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 tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

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.     

Power signal classification using dynamic wavelet network

A new approach to classification of non-stationary power signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary power signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary power signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN).     

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.     

Nonlinear model predictive control using parameter varying BP-ARX combination model

A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.     

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 dynamic surface control of nonaffine nonlinear system with time‐varying state constraints

This paper investigates the problem of adaptive dynamic surface control for pure‐feedback time‐varying state constrained nonaffine nonlinear system. A continuous and semibounded condition is proposed of nonaffine function to ensure that the system can be controlled, and the invariant set is introduced for this mild condition. By employing the dynamic surface control technique, the “complexity explosion” problem caused by backstepping technique is averted in developed control method. Robust compensators are devised to weaken poor effect of disturbances and uncertainties. The time‐varying barrier Lyapunov functions are adopted to dispose the problem of time‐varying state constrains. Moreover, it is proved that the whole closed‐loop signals are bounded, the tracking error can converge to a small neighborhood of zero, and the system states are insured to maintain in the predefined compact sets. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed method.     

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 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.

     

舰载机着舰侧回路时变风险权值矩阵线性变参数预测控制

针对舰载机钩索阻拦阶段,提出阻拦风险函数的概念和计算方法,通过离线计算获得时变状态权值矩阵和时变控制输入权值矩阵.采用时变状态权值矩阵可以实时调整各状态之间的变化关系,而时变的控制输入权值矩阵能够调整副翼和方向舵的输入峰值,避免出现输入饱和情况,提高横侧向自动着舰的效率和安全性.建立了时变的系统输出约束函数,增加了舰载机进舰阶段控制器的可解性.建立了基于状态偏差的舰载机横侧向着舰模型,针对舰载机上很多状态不能直接测量的特点,采用离线设计状态观测器的方法来估计舰载机的真实状态值,利用线性矩阵不等式求解系统最优解,通过舰载机自动着舰三维仿真模拟平台验证了算法的可行性.     

Speed tracking control for the gimbal system with harmonic drive

This paper presents a composite controller based on a disturbance observer for the gimbal system of double-gimbal magnetically suspended control moment gyro (DGMSCMG) with harmonic drives. The controller removes the influence by coupling moments and nonlinear transmission torques. The disturbances are estimated by the designed disturbance observer. By introducing a state feedback controller, the disturbances can be eliminated from the output channel of the system. The gain selection principle of the disturbance observer is also analyzed. Both the simulation and experimental results indicate that the proposed control method can reject mismatched disturbances and improve system performance.     

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 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.