Adaptive Inverse Control Method Research Articles

Research on an Ice Tolerance Control Method for Large Aircraft Based on Adaptive Dynamic Inversion

Considering the effect of icing on aircraft control performance, this paper proposes an adaptive dynamic inverse ice tolerance control method based on piecewise constant. A control allocation algorithm is introduced to compensate for the change of control surface performance caused by icing. This method can achieve satisfactory disturbance estimation accuracy under a given sampling time, and thus ensure a closed-loop system error within an acceptable range. The proposed design method is applied to the design of a flight control law for a transport aircraft, aiming to solve the problem of ice-tolerant flight control, reduce the influence of icing conditions on controllability and safe flight of the transport aircraft, and thus improve the flight quality of the transport aircraft. The simulation results are verified under the influence of both standby ice type and failure ice type, and the interference effect on aircraft aerodynamic parameters is further added. The simulation results show that adaptive dynamic inverse control based on piecewise constant can overcome the influence caused by icing and aerodynamic parameter interference, achieve accurate tracking of command, and provide excellent fault tolerance and robustness, which ensures that the transport aircraft can achieve the desired control performance and safe flight capability.

Observer-based adaptive neutral network inverse optimal containment control for nonlinear multiagent systems with input quantization

This article focuses on the issue of addressing an adaptive neural network (NN) inverse optimal containment control for nonlinear multiagent systems (MASs), which are subject to immeasurable states and quantized input signals simultaneously. To tackle this problem, we utilize a NN to model unknown agents and design a NN observer to estimate the immeasurable states. Additionally, we decompose the hysteretic quantized input into two bounded nonlinear functions. By employing the adaptive backstepping approach and inverse optimal principle, we formulate an adaptive NN inverse optimal containment control method. The developed inverse optimal containment control scheme guarantees that the controlled system is input-to-state stabilizable (ISS). Finally, we validate the effectiveness of our proposed control scheme through simulation results.

An Adaptive Inverse Model Control Method of Vehicle Yaw Stability with Active Front Steering Based on Adaptive RBF Neural Networks

In order to enhance vehicle yaw stability, this paper proposes a novel adaptive inverse model control (AIMC) method for active front steering (AFS) design based on adaptive radial basis function (RBF) neural networks. To approximate the uncertain nonlinearity and modeling errors in vehicle dynamics, an adaptive RBF neural network (ARBFNN) controller is firstly designed. For smaller tracking errors and better robustness, a RBF networks-based AIMC system is further established. Apart from the ARBFNN control law, another two RBF neural networks are utilized, which work as model identifier and inverse model controller, respectively. After being trained offline, both of them are updated by using online learning algorithms. For faster learning and stability, adaptive learning rates are developed. Consequently, the inverse model controller is able to generate required steering wheel angle. Finally, the comparative studies are carried out and the simulation results illustrate the robustness and effectiveness of proposed control strategy.

Observer-Based Adaptive Inverse Optimal Output Regulation for a Class of Uncertain Nonlinear Systems

This paper addresses the adaptive inverse optimal output regulation problem for a class of uncertain nonlinear systems driven by an exosystem. The unknown parameters, internal disturbances, and unmeasured states are contained in the nonlinear system. Firstly, the output regulation problem is decomposed into a feedforward control design problem which can be solved by the internal model based on the output regulation theory, and an adaptive inverse optimal stabilization problem. Then an auxiliary system is designed, and a new state observer related to the auxiliary system is given. By combining adaptive control technology and inverse optimal control method, a novel adaptive output feedback inverse optimal controller is developed to make the output of the system track the reference signal fast. With this control strategy, all the signals of the closed-loop system are uniformly ultimately bounded (UUB), and the newly well-defined cost functional which is connected with the auxiliary system and the controller can be minimized. Finally, a simulation case is put forward to verify the feasibility of the newly raised controller and the state observer.

A Vehicle Handling Inverse Dynamics Method for Emergency Avoidance Path Tracking Based on Adaptive Inverse Control

In this paper, a new adaptive inverse control (AIC) method is presented to solve vehicle handling inverse dynamics problem and achieve path tracking control for emergency avoidance. The designed AIC system includes two crucial modules: the model identifier and the inverse model controller which are both the nonlinear adaptive filters constituted by neural networks. Firstly, they are both trained offline and connected with the vehicle to be the initial states of AIC system. Then, the back-propagation (BP) algorithm is used to adjust the weight parameters of the model identifier to identify the nonlinear characteristics of vehicle in real time. Simultaneously, the inverse model controller is used to be the controller of AIC system and the controller's weight parameters are tuned by the back-propagation through model (BPTM) algorithm online. A novel adaptive learning rate is introduced into the BP algorithm and the BPTM algorithm to guarantee the stability of neural networks. Finally, the inverse model controller can be regarded as the inverse of vehicle and used to solve required steering wheel angle according to the desired path to realize path tracking. The double lane change (DLC) road is used to be the desired path for emergency avoidance. The simulation results illustrate that the AIC system could realize accurate path tracking although there exist external disturbances and the AIC is an effective method to deal with the handling inverse dynamics problem for emergence avoidance.

Adaptive fuzzy output feedback inverse optimal control for vehicle active suspension systems

In this paper, the adaptive fuzzy inverse optimal output feedback control problem for vehicular active suspension systems (ASSs) is addressed. The considered vehicular active suspension system (ASS) is a quarter-car model with electromagnetic actuators and contains unknown nonlinear characteristics. The fuzzy logic systems (FLSs) are used to approximate unknown nonlinearities, and an auxiliary system model is constructed. Based on auxiliary system model, a fuzzy state observer is designed to estimate immeasurable states. By using adaptive backstepping design technique and inverse optimal principle, an adaptive fuzzy output feedback inverse optimal strategy is proposed. The developed adaptive fuzzy inverse optimal output feedback control method not only guarantees that the attitude of the vehicle is stabilized, but also achieves inverse optimization in relation to the cost functional. Finally, the simulation studies are given in the cases of both bump road and sinusoid road displacement input, and the simulation results show the validity of the proposed control method.

Permanent Magnet Synchronous Motor Speed Control System Based on Adaptive Inverse Control Method

The classical feedback control method for the permanent-magnet synchronous motor cannot fulfill the dynamic requirement and anti-interference requirement at the same time. In this paper, an adaptive inverse control method with disturbance elimination is proposed based on the vector control including speed and current loops. It avoids the system instability and the anti-disturbance performance of the system can be enhanced as well. Firstly, the speed loop adopts the adaptive inverse controller possessing a feed forward control structure, and a normalized least-mean square filtering algorithm accelerates the speed error convergence. The inverse model of the approximately linearized system is obtained by modifying the weighting factor online. Secondly, in order to suppress and eliminate the influence of motor parameter perturbation and external disturbance on the control system, an extended state observer is designed to observe and compensate the disturbance of the system. Thus, the dynamic performance and anti-disturbance performance of the system can be improved simultaneously. Finally, the effectiveness of the proposed method is verified by experiment and simulation.

Adaptive Inverse Control Based on Kriging Algorithm and Lyapunov Theory of Crawler Electromechanical System

The electromechanical system of a crawler is a multi-input, multioutput strongly coupled nonlinear system. In this study, an adaptive inverse control method based on kriging algorithm and Lyapunov theory is proposed to improve control accuracy during adaptive driving. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the crawler. In accordance with the kriging algorithm, the inverse model of the electromechanical system of the crawler is established by offline data. The adaptive travel control law of the crawler is obtained on the basis of Lyapunov theory. Combined with the kriging algorithm, the adaptive driving reverse control method is designed, and the online system is used to update and perfect the inverse system model in real time. Finally, the virtual prototype model of the crawler is established, and the control effect of the adaptive inverse control method is verified by theoretical analysis and virtual prototype simulation.

Adaptive Inverse Control for Gripper Rotating System in Heavy-Duty Manipulators With Unknown Dead Zones

The gripper rotation system of a heavy-duty manipulator is generally driven by hydraulic motors, and it is difficult to guarantee the accuracy of the rotation angle using conventional control methods due to the existence of dead zones with unknown characteristics. This paper proposes an adaptive inverse dead-zone control method, in which the gradient projection technique is applied to design an adaptive updating law for estimating the inverse dead-zone parameters online. First, a dynamic model of the gripper rotation system is presented with an analysis of its dead-zone characteristics. Then, the controller is designed based on a Takagi–Sugeno fuzzy model, and the estimated values of the inverse dead-zone parameters are shown to converge to their true values. Finally, the performance of the proposed adaptive control method is experimentally demonstrated on an actual forging manipulator. Comparisons with a fixed inverse dead-zone control method demonstrate the effectiveness and applicability of the proposed control method.

Robust Adaptive Inverse Control Based on Maximum Correntropy Criterion

Adaptive inverse control, proposed by Bernard Widrow, is mainly based on the well-known least mean square (LMS) algorithm. The LMS is a stochastic gradient algorithm under the minimum mean square error (MSE) criterion, which performs well for linear and Gaussian systems. However, its performance will become poor when signals are non-Gaussian, especially when systems are disturbed by impulsive noises. In this work, in order to improve the robustness of the adaptive inverse control against impulsive noises, we propose a new adaptive inverse control method, which is based on the recently developed maximum correntropy criterion (MCC) algorithm. The MCC algorithm aims at maximizing the correntropy between the model output and the desired response. Since correntropy is a nonlinear similarity measure that contains higher-order statistics of the signals and is insensitive to large outliers, the proposed method can achieve desirable performance in impulsive noise environments. Theoretical results on optimal solution and convergence are derived. Simulation results are also presented to demonstrate the superior performance of the new method.

The Use of Adaptive Inverse Control in Cascade STATCOM Modulated by PAM+PWM

In order to lower the switching losses and harmonic ratio, improve the precision of the STATCOM in the same time, the adaptive inverse control based on PAM+PWM method is proposed in this paper. The combination of PAM+PWM method is presented to enable the system to achieve the quality of low switching losses and good harmonic elimination effect. Furthermore, considering the nonlinear and time-varying characteristics which are caused by the introduction of the PAM and PWM method, an adaptive inverse control method is designed to adjust the parameters of the nonlinear mathematic model constantly to improve the precision of the control system. The results of simulation show the correctness of the proposed method.

Study on Vibration Controller Based on Adaptive Inverse Method

In this paper, study of adaptive inverse controller based on nonlinear multi-degree-of-freedom (NLMDF) algorithm has been carried out. A vibration control system with TMS320F2812 as the hardware platform has been developed, which can realize waveform reproduction and spectrum control. An improved least mean square algorithm-NLMDF has been introduced to design the filter parameters in the adaptive inverse control and simulations have been carried out in Matlab software environment. Accordance with the requirements of the adaptive inverse control method, hardware platform of the vibration control system based on dual-Digital Signal Processor (DSP) architecture, has been designed; through abstraction of the driver layer, system layer and application layer of the vibration control system , software platform has been developed. The experiment results show that functionality and performance indicators of the system have reached the design requirements.

Adaptive Inverse Control of Hydro Electric Unit Based on Wavelet Neural Networks

Due to the nonlinear, time-variable and non-minimum phase character of hydro electric unit system integrated with water, motor and power, a new adaptive inverse control method of hydro electric unit based on the function approximation ability of the wavelet analysis and the learning characteristic of neural network is presented. The algorithm and formulas and method of adaptive inverse control is studied. It approximates the model and its inversion of hydro electric unit by wavelet neural networks(WNN), and then through constructing an aim function of broad sense, which is effective to the nonlinear non-minimum phase system. Theory and simulation to for hydro electric unit system demonstrate that the control strategy can more effective improve the dynamic and stationary performance than those based on neural networks. It gives a new approach in control for hydro electric unit system besides offer a beneficial reference to the control of non-minimum phase systems.

An adaptive inverse control method based on SVM–fuzzy rules acquisition system for pulsed GTAW process

This paper proposes a new method of adaptive inverse control based on support vector machine–fuzzy rules acquisition system (SVM-FRAS) for the gas tungsten arc welding (GTAW) process. In this control mechanism, an identifier is established based on SVM-FRAS, and an inverse controller based on SVM-FRAS is designed. The proposed adaptive inverse control method can automatically extract control rules from the process data. Comprehensibility is one of the required characteristics for a complex GTAW process control. We use the proposed SVM-FRAS-based adaptive inverse control method to obtain the rule-based process and the control model of the aluminum alloy pulse GTAW process. Based on the simulation experiments for GTAW process, the SVM-FRAS adaptive inverse control method is found to be effective.

Adaptive Control of Helicopter Ground Resonance with Magnetorheological Damper

The methodology for adaptive control of helicopter ground resonance with magnetorheological (MR) damper is presented. The adaptive inverse control method is used to control the output damping force of MR damper and the range of the damping force is given. Through the adaptive inverse control, the damping force of MR damper is fit to a desired damping force. With the background of applying MR damper to control of helicopter ground resonance, a model of loss force and an adaptive arithmetic for stabilization of the coupled rotor/fuselage system are presented. The simulation shows that the controller presented in this paper can stabilize the rotor/fuselage coupling system quickly and control the helicopter ground resonance effectively.

Improved filtered-ɛ adaptive inverse control and its application on nonlinear ship maneuvering

The drawbacks of common nonlinear Filtered-ɛ adaptive inverse control (AIC) method, such as the unreliability due to the change of delay time and the faultiness existing in its disturbance control loop, are discussed. Based on it, the diagram of AIC is amended to accommodate with the characteristic of nonlinear object with time delay. The corresponding Filtered-ɛ adaptive algorithm based on RTRL is presented to identify the parameters and design the controller. The simulation results on a nonlinear ship model of “The R.O.V Zeefakkel” show that compared with the previous scheme and adaptive PID control, the improved method not only keeps the same dynamic response performance, but also owns higher robustness and disturbance rejection ability, and it is suitable for the control of nonlinear objects which have higher requirement to the maneuverability under complex disturbance environment.

A Smart Microrobot on Chip: Design, Identification, and Control

This paper deals with the microrobot on chip (MOC) concept which corresponds to the development of compact, inexpensive, and easily "plug and use" microrobotic components (as it is the usual case with electronic chips). A first prototype of a 6 degrees-of-freedom (DOF) piezoelectric micromanipulator MOC (MMOC) illustrates this concept: one scanning piezostage can move the gripper in x-y axes and the piezoelectric microgripper itself has two y-z DOF per finger. An embedded optical fiber connected to a CCD camera and a force self-sensing system allowing force feedback. The first prototype of MMOC is electrically and mechanically connected to a smartcard reader. In order to be able to manipulate safely and accurately micro-objects, a force/position closed-loop controller is proposed taking into account the static and dynamic behavior of the microgripper. Hysteresis compensation is obtained through Preisach model and then an adaptive inverse control method is employed for open-loop control strategy. Numerical and experimental results which validate the theoretical developments are presented.

Adaptive inverse control algorithm for shock testing

An adaptive inverse control algorithm is proposed for shock testing an arbitrary specimen using an electrodynamic actuator. The purpose is to ascertain whether the specimen can survive and continue to function under severe shock conditions. The main difficulty in shock control is that the specimen dynamics vary significantly and a control algorithm is required that adapts to the characteristics of a new specimen. 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. The standard filtered-x least mean square control structure used in the adaptive inverse control algorithm is modified to a block-processing structure, with the frequency-domain adaptive filter as the adaptation algorithm. Practical results show that the filtered-x frequency-domain adaptive filter control algorithm allows convergence of the shaker output to the assigned reference shock pulse.