An adaptive inverse identification process can be used to obtain a stable controller, even if the plant is nonminimum phase. The siso control system actuates in the direction normal to the cut surface. Other articles where adaptive control is discussed. 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. Pdf methods for adaptive control of plant dynamics and for control of plant disturbance for unknown linear plants are described. Nonminimum phase adaptive inverse control for settle performance applications brian p. Adaptive plant modeling or identification is an important function. 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 adaptive inverse control aic, adaptive inverse of the plant is used as a feedforward controller. Adaptive control is seen as a two part problem, control of plant dynamics and control of plant noise.
Majority of aic schemes estimate controller parameters using the indirect method. Application of the freemodel based neural networks in. The plants dynamic equations are assumed to be known. The system inverse model identifier is constructed by neural network. Control of linear systems require linear adaptivefiltering methods and control of nonlinear systems require nonlinear adaptivefiltering methods. Adaptive inverse control based on kriging algorithm and. Adaline neural networkbased adaptive inverse control for. The adaptive controller is implemented using adaptive fil ters. Institute of information technology, bulgarian academy of sciences, bl. 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.
In this paper, we discuss the stability and convergence of daic algorithm. Motter2 1 computational neuroengineering laboratory, university of florida, gainesville, fl 32611 2 nasa langley research center, hampton, virginia, 236812199 abstract. Adaptive inverse control for rotorcraft vibration reduction. 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. This paper proposes an alternative approach that uses adaptive filtering to achieve feedforward control for both lin ear and nonlinear. The use of adaptive inverse control in cascade statcom. Download adaptive inverse control, reissue edition. 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 based on forward model identification. 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. Usa inear adaptive inverse control stanford university. Written by two pioneers in the field, adaptive inverse control presents methods of adaptive signal processing that.
Adaptive inverse control, reissue edition by bernard widrow. Adaptive inverse control using an online learning algorithm. High performance speed control of direct current motors. 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. Written by two pioneers in the field, adaptive inverse control presents methods of adaptive signal processing that are. Adaptive online inverse control of a shape memory alloy. Pdf adaptive inverse control using an online learning algorithm. Adaptive inverse control, adaptive filters, direct current motor, speed control. Research on hotrolling steel products quality control based. An adaptive inverse control algorithm is proposed for shock testing an arbitrary specimen using an electrodynamic actuator. Disturbance statistics are also assumed to be known and often assumed gaussian and white.
Pdf adaptive inverse control based on linear and nonlinear. Adaptive inverse control of weakly nonlinear systems wolfgang j. Stability and convergence analysis of direct adaptive. The study on the adaptive control of robot manipulators with dynamic parameter uncertainty has a long and rich history see, e. 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. To guarantee convergence and for faster learning, adaptive learning rates and convergence theorems are developed. Lawrenceb aelectrical and computer engineering dept. It is based on a modified filteredx least mean square mfxlms algorithm that is proposed and analyzed. Adaptive inverse control using an online learning algorithm for. Adaptive filtering techniques have been successfully applied to adaptive antenna systems 120. Control of plant disturbance can be achieved by an adaptive feedback process that minimizes plant output disturbance without altering plant dynamics 4. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the.
Adaptive inverse control based on linear and nonlinear. 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. 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. 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. In this study, an adaptive inverse control method based on kriging algorithm and lyapunov theory is proposed to improve control accuracy during adaptive driving. 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. Adaptive control over the last five decades has emerged as one of the wellestablished discipline. There are a number of different formulations of aic, and we will look at one proposed by widrow and walach.
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. In this paper, we first discuss the adaptive elements used by the adaptive inverse control designer. The leading world practitioners of adaptive control have contributed to this handbook which is the most important work yet in this field. Review of adaptive inverse control a signal processing. Adaptive inverse control using som based multiple models. Adaptive inverse control using kernel identification. Experiments were conducted, and experimental results demonstrated realtime modeling capabilities of dnn and the performance of the adaptive inverse controller.
Adaptive control is seen as a two part problem control of plant dynamics and control of plant noise. Nonminimum phase adaptive inverse control for settle. In adaptive inverse control aic, parameters of the inverse are obtained using direct or direct methods. An adaptive parameter updating is preferable to an offline adjustment because parameter varia. A unified framework for adaptive inverse power control. Adaline neural networkbased adaptive inverse control for an. Direct adaptive inverse control daic alleviates the adhocism in adaptive loop. Adaptive inverse control of a vibrating coupled vessel. Many problems in adaptive control can be divided into two parts. Adaptive online inverse control of a shape memory alloy wire. It is shown that the inverse control of nonlinear plants and disturbance canceling could be realized through nonlinear adaptive filters. We propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in online mode. The main difficulty in shock control is that the specimen dynamics vary significantly and a control algorithm is required that adapts to the. Modeling and adaptive inverse control of hysteresis and.
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. Inverse plant modeling adaptive inverse control wiley. 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. Another feature of adaptive inverse control is that the same methods can be applied to adaptive control of nonlinear plants. Adaptive inverse control of chatter vibrations in internal.
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. Next, the adaptive inverse control concept is presented. In this paper, a unified framework for adaptive inverse power control is developed. Adaptive inverse control using som based multiple models geetha k. Based on adaptive inverse control theory, combined with neural network, neural network adaptive inverse controller is developed and applied to an electrohydraulic servo system. To validate the performances of the proposed new control scheme, we provided a series of simulation results. Experiments were conducted, and experimental results demonstrated realtime modeling capabilities of dnn and the performance of. 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. Like feedback linearization, adaptive inverse control is based on the concept of dynamic inversion, but an inverse need not exist. Adaptive techniques will transform the world of control. The adaptive inverse control concept some problems with conventional control methods. Research on hotrolling steel products quality control.
Adaptive inverse control of excitation system with. An adaptive inverse identification process can be used to obtain a stable controller. The leading world practitioners of adaptive control have contributed to this handbook which. In this article, adaptive inverse control of chatter vibrations in internal turning process is addressed. The purpose is to ascertain whether the specimen can survive and continue to function under severe shock conditions. Adaptive inverse control aic is a relatively new approach first introduced by. 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. Abstractkernel methods are exploited to implement an adaptive inverse control scheme of which a first introductory presentation is given.
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. The adaptive inverse control approach is applied to nonlinear plants. 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. Adaptive inverse control adaptive inverse control widrow, bernard 19900801 00. Simulations show that the proposed inverse controller has better performance in system damping and transient improvement. This paper addresses the problem of modeling and controlling nonlinear plants.
Like feedback linearization, adaptive inverse control is based on the concept of dynamic inversion, but an. Combined control strategy using internal model control and. The actuator uncertainty is parametrized by a set of unknown parameters which belong to a parameter region. Aic combines the signal processing method with the. 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. The basis for adaptive inverse control rests firmly 011 the foundational theory of adaptive signal processing. Cdaic can be used for tracking of stable or stabilized, minimum or nonminimum phase linear plants. In this paper, we propose a closed loop direct adaptive inverse control technique based on normalized least mean square nlms for controlling linear plants. The design of the inverse model is often performed by using the neural network approach and this type of control has been applied. A selfcontained introduction to adaptive inverse control.
Adaptive inverse control of linear and nonlinear systems. 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. Review of adaptive inverse control a signal processing approach by bernard widrow, eugene walach editors ivan popchev address. Therefore, it realizes the inverse mapping between the output and the input variables of the bp neural network. 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. A practical version of the algorithm for closed loop power control is also developed. There are a number of different approaches to adaptive control. Adaptive control is no longer just an important theoretical field of study, but is also providing solutions to realworld problems. Adaptive inverse control using a gradientprojection. 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. 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. 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. Modeling and adaptive inverse control of hysteresis and creep. Bernard widrow michel bilello stanford university department of electrical engineering, stanford, ca 943054055.
Adaptive inverse control for linear plants direct plant identification adaptive plant modeling or identification is an important function. With a manually controlled machine tool, the operator watches for changes in machining performance caused. Pdf we propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in. The electromechanical system of a crawler is a multiinput, multioutput strongly coupled nonlinear system. Digital control systems 71 adaptive inverse control 7. The authors develop the adaptive inverse control approach via nonlinear devices in siso and. Stability and convergence analysis of direct adaptive inverse. A special adaptive filter is used to cancel the disturbances. Section 4 mainly gives simulation and experimental results. 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. Adaptive inverse control is not only simple, but it affords new control capabilities that can often be superior to those of conventional systems.
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