We propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in online mode. Lawrenceb aelectrical and computer engineering dept. Adaptive inverse control using som based multiple models geetha k. Adaptive inverse control for rotorcraft vibration reduction.
Research on hotrolling steel products quality control. An adaptive inverse identification process can be used to obtain a stable controller. An adaptive parameter updating is preferable to an offline adjustment because parameter varia. High performance speed control of direct current motors. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the. There are a number of different approaches to adaptive control. To guarantee convergence and for faster learning, adaptive learning rates and convergence theorems are developed. In this paper, we first discuss the adaptive elements used by the adaptive inverse control designer. Adaptive inverse control of linear and nonlinear systems. The plants dynamic equations are assumed to be known. Adaline neural networkbased adaptive inverse control for an.
An unknown plant will track an input command signal if the plant is driven by a controller whose transfer function approximates the inverse of the plant transfer function. Pleasant surprises a stability and misadjustment of the lms adaptive filter b comparative analyses of dither modeling schemes a, b, and c c a comparison of the selftuning regulator of astrom and wittenmark with the techniques of adaptive inverse control d adaptive inverse control for unstable linear siso plants e orthogonalizing. Klippel dresden, germany abstract a weak nonlinear plant can be linearized and will track an input signal if the plant is preceded by a nonlinear controller which approximates the inverse of the plants transfer function. The leading world practitioners of adaptive control have contributed to this handbook which is the most important work yet in this field. Pdf we propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in. Very often, a single system is utilized to achieve both of these control objectives. As an application of an optimization technique, a gradientprojection method is employed to derive an adaptive algorithm for updating the parameters of an inverse which is designed to cancel the effects of actuator uncertainties in a control system. Nonminimum phase adaptive inverse control for settle. With a manually controlled machine tool, the operator watches for changes in machining performance caused. A weak nonlinear plant can be linearized and will track an input signal if the plant is preceded by a nonlinear controller which approximates the. The purpose is to ascertain whether the specimen can survive and continue to function under severe shock conditions.
Many practical examples and applications are shown in the text. Adaptive control over the last five decades has emerged as one of the wellestablished discipline. Another feature of adaptive inverse control is that the same methods can be applied to adaptive control of nonlinear plants. In this paper, the offline training of the freemodel based neural networks is attempted in the model reference adaptive inverse control scheme for a more complex plant. Bernard widrow michel bilello stanford university department of electrical engineering, stanford, ca 943054055. A special adaptive filter is used to cancel the disturbances. Adaptive inverse control using kernel identification. Adaptive plant modeling or identification is an important function. Many problems in adaptive control can be divided into two parts.
The active boring bar is composed of a slender steel cutting tool, an electrodynamic shaker as controllable actuator and an iepe accelerometer as feedback sensor. Pdf adaptive inverse control based on linear and nonlinear. Review of adaptive inverse control a signal processing. Adaptive control is seen as a two part problem, control of plant dynamics and control of plant noise. A selfcontained introduction to adaptive inverse controlnow featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of adaptive inverse control takes a novel approach that is not available in any other book. Like feedback linearization, adaptive inverse control is based on the concept of dynamic inversion, but an inverse need not exist. To validate the performances of the proposed new control scheme, we provided a series of simulation results. There are a number of different formulations of aic, and we will look at one proposed by widrow and walach. Like feedback linearization, adaptive inverse control is based on the concept of dynamic inversion, but an. Other articles where adaptive control is discussed. Application of the freemodel based neural networks in. Experiments were conducted, and experimental results demonstrated realtime modeling capabilities of dnn and the performance of.
Adaptive inverse control using som based multiple models. The adaptive inverse control approach is applied to nonlinear plants. In this article, adaptive inverse control of chatter vibrations in internal turning process is addressed. In this paper, a unified framework for adaptive inverse power control is developed. An unknown plant will track an input command signal if the plant is driven by a controller whose transfer function approximates the inverse of the plant transfer. The main difficulty in shock control is that the specimen dynamics vary significantly and a control algorithm is required that adapts to the. This paper addresses the problem of modeling and controlling nonlinear plants. It is shown that the inverse control of nonlinear plants and disturbance canceling could be realized through nonlinear adaptive filters. Cdaic can be used for tracking of stable or stabilized, minimum or nonminimum phase linear plants.
Pdf methods for adaptive control of plant dynamics and for control of plant disturbance for unknown linear plants are described. Experiments were conducted, and experimental results demonstrated realtime modeling capabilities of dnn and the performance of the adaptive inverse controller. Adaptive inverse control adaptive inverse control widrow, bernard 19900801 00. Eugene walach adaptive filtering techniques have been used successfully in a variety of signal processing problems, including antenna systems, channel equalization, echo cancellation and spectral estimation.
Control of linear systems require linear adaptivefiltering methods and control of nonlinear systems require nonlinear adaptivefiltering methods. Adaptive inverse control based on linear and nonlinear. The study on the adaptive control of robot manipulators with dynamic parameter uncertainty has a long and rich history see, e. Depending on the particular control problem at hand, either or both of the two basic categories of adaptive element may be required, that is, linear adaptive elements filters or nonlinear adaptive elements. A unified framework for adaptive inverse power control. Adaptive inverse control for linear plants direct plant identification adaptive plant modeling or identification is an important function. Motter2 1 computational neuroengineering laboratory, university of florida, gainesville, fl 32611 2 nasa langley research center, hampton, virginia, 236812199 abstract. Adaline neural networkbased adaptive inverse control for. Now featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of adaptive inverse control takes a novel approach that is not available in any other book. In adaptive inverse control aic, parameters of the inverse are obtained using direct or direct methods.
First, the combined control strategy utilizes an adaptive inverse control as a feedforward controller for extending the acceleration frequency bandwidth of the electrohydraulic shaking table when the estimated plant model may be a nonminimum phase system and. Digital control systems 71 adaptive inverse control 7. A selfcontained introduction to adaptive inverse control now featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of adaptive inverse control takes a novel approach that is not available in any other book. An adaptive inverse control algorithm is proposed for shock testing an arbitrary specimen using an electrodynamic actuator. Written by two pioneers in the field, adaptive inverse control presents methods of adaptive signal processing that are. The design of the inverse model is often performed by using the neural network approach and this type of control has been applied. Adaptive inverse control using an online learning algorithm. Download adaptive inverse control, reissue edition. Adaptive control is no longer just an important theoretical field of study, but is also providing solutions to realworld problems. The leading world practitioners of adaptive control have contributed to this handbook which.
Adaptive inverse control based on kriging algorithm and. Direct adaptive inverse control daic alleviates the adhocism in adaptive loop. Aic combines the signal processing method with the. Adaptive filtering techniques have been successfully applied to adaptive antenna systems 120. In this study, an adaptive inverse control method based on kriging algorithm and lyapunov theory is proposed to improve control accuracy during adaptive driving. Stability and convergence analysis of direct adaptive inverse.
The use of adaptive inverse control in cascade statcom. The control algorithm used is the adaptive inverse control method, which approximates an inverse model of the loaded shaker with a finite impulse response adaptive filter, such that the reference input is reproduced at the shaker output. Combining the bp neural network, adaptive inverse control with internal model control theory, this paper builds the bp neural network inverse model with multiple input and single output based on internal model control. Request pdf adaptive inverse control of linear and nonlinear systems using dynamic neural networks in this paper, we see adaptive control as a threepart adaptivefiltering problem. Nonminimum phase adaptive inverse control for settle performance applications brian p.
Adaptive inverse control of chatter vibrations in internal. Abstractkernel methods are exploited to implement an adaptive inverse control scheme of which a first introductory presentation is given. The system inverse model identifier is constructed by neural network. Adaptive online inverse control of a shape memory alloy wire. Adaptive online inverse control of a shape memory alloy. Improvements in cnc machine tools depend on the refinement of adaptive control, which is the automatic monitoring and adjustment of machining conditions in response to variations in operation performance. Adaptive inverse control of weakly nonlinear systems wolfgang j. An adaptive inverse identification process can be used to obtain a stable controller, even if the plant is nonminimum phase. Adaptive inverse control using an online learning algorithm for.
Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electrohydraulic servo system. Combined control strategy using internal model control and. Therefore, it realizes the inverse mapping between the output and the input variables of the bp neural network. Modeling and adaptive inverse control of hysteresis and. Simulations show that the proposed inverse controller has better performance in system damping and transient improvement. Adaptive inverse control of excitation system with. Control of plant disturbance can be achieved by an adaptive feedback process that minimizes plant output disturbance without altering plant dynamics 4. Next, the adaptive inverse control concept is presented.
Adaptive inverse control aic is a relatively new approach first introduced by. Adaptive control is seen as a two part problem control of plant dynamics and control of plant noise. A plant can track an input command signal if it is driven by a controller whose transfer func tion approximates the inverse of its transfer func tion. The actuator uncertainty is parametrized by a set of unknown parameters which belong to a parameter region. The siso control system actuates in the direction normal to the cut surface. Adaptive inverse control using a gradientprojection. He, xiuyu and zhao, zhijia and su, jinya and yang, qinmin and zhu, dachang 2019 adaptive inverse control of a vibrating coupled vesselriser system with input backlash. Pdf adaptive inverse control using an online learning algorithm. Research on hotrolling steel products quality control based. Stability and convergence analysis of direct adaptive. Section3 discusses the design of an adaptive inverse controller, including methods for solving the inverse of the creep model, the use of the lms least mean square algorithm and the structure of the adaptive inverse control. First, the combined control strategy utilizes an adaptive inverse control as a feedforward controller for extending the acceleration frequency bandwidth of the electrohydraulic shaking table when the estimated plant model may be a nonminimum phase system and its inverse model is an unstable system. Written by two pioneers in the field, adaptive inverse control presents methods of adaptive signal processing that. Adaptive inverse control intelligent control, 1993.
An adaptive inverse strategy of control for ipmc actuators was set up on the basis of a superposition model of nonlinear hysteresis and linear creep, and we obtained good simulation and experiment results. In the domain of automatic control, adaptive inverse control is based on inverse processes identification where the output of the process becomes the input of the model figure 1 widrow and bilello 1993. The electromechanical system of a crawler is a multiinput, multioutput strongly coupled nonlinear system. Adaptive techniques will transform the world of control. Disturbance statistics are also assumed to be known and often assumed gaussian and white. After finding adequate filter structures the interest of research is now focused on the optimal adjustment of the free control ler parameters to the particular plant. It is based on a modified filteredx least mean square mfxlms algorithm that is proposed and analyzed. A practical version of the algorithm for closed loop power control is also developed. Adaptive inverse control is not only simple, but it affords new control capabilities that can often be superior to those of conventional systems. Review of adaptive inverse control a signal processing approach by bernard widrow, eugene walach editors ivan popchev address. Inverse plant modeling adaptive inverse control wiley. Institute of information technology, bulgarian academy of sciences, bl. This paper proposes an alternative approach that uses adaptive filtering to achieve feedforward control for both lin ear and nonlinear. Modeling and adaptive inverse control of hysteresis and creep.
After finding adequate filter structures the interest of research is now focused on the optimal adjustment of the free controller parameters to the particular plant. Adaptive inverse control using an online learning algorithm for neural networks 403 then, we propose a new nonlinear adaptive control model which employs, as part of the system identi. Closed loop direct adaptive inverse control for linear plants. A selfcontained introduction to adaptive inverse control. Adaptive inverse control, adaptive filters, direct current motor, speed control. In this paper, we propose a closed loop direct adaptive inverse control technique based on normalized least mean square nlms for controlling linear plants. Adaptive inverse control of a vibrating coupled vessel. The adaptive inverse control concept some problems with conventional control methods.
The adaptive controller is implemented using adaptive fil ters. In adaptive inverse control aic, adaptive inverse of the plant is used as a feedforward controller. Adaptive system identification is represented as shown. Usa inear adaptive inverse control stanford university. Majority of aic schemes estimate controller parameters using the indirect method. Adaptive inverse control based on forward model identification. In this paper, we discuss the stability and convergence of daic algorithm. Adaptive inverse control, reissue edition by bernard widrow. Section 4 mainly gives simulation and experimental results. The basis for adaptive inverse control rests firmly 011 the foundational theory of adaptive signal processing. The authors develop the adaptive inverse control approach via nonlinear devices in siso and.
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