of interaction force, there are a number of uncertainties including tip mass, damping coefficients and nature of the interaction force that would affect the response of the system and hence, an adaptive controller is needed to compensate for these unmodelled dynamics and uncertainties. Unlike the current practices that deal with the lumped-parameters model of the cantilever, a comprehensive distributed-parameters model based on the Euler–Bernoulli theory is considered here. An adaptive controller is then designed such that by giving a force input to the base of the microcantilever, the tip of the microcantilever can track a desired trajectory despite the flexibility of the microcantilever and aforementioned uncertainties. Extensive simulation results are provided to illustrate that the microcantilever's tip can asymptotically follow a harmonic trajectory even for a system with higher modes of vibration when it is designed based on single-mode model.     

基于几何特征相似度评价的跨尺度显微图像配准

原子力显微镜能够在光学显微镜的协助下, 克服自身成像范围的限制获得更大的成像视野, 同时保证纳米 级的成像精度. 该方法需要实现原子力显微镜成像结果在光学视野中的精确定位, 而解决该问题的关键是进行两种 显微图像之间的准确配准. 因此, 本文提出了一种基于几何特征相似度评估的跨尺度图像配准算法, 为进一步在原 子力/光学显微镜共焦系统中实现精确定位和成像提供了基础. 具体而言, 本文首先利用原子力显微镜的探针在样 品表面进行压印, 刻画出固定尺寸的几何图案,用以标定原子力显微镜和光学显微镜成像尺度之间的比例, 为图像 配准提供先验知识. 随后, 本文设计了一种先进的图像处理算法, 分别提取原子力/光学显微镜图像中几何图案的特 征, 并将其存储为内角向量和边长向量. 最后, 基于成像尺度比例和几何特征, 提出了一种新型的几何特征相似度 评价函数, 通过对内角特征相似度和边长特征相似度进行加权融合, 实现高精度的跨尺度显微图像配准. 实验部分 针对四种不同几何图案进行图像配准, 并对实验结果进行详细分析, 验证了本文方法的良好性能.     

Neural control strategy of constant cutting force system in end milling

This paper discusses the application of neural adaptive control strategy to the problem of cutting force control in high speed end milling operations. The research is concerned with integrating adaptive control and a standard computer numerical controller (CNC) for optimizing a metal-cutting process. It is designed to adaptively maximize the feed rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjusting feed rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.     

Position/force hybrid control system for high precision aligning of small gripper to ring object

A position/force hybrid control system based on impedance control scheme is designed to align a small gripper to a special ring object. The vision information provided by microscope vision system is used as the feedback to indicate the position relationship between the gripper and the ring object. Multiple image features of the gripper and the ring object are extracted to estimate the relative positions between them. The end-effector of the gripper is tracked using the extracted features to keep the gripper moving in the field of view. The force information from the force sensor serves as the feedback to ensure that the contact force between the gripper and the ring object is limited in a small safe range. Experimental results verify the effectiveness of the proposed control strategy.     

用侧向力显微镜对力/电/热耦合作用下金表面力学行为的研究

对微构件力/电/热等多域场下力学行为的研究有着重要的意义,利用原子力显微镜采用纳米压痕法对样品金在力-电-热耦合作用下的表面力学性能进行了研究,实验表明在压痕过程中,无论在无电场作用下还是有电场作用下,压痕周围的金属由于挤压形成了凸起,从扫描得到的形貌图中可以明显的看出,压痕周围的亮区为材料被挤出的区域.此外,在外加直流电场作用下,随电流的增大,硬度值有增大的趋势,而残余压痕和弹性模量都呈现减小的趋势.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

The adaptive control using BP neural networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification （NNI） and control （NNC）, respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.     

Decentralized adaptive coordinated control of multiple robot arms without using a force sensor

This paper presents a distributed adaptive coordinated control method for multiple robot arms grasping a common object. The cases of rigid contact and rolling contact are analyzed. In the proposed controller, the dynamic parameters of both object and robot arms are estimated adaptively. The desired motions of the robot arms are generated by an estimated object reference model. The control method requires only the measurements of the positions and velocities of the object and robot arms, but not the measurements of forces and moments at contact points. The asymptotic convergence of trajectory is proven by the Lyapunov-like Lemma. Experiments involving two robot arms handling a common object are shown.     

A neural network-based approach for an assembly cell control

In several robotics applications, the robot must interact with the workspace, and thus its motion is constrained by the task. In this case, pure position control will be ineffective since forces appearing during the contacts must also be controlled. However, simultaneous position and force control called hybrid control is then required. Moreover, the nonlinear plant dynamics, the complexity of the dynamic parameters determination and computation constraints makes more difficult the synthesis of control laws. In order to satisfy all these constraints, an effective hybrid force/position approach based on artificial neural networks for multi-inputs/multi-outputs systems is proposed. This approach realizes, simultaneously, an identification and control of systems, and it is implemented according to two phases: At first, a neural observer is trained off-line on the basis of the data acquired during contact motion, in order to realize a smooth transition from free to contact motion. Then, an online learning of the neural controller is implemented using neural observer parameters so that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot and the environment. A typical example on which we shall focus is an assembly task. Experimental results on a C5 links parallel robot demonstrate that the robot's skill improves effectively and the force control performances are satisfactory, even if the dynamics of the robot and the environment change.     

基于Matlab机械臂力控系统仿真研究

以位置控制为主的机械臂控制方法已不能满足某些复杂环境（装配、抛光、去毛刺）的应用要求,控制机械臂与环境间的接触力已成为机器人学研究的一个热点。提出一种在Matlab/SimMechanics环境下平面二自由度机械臂力控制的仿真研究方法。在平面中模拟机械臂与环境的接触面,设计振荡抑制控制器,实现机械臂与环境间接触力的控制,以及机械臂与刚性环境碰撞接触过程中冲击振荡阶段的振荡抑制,生成机械臂期望的运动轨迹。仿真结果表明,该方法可实现特定作业下机械臂与环境间接触力的控制。