Flexible-link robots with combined trajectory tracking and vibration control

This work deals with asymptotic trajectory tracking and active damping injection on a flexible-link robot by application of Multiple Positive Position Feedback. The flexible-link robot is modeled and validated by using finite element methods and experimental modal analysis, and then a reduced order model of the flexible-link robot dynamics, up to the first dominant vibration modes, is employed for experimental evaluation on a test rig. Then, a combined control scheme is synthesized in two parts: first, a Sliding-Mode Control based on a cascaded Proportional-Integral-Derivative for regulation and trajectory tracking tasks, via a direct current motor torque as the control input for the overall system dynamics, and, second, a Multiple Positive Position Feedback for active vibration control and attenuation of residual vibrations on the tip position, via the input voltage applied to a piezoelectric patch actuator attached directly on the flexible beam. The results are evaluated on an experimental platform, where the dynamic performance of the overall active vibration control scheme leads to fast and effective tracking results, with damping ratios increased up to 300%.     

柔性杆柔性铰机器人动力学分析

研究由N柔性杆和N柔性铰组成的空间机器人的动力学问题．把柔性铰简化成一个线性扭转弹簧,采用假设模态法表示杆件的弹性变形,运用Kane方法对全柔机器人进行动力学建模,推导出完整的系统动力学方程组．通过一个数值仿真算例,验证所做工作的可行性,并分析了柔性效应对机器人动力学响应的影响．     

Iterative identification and control design using finite-signal-to-noise models

In this paper, a model is said to be validated for control design if using the model-based controller, the closed loop performance of the real plant satisfies a specified performance bound. To improve the model for control design, only closed loop response data is available to deduce a new model of the plant. Hence the procedure described herein involves three steps in each iteration: (i) closed loop identification; (ii) plant model extraction from the closed loop model; (iii) controller design. Thus our criteria for model validation involve both the control design procedure by which the closed loop system performance is evaluated, and the identification procedure by which a new model of the plant is deduced from the closed loop response data. This paper proposes new methods for both parts, and also proposes an iterative algorithm to connect the two parts. To facilitate both the identification and control tasks, the new finite-signal-to-noise (FSN) model of linear systems is utilized. The FSN model allows errors in variables whose noise covariances are proportional to signal covariances. Allowing the signal to noise ratios to be bounded but uncertain, a control theory to guarantee a variance upper bound is developed for the discrete version of this new FSN model. The identification of the closed loop system is accomplished by a new type of q-Markov Cover, adjusted to accommodate the assumed FSN structure of the model. The model of the plant is extracted from the closed loop identification model. This model is then used for control design and the process is repeated until the closed loop performance validates the model. If the iterations produce no such a controller, we say that this specific procedure cannot produce a model valid for control design and the level of the required performance must be reduced.     

A velocity observer design for tracking task-based motions of unicycle type mobile robots

The paper presents a design of a nonlinear velocity observer and its application within a model-based tracking control strategy for tracking task-based motions of unicycle type mobile robots. The strategy is the model reference tracking control strategy for programmed motion and it enables switching between controllers employed in it to improve a tracking precision as well as switching between coordinates used for modeling based on a type of a nonholonomic system. The strategy benefits by adding the velocity observer to its architecture due to the reduction of a number of measurements needed for feedback tracking.     

Mathematical modeling and fuzzy control of a flexible-link robot arm

In this paper, the Timoshenko theory is applied to investigate a new mathematical model for the “shoulder-elbow-like” single flexible-link robot arm with dampings. Detailed analysis and derivation are given to support the mathematical modeling of this particular flexible mechanism. A new design of a fuzzy-logic-based (PI + D)² control scheme is developed for both vibration suppression and set-point tracking. Computer simulation results for the modeling are performed to observe the significant vibration modes, and simulation results for the control scheme demonstrate that the controllers perform very well for the tracking based on this flexible-link model. A newly developed method for stability analysis using the “two-straight-lines” criterion is also presented.     

Robust Optimal Trajectory Planning for Robots by Stochastic Optimization

In the optimal control of industrial, field or service robots, the standard procedure is to determine first offline a reference trajectory and a feedforward control, based on some selected nominal values of the unknown stochastic model parameters, and to correct then the inevitable and increasing deviation of the state or performance of the robot from the prescribed state or performance of the system by online measurement and control actions. Due to the stochastic variations of the model parameters, increasing measurement and correction actions are needed during the process. By optimal stochastic trajectory planning (OSTP), based on stochastic optimization methods, the available a priori and sample information about the robot and its working environment is incorporated into the control process. Consequently, more robust reference trajectories and feedforward controls are obtained which cause much less online control actions. In order to maintain a high quality of the guiding functions, the reference trajectory and the feedforward control can be updated at some later time points such that additional information about the control process is available. After the presentation of the Adaptive Optimal Stochastic Trajectory Planning (AOSTP) procedure based on stochastic optimization methods, several numerical techniques for the computation of robust reference trajectories and feedforward controls under real-time conditions are presented. Additionally, numerical examples for a Manutec r3 industrial robot are discussed. The first one demonstrates real-time solutions of (OSTP) based on a sensitivity analysis of a before-hand calculated reference trajectory. The second shows the differences between reference trajectories based on deterministic methods and the stochastic methods introduced in this paper. Based on simulations of the robots behavior, the increased robustness of stochastic reference trajectories is demonstrated.     

Controlling Hopf bifurcation and chaos in a small power system

For the power systems, the stabilization and tracking of voltage collapse trajectory, which involves severe nonlinear and nonstationary (unstable) features, is somewhat difficult to achieve. In this paper, we choose a widely used three-bus power system to be our case study. The study shows that the system experiences a Hopf bifurcation point (subcritical point) leads to chaos throughout period-doubling route. A model-based control strategy based on global state feedback linearization (GLC) is applied to the power system to control the chaotic behavior. The performance of GLC is compared with that for a nonlinear state feedback control.     

Distributed adaptive formation tracking using quantized feedback communication for networked mobile robots with unknown wheel slippage

In this study, we consider a quantized-feedback-communication-based control design problem for the distributed adaptive formation tracking of multiple nonholonomic mobile robots with unknown slippage constraints under capacity-limited network control environments. Uniform-hysteretic quantizers are employed to quantize all the inputs and states of robots and the quantized position information of each robot is only transmitted to neighboring robots through directed networks. Compared with existing literature related to the robot formation, the primary contribution of this paper lies in establishing a novel local adaptive control design methodology to deal with the discontinuity problem caused by using the quantized states of each follower and the quantized position communication of neighboring robots. In the proposed strategy, the communication of the orientations and velocities of neighboring robots is not required for the local control design of follower robots. Moreover, quantized-states-based adaptive compensation schemes are constructed for the effects of signal quantization and wheel slippage. Based on the analysis of quantization errors, the practical stability strategy of the overall closed-loop formation system is derived with the convergence of local tracking errors. Simulation results clarify the proposed formation strategy.     

A bootstrap-based aggregate classifier for model-based clustering

In model-based clustering, a situation in which true class labels are unknown and that is therefore also referred to as unsupervised learning, observations are typically classified by the Bayes modal rule. In this study, we assess whether alternative classifiers from the classification or supervised-learning literature—developed for situations in which class labels are known—can improve the Bayes rule. More specifically, we investigate the performance of bootstrap-based aggregate (bagging) rules after adapting these to the model-based clustering context. It is argued that specific issues, such as the label-switching problem, have to be carefully addressed when using bootstrap methods in model-based clustering. Our two Monte Carlo studies show that classification based on the Bayes rule is rather stable and difficult to improve by bootstrap-based aggregate rules, even for sparse data. An empirical example illustrates the various approaches described in this paper.     

Cooperative Sensing and Distributed Control of a Diffusion Process Using Centroidal Voronoi Tessellations

<正>This paper considers how to use a group of robots to sense and control a diffusion process.The diffusion process is modeled by a partial differential equation (PDE),which is a both spatially and temporally variant system.The robots can serve as mobile sensors,actuators,or both.Centroidal Voronoi Tessellations based coverage control algorithm is proposed for the cooperative sensing task.For the diffusion control problem,this paper considers spraying control via a group of networked mobile robots equipped with chemical neutralizers,known as smart mobile sprayers or actuators,in a domain of interest having static mesh sensor network for concentration sensing.This paper also introduces the information sharing and consensus strategy when using centroidal Voronoi tessellations algorithm to control a diffusion process.The information is shared not only on where to spray but also on how much to spray among the mobile actuators.Benefits from using CVT and information consensus seeking for sensing and control of a diffusion process are demonstrated in simulation results.     

Accurate and robust body position trajectory tracking of six-legged walking robots with nonsingular terminal sliding mode control method

Many tasks, such as walking in narrow environments, detecting land mines, coordinating with manipulators, and avoiding obstacles, demand multi-legged walking robots to accurately and robustly track predefined body trajectories. Tracking body position trajectory must be accurate and robust in these situations, but research on this topic is rarely carried out. In this study, we propose a nonsingular terminal sliding mode (NTSM) control algorithm to implement accurate and robust body position trajectory tracking of six-legged walking robots. The NTSM control algorithm is constructed on the basis of the body position trajectory tracking model with a new NTSM reaching law. The performance of the NTSM control method is evaluated through several verifications. Results demonstrate that the proposed algorithm is effective for accurate and robust body position trajectory tracking. The findings of this study can provide insights into improving multi-legged walking robots’ walking and operation abilities in special environments and expanding the application fields of these robots.     

A data-driven switching control approach for braking systems with constraints

Braking control is of paramount importance in guaranteeing driving safety and comfort, but it is a well-known challenging task, due to the highly nonlinear and road condition-dependent behavior of the vehicle. Existing braking controllers typically rely on accurate models of the vehicle dynamics and the vehicle–road interaction, which are quite difficult to be retrieved in practice. In the wake of the data-driven control paradigm, we propose a model-free and fully data-based braking control method. The architecture of our scheme is two-layered, featuring: an inner switching controller, directly designed from data to match a given closed-loop behavior, and an outer predictive reference governor, exploited to enforce constraints and possibly improve the overall braking performance. The effectiveness of the approach is shown in a simulation environment, by providing a sensitivity analysis to the main tuning knobs of the method.     

Tracking control of high-performance robots via stabilizing controllers for uncertain systems

The development of flexible manufacturing systems calls for industrial robots characterized by robustness of performance with regard to the variations of the loads and real time specification of the trajectory in the work space. In this paper, the design of a feedback controller guaranteeing such performance is considered. At first, the manipulator dynamics are embedded into a larger class of uncertain dynamical systems and a class of feedback controls is proposed that guarantees uniform ultimate boundedness of the tracking error. Successively, the methodology is specialized for the case of robotic manipulators to track trajectories described in task-oriented coordinates; the proposed control algorithm operates without requiring any explicit coordinate transformation.     

The Lightweight Robot ElRob: Interesting Aspects in Modeling and Control

The articulated robot ElRob, consisting of flexible links and joints, is considered in several publications. Recent developments are presented in this work. The overall goal of the research is to decrease the effects of structural elasticities in lightweight robots. For this purpose model-based control concepts are investigated and very accurate and efficient kinematic and dynamic models are necessary. The robot is split into groups of bodies, the so called subsystems, with separated describing velocities and coordinate systems. To obtain structured equations of motion the Projection Equation is used. The beams are modelled using the floating frame of reference formulation and a Ritz-approach. Because of its flexibility, the examined robot is an underactuated system leading to special difficulties. As an example is it not possible to compute the desired joint angles with respect to a reference path in task space for the flexible system (inverse kinematic problem). Different methods to solve this drawback and other problems resulting from flexibility are discussed with special focus on feed forward control and different feedback control concepts. The resulting end point error, the necessary control input and other interesting results for the laboratory experiment are presented and compared. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Parametric study of nonlinear adaptive control algorithm with magneto-rheological suspension systems

This paper describes the details of the simulation analysis of a nonlinear model-based adaptive suspension control system [Song X, Ahmadian M, Southward SC, Miller LR. An adaptive semiactive control algorithm for magneto-rheological suspension systems. ASME J Vibr Acoust, in press; Song X. Design of adaptive vibration control systems with application of magneto-rheological dampers. Dissertation, Virginia Tech, December, 1999]. The numerical aspect of the simulation study of a seat suspension with application of magneto-rheological dampers will be presented. Magneto-rheological (MR) dampers have strong nonlinearities such as bi-linearity, hysteresis, and saturation related to magnetism, which can be represented by appropriate mathematic functions, respectively. Thus the model-based adaptive algorithm becomes complicated because of involvement of MR damper models. One objective of this study is to investigate the effect of MR damper model simplifications on the adaptive suspension performance. Furthermore, simulation is also applied to do parametric study of adaptive algorithm parameters such as filtering and step size. The numerical results compare the proposed adaptive controller with passive dampers to validate not only its effectiveness but also obtain some guidance information for its experimental implementation.     

A regularizing Kohn–Vogelius formulation for the model-free adsorption isotherm estimation problem in chromatography

Competitive adsorption isotherms must be estimated in order to simulate and optimize modern continuous modes of chromatography in situations where experimental trial-and-error approaches are too complex and expensive. The inverse method is a numeric approach for the fast estimation of adsorption isotherms directly from overloaded elution profiles. However, this identification process is usually ill-posed. Moreover, traditional model-based inverse methods are restricted by the need to choose an appropriate adsorption isotherm model prior to estimate, which might be very hard for complicated adsorption behavior. In this study, we develop a Kohn–Vogelius formulation for the model-free adsorption isotherm estimation problem. The solvability and convergence for the proposed inverse method are studied. In particular, using a problem-adapted adjoint, we obtain a convergence rate under substantially weaker and more realistic conditions than are required by the general theory. Based on the adjoint technique, a numerical algorithm for solving the proposed optimization problem is developed. Numerical tests for both synthetic and real-world problems are given to show the efficiency of the proposed regularization method.     

Enhancing the generality of fuzzy relational models for control

A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that ‘holes’ will be present in the identified relational model. These holes are real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to ‘fiil in’ the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.     

Dynamical Modeling and Flatness Based Control of a Belt Drive System

Belt driven systems are part of many industrial applications, like computerized numerical control (CNC) machines in particular cutting machines and 3D-printers. In this paper the dynamical modeling and a flatness based controller design for belt driven systems are proposed. Due to the special kinematics, the stiffness of the belt is nonlinear, leading to nonlinear equations of motion. By neglecting some minor dynamical effects, the resulting system simplifies to a differentially flat one. This allows to calculate nominal feed-forward control torques by using the flat output of the system. To stabilize the error dynamics, an additional PD control law is introduced. The proposed method is compared with a controller, where elastic deflections for the feed forward part are neglected and elastic deformations are compensated by modifying the desired trajectories in a model-based manner. The tracking performance of both methods is evaluated in certain simulations and experiments. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Model-based clustering for integrated circuit yield enhancement

This paper studies the defect data analysis method for semiconductor yield enhancement. Given the defect locations on a wafer, the local defects generated from the assignable causes are classified from the global defects generated from the random causes by model-based clustering, and the clustering methods can identify the characteristics of local defect clusters. The information obtained from this method can facilitate process control, particularly, root-cause analysis. The global defects are modeled by the spatial non-homogeneous Poisson process, and the local defects are modeled by the bivariate normal distribution or by the principal curve.     

基于条件分布函数的高维回归模型的变量选择方法

高维回归分析的变量选择问题是目前统计学研究的一个热点和难点问题.提出了一个基于条件分布函数的相关性度量准则,并在此基础上提出三种变量选择方法.与现有的方法相比,提出的方法不依赖于统计模型,可以适用于线性模型和非参数可加模型.数值模拟结果表明,即使协变量之间存在一定的相关性,方法也有较为满意的表现.