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Control of a Nonholonomic Mobile Robot Using Neural Networks”,
 IEEE Transactions on Neural Networks,
, 1998
"... AbstractA control structure that makes possible the integration of a kinematic controller and a neural network (NN) computedtorque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapuno ..."
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Cited by 62 (0 self)
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AbstractA control structure that makes possible the integration of a kinematic controller and a neural network (NN) computedtorque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do no require offline learning yet guarantee small tracking errors and bounded control signals are utilized.
Adaptive Output Feedback Control of Uncertain Systems using Single Hidden Layer Neural Networks
 IEEE Transactions on Neural Networks
, 2002
"... We consider adaptive output feedback control of uncertain nonlinear sy[q6M3 in which both the dy6Mq1g and the dimension of the regulated plant may be unknown.Only knowledge of relative degree is assumed. Given a smooth reference trajectory , the problem is to design a controller that forces the syM9 ..."
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Cited by 43 (14 self)
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We consider adaptive output feedback control of uncertain nonlinear sy[q6M3 in which both the dy6Mq1g and the dimension of the regulated plant may be unknown.Only knowledge of relative degree is assumed. Given a smooth reference trajectory , the problem is to design a controller that forces the syM9q measurement to track it with bounded errors. The classical approach necessitates buildinga state observer. However, findinga good observer for an uncertain nonlinear syM[9 is not an obvious task. We argue that it should be su#cient to build an observer for the output trackingerror. Ultimate boundedness of the error signals is shown through Ly apunov like stability analyqqg The method is illustrated in the design of a controller for a fourth order nonlinear syM99 of relative degree 2 and a highbandwidth attitude command symma for a model R50 helicopter. 1 Introdu88/ Research in adaptive output feedback control of uncertain nonlinear sy[q#6 is motivated by the many emerging applications that employ novel actuation devices for active control of flexible structures, fluid flows and combustion processes. These include such devices as piezo electric films, andsy thetic jets, which are ty pically nonlinearly coupled to the dy[M11q of the processes they are intended to control. Modelingfor these applications vary from havingaccurate low frequency models in the case of structural control problems, to havingno reasonable set of model equations in the case of active control of flows and combustion processes. Regardless of the extent of the model accuracy that may be present, an important aspect in any control design is the e#ect of parametric uncertainty and unmodeleddydg#q69 While it can be said the issue of parametric uncertainty is addressed within the context of adaptive cont...
Asymptotic tracking for systems with structured and unstructured uncertainties
 in Proc. IEEE Conf. Decision Control
, 2006
"... Abstract—The control of systems with uncertain nonlinear dynamics has been a decadeslong mainstream area of focus. The general trend for previous control strategies developed for uncertain nonlinear systems is that the more unstructured the system uncertainty, the more control effort (i.e., high ga ..."
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Cited by 32 (15 self)
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Abstract—The control of systems with uncertain nonlinear dynamics has been a decadeslong mainstream area of focus. The general trend for previous control strategies developed for uncertain nonlinear systems is that the more unstructured the system uncertainty, the more control effort (i.e., high gain or highfrequency feedback) is required to cope with the uncertainty, and the resulting stability and performance of the system is diminished (e.g., uniformly ultimately bounded stability). This brief illustrates how the amalgamation of an adaptive modelbased feedforward term (for linearly parameterized uncertainty) with a robust integral of the sign of the error (RISE) feedback term (for additive bounded disturbances) can be used to yield an asymptotic tracking result for Euler–Lagrange systems that have mixed unstructured and structured uncertainty. Experimental results are provided that illustrate a reduced rootmeansquared tracking error with reduced control effort. Index Terms—Adaptive control, friction, Lyapunov methods, nonlinearities, robustness.
Adaptive neural network control of nonlinear systems by stable output feedback
 IEEE Trans. Syst., Man, Cybern. B
, 1999
"... Abstract—This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN’s). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the o ..."
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Cited by 28 (6 self)
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Abstract—This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN’s). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a highgain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closedloop system is proven to be semiglobally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions. Index Terms — Adaptive control, highgain observer, neural networks, nonlinear system, output feedback control.
Limited Authority Adaptive Flight Control
, 2000
"... Contents Acknowledgements iii List of Figures vii Nomenclature xi Summary xiv 1 Introduction 1 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1.2 Design Integration Problems in Adaptive Control ................................................5 1.2.1 ..."
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Cited by 23 (11 self)
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Contents Acknowledgements iii List of Figures vii Nomenclature xi Summary xiv 1 Introduction 1 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1.2 Design Integration Problems in Adaptive Control ................................................5 1.2.1 Saturation ..................................................................... ..............................5 1.2.2 Linear Input Dynamics............................................................. ..................9 1.2.3 Quantized Control ..................................................................... ...............10 1.2.4 Adaptation While Not in Direct Control ..................................................10 1.2.5 Flight Certification of Adaptive Controllers.............................................11 1.3 Contributions of This Research ..................................................................... .....12 1.4 Brief Outline of Thesis ...............
Deadzone Compensation in Motion Control Systems Using Neural Networks
, 2000
"... A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NN's), one to estimate the unknown deadzone and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented fo ..."
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Cited by 22 (3 self)
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A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NN's), one to estimate the unknown deadzone and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented form containing extra neurons whose activation functions provide a "jump function basis set" for approximating piecewise continuous functions. Rigorous proofs of closedloop stability for the deadzone compensator are provided and yield tuning algorithms for the weights of the two NN's. The technique provides a general procedure for using NN's to determine the preinverse of an unknown rightinvertible function. I. INTRODUCTION A GENERAL class of industrial motion control systems has the structure of a dynamical system, usually of the Lagrangian form, preceded by some nonlinearities in the actuator, either deadzone, backlash, saturation, etc. [7]. This includespositioning tables [17], robot manip...
A comprehensive review for industrial applicability of artificial neural networks
 IEEE Transactions on Industrial Electronics
, 2003
"... Abstract—This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues ..."
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Cited by 21 (1 self)
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Abstract—This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems. Index Terms—Architecture, industrial control, neural network (NN) applications, training. I.
Adaptive friction compensation of servo mechanisms
 Int. J. of Systems Sciences
, 2001
"... Friction exists in all machines having relative motion, and plays an important role in many servo mechanisms and simple pneumatic or hydraulic systems. In order to achieve high precision motion control, accurate friction modeling and effective compensation techniques have to be investigated. In this ..."
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Cited by 18 (3 self)
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Friction exists in all machines having relative motion, and plays an important role in many servo mechanisms and simple pneumatic or hydraulic systems. In order to achieve high precision motion control, accurate friction modeling and effective compensation techniques have to be investigated. In this chapter, we shall present a systematic treatment of adaptive friction compensation techniques from both engineering and theoretical aspects. Firstly, a comprehensive list of the commonly used classical friction models and dynamic friction models is presented for comparison and controller design. Then, by considering the position and velocity tracking control of a servo mechanism with friction, adaptive friction compensation schemes are given based on the LIP static friction model and dynamic LuGre model respectively. Finally, extensive simulation comparison studies are presented to verify the effectiveness of the proposed methods. 1
Neural network adaptive robust control with application to precision motion control of linear motors
 International Journal of Adaptive Control and Signal Processing, 2000 (Accepted for the special issue on Developments in Intelligent Control for Industrial Applications
, 2001
"... In this paper, the recently proposed neural network adaptive robust control (NNARC) design are generalized to synthesize performance oriented control laws for a class of nonlinear systems transformable to the semistrict feedback forms through the incorporation of backstepping design techniques. Al ..."
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Cited by 15 (3 self)
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In this paper, the recently proposed neural network adaptive robust control (NNARC) design are generalized to synthesize performance oriented control laws for a class of nonlinear systems transformable to the semistrict feedback forms through the incorporation of backstepping design techniques. All unknown but repeatable nonlinearities in system are approximated by outputs of multilayer neural networks to achieve a better model compensation and an improved performance. Through the use of discontinuous projections with fictitious bounds, a controlled online training of all NN weights is achieved. Robust control terms can then be constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. 1
Neurocontroller using Dynamic State Feedback for Compensatory Control
, 1997
"... A common technique in neurocontrol is that of controlling a plant by static state feedback using the plant's inverse dynamics, which is approximated through a learning process. It is well known that in this control mode even small approximation errors or, which is the same, small perturbations ..."
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Cited by 13 (6 self)
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A common technique in neurocontrol is that of controlling a plant by static state feedback using the plant's inverse dynamics, which is approximated through a learning process. It is well known that in this control mode even small approximation errors or, which is the same, small perturbations of the plant may lead to instability. Here, a novel approach is proposed to overcome the problem of instability by using the inverse dynamics both for the Static and for the error compensating Dynamic State feedback control. This scheme is termed SDS Feedback Control. It is shown that as long as the error of the inverse dynamics model is "signproper" the SDS Feedback Control is stable, i.e., the error of tracking may be kept small. The proof is based on a modification of Liapunov's second method. The problem of online learning of the inverse dynamics when using the controller simultaneously for both forward control and for dynamic feedback is dealt with, as are questions related to noise sensitivity and robust control of robotic manipulators. Simulations of a simplified sensorimotor loop serve to illustrate the approach.