为提高轮廓加工精度采用DOB和ZPETC的直线伺服鲁棒跟踪控制

为了提高轮廓加工精度,本文针对高精度直线伺服系统,提出了一种将零相位误差跟踪控制器(ZPETC)和干扰观测器(DOB)相结合的鲁棒跟踪控制策略.ZPETC作为前馈跟踪控制器,保证了快速性,使系统实现准确跟踪;基于DOB的鲁棒反馈控制器补偿了外部扰动、未建模动态、系统参数变化和机械非线性等,保证了系统的强鲁棒性能.仿真结果表明了所提出的控制方案是有效的,既能实现完好跟踪,又有较强的鲁棒性能.从而有效地减小了轮廓误差,提高了轮廓加工精度.     

Pixel‐level snakes on the CNNUM: algorithm design,on‐chip implementation and applications

In this paper, a new algorithm for the cellular active contour technique called pixel‐level snakes is proposed. The motivation is twofold: on the one hand, a higher efficiency and flexibility in the contour evolution towards the boundaries of interest are pursued. On the other hand, a higher performance and suitability for its hardware implementation onto a cellular neural network (CNN) chip‐set architecture are also required. Based on the analysis of previous schemes the contour evolution is improved and a new approach to manage the topological transformations is incorporated. Furthermore, new capabilities in the contour guiding are introduced by the incorporation of inflating/deflating terms based on the balloon forces for the parametric active contours. The entire algorithm has been implemented on a CNN universal machine (CNNUM) chip set architecture for which the results of the time performance measurements are also given. To illustrate the validity and efficiency of the new scheme several examples are discussed including real applications from medical imaging. Copyright © 2005 John Wiley & Sons, Ltd.     

High‐Speed multiview face localization and tracking with a minimum bounding box using genetic algorithm

In this paper, we propose a high‐speed multiview face localization and tracking method with a minimum bounding box using template matching with genetic algorithm (GA). In this method, a head is treated as a cylinder, and the multiview face can be represented using the development of lateral surface (two‐dimensional (2D) model). The face can be localized by a template, which is generated from the model and corresponds to the target face direction. Processing is very fast because the parameters for both template generation and affine transformation are simultaneously optimized by GA. In the experiment, challenging 60‐video sequences are created in a situation where subjects drastically move their faces in a room, using a standard computer and web camera. Then, the proposed method is applied to the sequences, and the performance is investigated. As a result, the proposed method achieves fast and accurate multiview face localization and tracking. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust contour tracking of nonholonomic mobile robots via adaptive velocity field motion planning scheme

This paper aims at designing a contour tracking scheme based on an adaptive velocity field formulation, for the case of uncertain nonholonomic (differential drive) mobile robots, with its dynamic controller. First, to handle kinematic uncertainties that deviate the map of the velocity field into wheel velocities, a linear parameterization of the uncertain Jacobian operator is proposed to synthesize an adaptive kinematic controller that shapes correctly the velocity field. Then, a robust model‐free dynamic controller is proposed to compensate in finite time for uncertain dynamics and disturbances, enforcing the kinematic reference. Finally, a representative simulation study is discussed to show the reliability of the proposed scheme.     

基于ZPETC和DOB的永磁直线同步电机的鲁棒跟踪控制

针对高精度永磁直线同步电机直接驱动伺服系统，提出了一种将零相位误差跟踪控制器(ZPETC)和干扰观测器(DOB)相结合的鲁棒跟踪控制策略，以提高系统的跟踪性能和鲁棒性能。ZPETC作为前馈跟踪控制器，保证了快速性，使系统实现准确跟踪；基于DOB的鲁棒反馈控制器补偿了外部扰动、未建模动态、系统参数变化和机械非线性等，保证了系统的强鲁棒性。仿真结果表明，所提出的控制方案在保证系统实现完好跟踪的同时，又具有较强的鲁棒性，从而改善了数控机床进给系统的定位精度，进而提高了轮廓加工精度。     

Automatic detection and tracking of moving image target with CNN‐UM via target probability fusion of multiple features

A high speed target detection and tracking algorithm for a CNN‐UM chip is presented in this paper. The target confidence value is computed based on the fusion of target existence probabilities of features using products of weighted sums. The target decision is done with such a confidence value and target initiation is done through the temporal accumulation of the confidence. The probability of the target existence for each feature is created in the region of influence depending on the reliability and the strength of the feature. By virtue of the analogic parallel processing structure of the CNN‐UM (Roska T, Chua LO. The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Systems II 1993; CAS‐40 : 163–173), real time tracking can be achieved with presently available technologies with the speed of several kilo‐frames per second. Due to the utilization of multiple features of target, robust target detection is possible via the proposed algorithm. On‐chip experiments of the proposed target‐tracking algorithm have been done and properties of the proposed approach are disclosed through the various experiments. Copyright © 2003 John Wiley & Sons, Ltd.     

Iterative learning control for robotic manipulators: A bounded‐error algorithm

This paper presents a model‐based nonlinear iterative learning control (NILC) for nonlinear multiple‐input and multiple‐output mechanical systems of robotic manipulators. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory‐tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. Both standard and bounded‐error learning control laws with feedback controllers attached are considered. The NILC synthesis is based on a dynamic model of a six degrees of freedom robotic manipulator. The dynamic model includes viscous and Coulomb friction and input generalized torques are bounded. With respect to the bounded‐error and standard learning processes applied to a virtual PUMA 560 robot (Unimation, Inc. Danburry, CT, USA), simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive finite‐time actuator fault‐tolerant coordinated attitude control of multispacecraft with input saturation

In this study, for nonrigid spacecraft formation, a distributed adaptive finite‐time actuator fault‐tolerant (FTAFT) coordinated attitude tracking control (CATC) issue is addressed. Aiming at stabilizing the spacecraft formation flying system during a limited time, two distributed adaptive FTAFT CATC strategies are presented. Initially, on basis of distributed finite‐time observer (DFTO), adaptive control, consensus approach, graph theory, and finite‐time theory, we develop a distributed adaptive FTAFT coordinated attitude tracking controller to repress the impact of the external state‐dependent and state‐independent disturbance, unknown time‐varying inertia uncertainty, and actuator fading or fault. Then, combining with the proposed controller, a distributed adaptive FTAFT control law with input saturation subjected to physical limitations of actuator is further designed. In addition, a self‐adjusting matrix (SAM) is proposed to improve the actuators' performance. With the two proposed CATC strategies, the followers can synchronize with the leader. Simulations demonstrated the validity of the designed control laws.     

无权重系数整定的双轴进给驱动系统预测轮廓控制

为解决双轴进给驱动系统预测轮廓控制中,权重系数整定依赖人工经验和耗时较长的问题,本文提出一种双轴进给驱动系统有限控制集模型预测控制策略。首先将驱动电机的运动方程、电气方程和逆变器的数学模型相结合,建立统一模型;然后预测带有电流和转速信息的参考电压矢量;之后根据轮廓误差与跟踪误差之比,分情况来确定改进的价值函数;并进行备选电压矢量的选择和价值函数的寻优。最后,通过仿真研究证明所提出的控制策略能够满足所需的动态响应速度和轮廓跟踪精度,且无需权重系数整定。     

Achievement method of high‐speed response of error prediction tracking control for optical disk system

This paper proposes the new design method of error‐prediction control systems combining the ZPET control and the robust feedback control. The error‐prediction control system based on ZPET control can reduce the tracking error caused by periodic disturbances more efficiently than the conventional repetitive control technique. However, the proposed tracking servo system does not reduce the residual tracking error caused by the low‐pass filter of the feedforward compensation and by the long sampling time. In order to overcome this problem, this paper proposes the new structure of the feedforward tracking control system. The modified ZPET control tracking servo system for optical disk recording system does not include the low‐pass filter and its sampling time becomes faster. On the other hand, the sudden disturbance observer reduces the influence of nonperiodic disturbances. The experimental results point out that the proposed tracking servo system has a precise tracking response against both the periodic disturbances and the nonperiodic disturbances. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(4): 51–59, 2010; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20800     

Composite learning fuzzy synchronization for incommensurate fractional‐order chaotic systems with time‐varying delays

This paper presents a composite learning fuzzy control to synchronize two different uncertain incommensurate fractional‐order time‐varying delayed chaotic systems with unknown external disturbances and mismatched parametric uncertainties via the Takagi‐Sugeno fuzzy method. An adaptive controller together with fractional‐order composite learning laws is designed based on both a parallel distributed compensation technology and a fractional Lyapunov criterion. The boundedness of all variables in the closed‐loop system and the Mittag‐Leffler stability of tracking error can be guaranteed. T‐S fuzzy systems are provided to tackle unknown nonlinear functions. The distinctive features of the proposed approach consist in the following: (1) a supervisory control law is designed to compensate the lumped disturbances; (2) both the prediction error and the tracking error are used to estimate the unknown fuzzy system parameters; (3) parameter convergence can be ensured by an interval excitation condition. Finally, the feasibility of the proposed control strategy is demonstrated throughout an illustrative example.     

High‐speed and high‐precision position control using a sliding mode compensator

To achieve high‐speed, high‐precision position control for semiconductor product machines and industrial robots, full‐closed feedback control is applied. Many control methods have been proposed for such a system. In general, proportional position control and proportional plus integral velocity control or integral plus proportional velocity control (P,PI/I‐P), which is a type of proportional plus integral plus differential control (PID), is applied in many industrial applications. However, in the case of changing mechanical characteristics of the control target, the parameters of P,PI/I‐PI control must also change in order to maintain good motion performance. In this paper, we propose a new P,PI/I‐P control method that includes a nonlinear compensator. The algorithm of the nonlinear compensator is based on sliding mode control with chattering compensation. The effectiveness of the proposed control method is evaluated using a full‐closed single‐axis slider system via point‐to‐point control and contour control in the case of changing load. The experimental results indicate that the proposed control method is robust in the case of changing acceleration/deceleration of control reference, changing load, and low‐velocity contour motion. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 174(2): 65–71, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21011     

A decentralized model following controllers for a class of large‐scale interconnected dynamical systems with uncertainties

The problem of decentralized robust tracking and model following is considered for a class of large‐scale interconnected systems with uncertainties. A class of linear decentralized state feedback controllers are proposed for robust tracking of dynamical signals in such a class of uncertain large‐scale systems. The proposed decentralized tracking controllers can guarantee that the tracking errors between each subsystem and local reference model are uniformly ultimately bounded. Moreover, we modify the linear controllers by introducing some nonlinear parts so that the tracking errors decrease asymptotically to zero in the presence of uncertain parameters and interconnection terms. Finally, an illustrative example is given to demonstrate the validity of our results. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 142(2): 48–58, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10101     

Neural network adaptive robust control with application to precision motion control of linear motors

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance‐oriented control laws for a class of single‐input–single‐output (SISO) nth‐order non‐ linear systems. Both repeatable (or state dependent) unknown non‐linearities and non‐repeatable unknown non‐linearities such as external disturbances are considered. In addition, unknown non‐linearities can exist in the control input channel as well. All unknown but repeatable non‐linear functions are approximated by outputs of multi‐layer neural networks to achieve a better model compensation for an improved performance. All NN weights are tuned on‐line with no prior training needed. In order to avoid the possible divergence of the on‐line tuning of neural network, discontinuous projection method with fictitious bounds is used in the NN weight adjusting laws to make sure that all NN weights are tuned within a prescribed range. By doing so, even in the presence of approximation error and non‐repeatable non‐linearities such as disturbances, a controlled learning is achieved and the possible destabilizing effect of on‐line tuning of NN weights is avoided. Certain robust control terms are constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy in general. In addition, if the unknown repeatable model uncertainties are in the functional range of the neural networks and the ideal weights fall within the prescribed range, asymptotic output tracking is also achieved to retain the perfect learning capability of neural networks in the ideal situation. The proposed neural network adaptive control (NNARC) strategy is then applied to the precision motion control of a linear motor drive system to help to realize the high‐performance potential of such a drive technology. NN is employed to compensate for the effects of the lumped unknown non‐linearities due to the position dependent friction and electro‐magnetic ripple forces. Comparative experiments verify the high‐performance nature of the proposed NNARC. With an encoder resolution of 1 µm, for a low‐speed back‐and‐forth movement, the position tracking error is kept within ±2 µm during the most execution time while the maximum tracking error during the entire run is kept within ±5.6 µm. Copyright © 2001 John Wiley & Sons, Ltd.     

Quasi‐ARX neural network based adaptive predictive control for nonlinear systems

In this paper, a new switching mechanism is proposed based on the state of dynamic tracking error so that more information will be provided –not only the error but also a one up to pth differential error will be available as the switching variable. The switching index is based on the Lyapunov stability theory. Thus the switching mechanism can work more effectively and efficiently. A simplified quasi‐ARX neural‐network (QARXNN) model presented by a state‐dependent parameter estimation (SDPE) is used to derive the controller formulation to deal with its computational complexity. The switching works inside the model by utilizing the linear and nonlinear parts of an SDPE. First, a QARXNN is used as an estimator to estimate an SDPE. Second, by using SDPE, the state of dynamic tracking error is calculated to derive the switching index. Additionally, the switching formula can use an SDPE as the switching variable more easily. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance‐rejection performances. Experimental results demonstrate its effectiveness. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Fully distributed tracking control for non‐identical multi‐agent systems with matching uncertainty

This article addresses the fully distributed consensus tracking control problem of linear multi‐agent systems with parameter uncertainties. First, a new class of distributed protocol, based on the relative states of neighbors, is proposed. Theoretical analysis indicates that the considered problem can be solved if the control gain constant of the protocol is larger than the norm bound of the leader's nonlinear inputs. Furthermore, a distributed adaptive control protocol is proposed for the case without available global information. The distributed consensus tracking control problem of uncertain linear multi‐agent systems is solved based only on local information under the proposed adaptive protocol. Finally, an application in low‐Earth‐orbit satellite formation flying is provided to illustrate the theoretical results. Copyright © 2014 John Wiley & Sons, Ltd.     

Output‐feedback H∞ quadratic tracking control of linear systems using reinforcement learning

This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to design an output‐feedback (OPFB) H_∞ tracking controller for partially unknown linear continuous‐time systems. Although reinforcement learning techniques have been successfully applied to find optimal state‐feedback controllers, in most control applications, it is not practical to measure the full system states. Therefore, it is desired to design OPFB controllers. To this end, a general bounded L₂ ‐gain tracking problem with a discounted performance function is used for the OPFB H_∞ tracking. A tracking game algebraic Riccati equation is then developed that gives a Nash equilibrium solution to the associated min‐max optimization problem. An IRL algorithm is then developed to solve the game algebraic Riccati equation online without requiring complete knowledge of the system dynamics. The proposed IRL‐based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an OPFB policy and updates the OPFB gain using the information given by the evaluated policy. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. A simulation example is provided to verify the convergence of the proposed algorithm to a suboptimal OPFB solution and the performance of the proposed method.     

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab‐scale PEA system. Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root‐mean‐square and maximum tracking errors for different frequencies.     

A modified MPPT control loop for PV/battery‐charging system using PI controller optimally tuned with GA

In this study, a stand‐alone photovoltaic (PV)/battery‐charging system is proposed to efficiently charge a lead–acid battery with the available maximum power from the PV array. The relative sizing of the battery characteristics with respect to the PV array characteristics is indicated. The maximum‐power‐point (MPP)‐tracking operation is achieved through developing a new control loop, which is based on using the incremental conductance algorithm and the PI controller. The parameters of the PI controller are optimally tuned using the genetic algorithm (GA) by minimizing the integral of squared error and settling time. The proposed system was simulated twice by using MATLAB‐SIMULINK and by writing the appropriate MATLAB code in an M‐file. The SIMULINK model was designed to investigate the performance of the proposed system, whereas the M‐file model was designed to be used with the GA tool under MATLAB software, to optimally tune the PI controller. The simulation results indicated a rapid tracking capability for the PV array MPPs and a good matching between the PV array and the battery under various charging conditions. Copyright © 2010 John Wiley & Sons, Ltd.     

Process fault prognosis using a fuzzy‐adaptive unscented Kalman predictor

By monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy‐adaptive unscented Kalman filter (FAUKF)‐based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi–Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor. Copyright © 2011 John Wiley & Sons, Ltd.