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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...
Backlash Compensation In Nonlinear Systems Using Dynamic Inversion By Neural Networks
, 1999
"... A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of ..."
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Cited by 7 (4 self)
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A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator which yields a stable closedloop system. 1 INTRODUCTION A general class of industrial motion control systems has the structure of a nonlinear dynamical system preceded by some nonlinearities in the actuator, either deadzone, backlash, saturation, etc. This includes xypositioning tables [19], robot manipulators [14], overhead crane mechanisms, and more. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the nonanalytic nature of the actuator nonlinearities and the fact that their exact nonlinear functions are unknown, such systems...
Backlash Compensation with Filtered Prediction in Discrete Time Nonlinear Systems by Dynamic Inversion Using Neural Networks
 Journal of Control
, 1999
"... A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. This paper extends the dynamic inversion technique to discretetime systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash no ..."
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Cited by 4 (1 self)
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A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. This paper extends the dynamic inversion technique to discretetime systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discretetime tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discretetime adaptive control techniques, no certainty equivalence (CE) or linearinthe parameters (LIP) assumptions are needed. 1 Introduction Many physical components of control systems have nonsmooth nonlinea...
Backlash Compensation in Discrete Time Nonlinear Systems Using Dynamic Inversion by Neural Networks
, 1999
"... A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure f ..."
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Cited by 2 (1 self)
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A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discretetime tuning algorithm is given for the NN weights so that the backlash compensation scheme becomes adaptive, guaranteeing bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discretetime adaptive control techniques, no certainty equivalence (CE) assumption is needed. 1 Introduction Robotic systems often have nonlinearities in the actuator such as deadzone, backlash, saturation, etc. This includes xypositioning tables, robot manipulators, ove...
Neural Net Backlash Compensation With Hebbian Tuning By Dynamic Inversion
"... Neural network compensation scheme is presented for the class of nonlinear systems with backlash nonlinearity. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. Instead of a derivative, which cannot be implement ..."
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Cited by 2 (0 self)
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Neural network compensation scheme is presented for the class of nonlinear systems with backlash nonlinearity. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. Instead of a derivative, which cannot be implemented, a filtered derivative is used. Full rigorous stability proofs are given using filtered derivative. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A modified Hebbian algorithm is presented for NN tuning which yields a stable closedloop system. Using this method yields a relatively simple adaptation structure and offers computational advantages over gradient descent based algorithms. 1 Introduction Recently, in seminal work several rigorously derived ad...
TwoLayer Fuzzy Logic Based LoadFrequency Controller for a TwoArea Interconnected Power System Considering Nonlinearities with Super Capacitor Energy Storage Units
"... Abstract: This paper proposes a sophisticated application of Super Capacitor Energy Storage (SCES) unit for the improvement of the LoadFrequency Control in a twoarea interconnected power system considering Governor Dead Band (GDB) and Generation Rate Constraints (GRC) nonlinearities. In this pape ..."
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Cited by 1 (0 self)
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Abstract: This paper proposes a sophisticated application of Super Capacitor Energy Storage (SCES) unit for the improvement of the LoadFrequency Control in a twoarea interconnected power system considering Governor Dead Band (GDB) and Generation Rate Constraints (GRC) nonlinearities. In this paper, the proposed fuzzy controller, called as two layered fuzzy controller, consists of two layers. The first layer is called compensator, which is used to generate and update the reference value of Area Control Error (ACE). The second layer called feedback fuzzy logic controller, makes ACE decay to zero at steady state. The proposed two layered fuzzy controller, with the updated reference value of Area Control Error using precompensator ensures the ACE to zero with the inclusion of Proportional plus Integral (PI) controllers. The Integral Square Error (ISE) criterion is adopted in optimizing the PI controller gains. In addition to levelling load, the SCES is advantageous for secondary control in the power system and maintains the power quality. When an AC power system is subjected to load disturbances, considerable frequency oscillations may result to system instability. So as to ensure the system stability, the power modulation control offered by SCES is enhanced to suppress the peak value of the transient frequency deviation. Simulation results show that the proposed two layered fuzzy logic controller is not only effective in damping out the frequency oscillations, but also capable of
Dual Mode TwoLayer Fuzzy Logic based Load Frequency Controller for a TwoArea Interconnected Power System with Super Capacitor Energy Storage Units
"... The Load Frequency Control (LFC) is of great importance in power system operation and control for providing sufficient and reliable electric power with good quality. Even though the simple Proportional Integral (PI) controllers are still popular in power industry for frequency regulation it does no ..."
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The Load Frequency Control (LFC) is of great importance in power system operation and control for providing sufficient and reliable electric power with good quality. Even though the simple Proportional Integral (PI) controllers are still popular in power industry for frequency regulation it does not eliminate the conflict between the static and dynamic accuracy. This conflict may be resolved by employing the principle of Dual Mode control. The Dual Mode controller operates by switching between proportional controller mode and Integral controller mode depending upon the magnitude of the Area Control Error (ACE). The Artificial Bee Colony (ABC) algorithm is used to optimize the cost function of the two area interconnected power system along with the PI controller. In this paper proposes Dual Mode two
Digital Stabilization of Fuzzy Systems with TimeDelay and Its Application to Backing up Control of a TruckTrailer
"... This paper presents the design methodology of digital fuzzy controller(DFC) for the systems with timedelay. We propose the fuzzy feedback controller whose output is delayed with unit sampling period and predicted. The analysis and the design problem considering timedelay become very easy because t ..."
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This paper presents the design methodology of digital fuzzy controller(DFC) for the systems with timedelay. We propose the fuzzy feedback controller whose output is delayed with unit sampling period and predicted. The analysis and the design problem considering timedelay become very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy system with timedelay is solved by linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to solve the stable feedback gains and a common Lyapunov function for designed fuzzy control system. Furthermore, we develop a control system for backing up a computersimulated trucktrailer with the consideration of timedelay. By using the proposed method, we design a DFC which guarantees the stability of the control system in the presence of timedelay.
Design and Implementation of Fuzzy Logic system for DC motor Speed Control
"... Abstract — In this paper an integrated electronic system has been designed, constructed and tested. The system utilizes an interface card through the parallel port in addition to some auxiliary circuits to perform fuzzy control operations for DC motor speed control with load and no load. Software is ..."
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Abstract — In this paper an integrated electronic system has been designed, constructed and tested. The system utilizes an interface card through the parallel port in addition to some auxiliary circuits to perform fuzzy control operations for DC motor speed control with load and no load. Software is written using (C++ language Ver. 3.1) to display the image as control panel for different types of both conventional and fuzzy control. The main task of the software is to simulate: first, how to find out the correct parameters for fuzzy logic controller (membership’s function, rules and scaling factor). Second, how to evaluate the gain factors (KP, KI and KD) by ZieglerNichols method. When executing any type of control process the efficiency is estimated by drawing the relative speed response for this control. I.