Adaptive input matching control of structurally varying plants

Adaptive control using input matching can avoid the identifiability and/or exact model-matching constraints of the bulk of existing adaptive control methods. The focus on input matchability allows the use of general controller structures capable of controlling a plant whose structure may vary arbitrarily within an allowable class. The applicability of this technique is demonstrated for two simulated plants: a MIMO linear network increasing its complexity due to component failure and an unstable SISO plant controlled by an actuator which gradually develops a nonlinearity. The sole need for pre-specification of an allowable plant structure re-focuses the thrust of system identification from parameter estimation to structure determination.     

Adaptive robust control of linear motors with dynamic friction compensation using modified LuGre model

LuGre model has been widely used in dynamic friction modeling and compensation. However, there are some practical difficulties when applying it to systems experiencing large range of motion speeds such as, the linear motor drive system studied in the article. This article first details the digital implementation problems of the LuGre model based dynamic friction compensation. A modified model is then presented to overcome those shortcomings. The proposed model is equivalent to LuGre model at low speed, and the static friction model at high speed, with a continuous transition between them. A discontinuous projection based adaptive robust controller (ARC) is then constructed, which explicitly incorporates the proposed modified dynamic friction model for a better friction compensation. Nonlinear observers are built to estimate the unmeasurable internal state of the dynamic friction model. On-line parameter adaptation is utilized to reduce the effect of various parametric uncertainties, while certain robust control laws are synthesized to effectively handle various modeling uncertainties for a guaranteed robust performance. The proposed controller is also implemented on a linear motor driven industrial gantry system, along with controllers with the traditional static friction compensation and LuGre model compensation. Extensive comparative experimental results have been obtained, revealing the instability when using the traditional LuGre model for dynamic friction compensation at high speed experiments and the improved tracking accuracy when using the proposed modified dynamic friction model. The results validate the effectiveness of the proposed approach in practical applications.     

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.     

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.     

Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network

This work presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.     

High-speed precision tracking with harmonic drive systems using integral manifold control design

Harmonic drives are popular in precision positioning applications such as military radars, satellite cameras, and wafer alignment machines because of their unique property of near-zero backlash. However, precision positioning performance is degraded by non-linear effects of inherent kinematic error and flexibility. This paper presents new non-linear controller development along with experimental verification to compensate for kinematic error in the presence of flexibility in high-speed regulation and trajectory tracking applications. Several issues in implementation of complex theoretical controllers in experiments have been discussed. The development uses our previous algorithms to compensate only for the kinematic error ignoring flexibility effects (U.S. Patent 6,459,940). The proposed control development is based on recent results on the integral manifold approach and guarantees asymptotic stability. We present simulation and experimental results to further demonstrate the effectiveness of our approach for practical application. Our results thus establish a solid basis for high-speed, high-precision control design for harmonic drive systems.     

Backstepping wavelet neural network control for indirect field-oriented induction motor drive

This study address a newly designed decoupling system and a backstepping wavelet neural network (WNN) control system for achieving high-precision position-tracking performance of an indirect field-oriented induction motor (IM) drive. First, a decoupling mechanism with an online inverse time-constant estimation algorithm is derived on the basis of model reference adaptive system theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. Moreover, based on the backstepping design methodology, a desired feedback control law is developed for ensuring the favorable control performance. However, the uncertainties, such as mechanical parameter uncertainty, external load disturbance, unstructured uncertainty due to nonideal field orientation in transient state, and unmodeled dynamics in practical applications, are difficult to know in advance. Thus, the stability of the desired feedback control may be destroyed. Due to the powerful approximation ability of WNN, a backstepping WNN control scheme is designed in this study to control the rotor position of an indirect field-oriented IM drive for periodic motion. This control scheme contains two parts: one is a WNN control that is utilized to mimic the desired feedback control law, and the other is a robust control that is designed to recover the residual part of approximation for ensuring the stable control characteristic. In addition, numerical simulation and experimental results due to periodic commands are provided to verify the effectiveness of the proposed control strategy.     

Adaptive control for mobile robot using wavelet networks

This work improves recent results concerning the adaptive control of mobile robots via neural and wavelet networks, in the sense that the stability proof, based on the second method of Lyapunov, encompasses (1) unmodeled dynamics and disturbances in the robot model; (2) adaptation of all parameters in the wavelet networks; and (3) a flexible procedure for automatically adjusting the wavelet architecture. Prior knowledge of dynamic of the mobile robot and network training is not necessary because the controller learns the dynamics online. The wavelet network's parameters and structure are also adapted online. Simulation results are presented by using parameters of the Magellan mobile robot from IS Robotics, Inc.     

Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.     

Adaptive passive control with varying time delay

This paper presents a passive control scheme for a force reflecting bilateral teleoperation system with a varying time communication delay. To improve the stability and performance of the system, the master and slave must be coupled dynamically via a transmission network through which the force and velocity are communicated bilaterally. However, the time delay caused by various factors, such as the transmission distance, network congestion, and communication bandwidth, is a long-standing impediment to bilateral control that can destabilize the system. In this study, we investigated how a varying time delay affects the stability of a teleoperation system. A new optimal adaptive approach based on a passive control scheme was designed bilaterally for both the master and slave sites. Extra variables were transmitted together with the wave variables in the scattering system. The proposed scheme achieved both passive control, and an acceptable tracking performance. The tracking performance was demonstrated using a computer simulation of varying time delays in a bilateral teleoperation system.     

Adaptive control for robot manipulators using multiple parameter models

In this paper, we propose multiple parameter models based adaptive switching control system for robot manipulators. We first uniformly distribute the parameter set into a finite number of smaller compact subsets. Then, distributed candidate controllers are designed for each of these smaller compact subsets. Using Lyapunov inequality, a candidate controller is identified from the finite set of distributed candidate controllers that best estimates the plant at each instant of time. The design reduced the observer-controller gains by reducing modeling errors and uncertainties via identifying an appropriate control/model via choosing largest guaranteed decrease in the value of the Lyapunov function energy function. Compared with CE based CAC design, the proposed design requires smaller observer-controller gains to ensure stability and tracking performance in the presence of large-scale modeling errors and disturbance uncertainties. In contrast with existing adaptive method, multiple model based distributed hybrid design can be used to reduce the energy consumption of the industrial robotic manipulator for large scale industrial automation by reducing actuator input energy. Finally, the proposed hybrid adaptive control design is experimentally tested on a 3-DOF Phantom^TM robot manipulator to demonstrate the theoretical development for real-time applications.

     

Adaptive motion control using neural network approximations

In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared.     

Adaptive bearing friction compensation based on recent knowledge of dynamic friction

Gimbal bearing friction is a major source of stabilization errors for airborne pointing and tracking systems. This paper describes a novel addition to conventional stabilization techniques which has recently been incorporated in such a system to greatly improve stabilization performance. This addition contains a model in system software which predicts realtime friction torque values. This new, dynamic friction model, which is the result of recent investigations into dynamic friction characteristics, is adaptively adjusted into agreement with actual friction behavior by processing inputs from conventional system sensors. Measurements from these sensors cause on-line adjustment of model parameters, resulting in ‘adaptive’ compensator action. The model's output is used to generate an addition to conventional stabilization subsystem commands. The resulting additional gimbal motor torque is equal and opposed to the actual friction disturbance such that the residual torque, and hence stabilization errors, are a small fraction of those for an uncompensated system. The model-referenced compensator thus operates in a predictive, adaptive, feedforward manner to pre-condition the stabilization subsystem, reducing stabilization errors well below levels which are achievable through conventional feedback operation alone.     

Adaptive output control with compensation of biased harmonic disturbance

Methods for compensation of biased harmonic disturbances using measurements of the output variable of the plant are developed. An algorithm of adaptive control, which outperforms known analogues in simplicity of implementation and in a number of characteristics is proposed.     

Adaptive control of a nonlinear dc motor drive using recurrent neural networks

A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system.     

Research on nonlinear friction compensation of harmonic drive in gimbal servo-system of DGCMG

The purpose of this paper is to present our results in overcoming the influence of the nonlinear friction afforded by harmonic drive to the gimbal servo-system of double-gimbal control momentum gyro (DGCMG). The existence of compliance and oscillation inherent in harmonic drive systems, and the lack of any technical information on the internal dynamics of the transmission, make the development of friction compensation in harmonic drive system extremely challenging. In this paper, the modeling of nonlinear friction in harmonic drive gear transmission in gimbal servo-system of the DGCMG is proposed. The relationship among the nonlinear friction, the angular velocity and the angular position with an improved Coulomb-Viscous model is derived, and the experiments to identify the various parameters of the improved model are given. At last a feed-forward compensation controller based on the improved model is designed to carry out the friction compensation study.     

Robust control for nonlinear motor-mechanism coupling system using wavelet neural network

A robust controlled toggle mechanism, which is driven by a permanent magnet (PM) synchronous servo motor is studied in this paper. First, based on the principle of computed torque control, a position controller is developed for the motor-mechanism coupling system. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a wavelet neural network (WNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on the Lyapunov stability a robust control system, which combines the computed torque controller, the WNN uncertainty observer and a compensated controller is proposed to control the position of the motor-mechanism coupling system. The computed torque controller with WNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer. Finally, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed robust control system are robust with regard to parametric variations and external disturbances.     

Estimation and model predictive control of non-linear batch processes using linear parameter varying models

In this paper, feedback control is implemented for batch processes using linear models which describe the batch dynamics locally along its optimal trajectory. A Linear Parameter Varying (LPV) model obtained by interpolation between these multiple models is used to emulate the behaviour of the non-linear batch. The interpolation functions and state estimates are computed using a recursive Bayesian technique. The control technique is based on model predictive control (MPC) which is used for regulation and targeting the product specifications at the end of the batch.     

Intelligent control of SVC using wavelet neural network to enhance transient stability

In order to enhance transient stability in a power system, a new intelligent controller is proposed to control a Static VAR compensator (SVC) located at center of the transmission line. This controller is an online trained wavelet neural network controller (OTWNNC) with adaptive learning rates derived by the Lyapunov stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller. One of the proposed controller features is robustness to different operating conditions and disturbances. The test power system is a two-area two-machine system power. The simulation results show that the oscillations are satisfactorily damped out by the OTWNNC.