Adaptive Inverse Control Algorithm Research Articles

Event-triggered adaptive neural inverse optimal output-feedback control of steer-by-wire vehicle systems with prescribed performance

In this article, an event-triggered adaptive neural network (NN) output-feedback inverse optimal control issue is investigated for the steer-by-wire vehicle (SBWV) systems. Firstly, NNs are utilized to approximate the unknown nonlinear dynamics and the auxiliary system of SBWV systems is established. Then, a NN state observer is constructed to estimate the unmeasured states. To obtain better tracking performance, the prescribed performance technique is introduced to constrain the tracking error. An event-triggered mechanism (ETM) is established to decrease the numbers of controller execution times. Subsequently, an event-triggered adaptive NN inverse optimal output-feedback control algorithm is proposed by employing the backstepping control theory. It is proved that the developed control method can not only ensure the stability of the SBWV systems, but also guarantee the tracking error does not exceed the prescribed performance bound and converges to a small neighborhood of zero. Finally, simulation results are given to verify the validity of the proposed control method.

Adaptive inverse control of chatter vibrations in internal turning operations

Abstract In this article, adaptive inverse control of chatter vibrations in internal turning process is addressed. The active boring bar is composed of a slender steel cutting tool, an electrodynamic shaker as controllable actuator and an IEPE accelerometer as feedback sensor. The SISO control system actuates in the direction normal to the cut surface. A novel adaptive inverse control algorithm is presented in this paper. This algorithm is mainly used in the feed-forward Active Noise Control (ANC) applications and is known as the Filtered-x Normalized Least Mean Square (FxNLMS). In order to extend the application of adaptive inverse control algorithms in the field of Active Vibration Control (AVC), the FxNLMS algorithm with feedback architecture is suggested for the specific problem of chatter suppression in internal turning operations. The performance of developed adaptive feedback controller is experimentally verified during the internal turning of Aluminum alloy 6063-T6. The value of critical limiting depth of cut is anticipated to be nearly 0.2 [mm] for the slender boring bar. However, the stable cutting process is conducted by the active boring bar in the presence of adaptive controller up to depth of cut 2 [mm]. That is, the boundary of stability is enhanced by at least 10 folds. It has been observed that the amplitude of chatter vibrations is efficiently suppressed adjacent to the fundamental resonance peak of the active boring bar. In addition, the feedback FxNLMS controller effectively attenuates the boring bar’s vibration by at least 70 dBs. Moreover, the periodic chatter marks are eliminated from the surface texture of workpiece and the roughness of cut surface is remarkably improved. The obtained results suggest that the practical application of adaptive inverse control algorithms for chatter rejection can be extended to other machining processes as well.

Research on the Random Shock Vibration Test Based on the Filter-X LMS Adaptive Inverse Control Algorithm

The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.

Design and implementation of adaptive inverse control algorithm for a micro‐hand control system

The Letter proposes an online tuned adaptive inverse position control algorithm for a micro-hand. First, the configuration of the micro-hand is discussed. Next, a kinematic analysis of the micro-hand is investigated and then the relationship between the rotor position of micro-permanent magnet synchronous motor and the tip of the micro-finger is derived. After that, an online tuned adaptive inverse control algorithm, which includes an adaptive inverse model and an adaptive inverse control, is designed. The online tuned adaptive inverse control algorithm has better performance than the proportional–integral control algorithm does. In addition, to avoid damaging the object during the grasping process, an online force control algorithm is proposed here as well. An embedded micro-computer, cRIO-9024, is used to realise the whole position control algorithm and the force control algorithm by using software. As a result, the hardware circuit is very simple. Experimental results show that the proposed system can provide fast transient responses, good load disturbance responses, good tracking responses and satisfactory grasping responses.

Implementation of electrohydraulic shaking table controllers with a combined adaptive inverse control and minimal control synthesis algorithm

This study presents a combined control strategy for electrohydraulic shaking table (EST) systems. The position frequency bandwidth of the EST system is so low that it results in a worse tracking accuracy during seismic tests, particularly when the frequency bandwidth of the desired position signal exceeds the frequency bandwidth of the EST position closed-loop system. In order to improve the position tracking accuracy, a combined control strategy is proposed, which utilises a recursive extended least square (RELS) algorithm to offline or online estimate the position closed-loop transfer function of the EST system, then uses the estimated transfer function to offline or online yield a feedforward inverse controller for extending the position frequency bandwidth, finally, employs a minimal control synthesis (MCS) algorithm to further improve the tracking performance of the EST position system. The proposed control strategy combines the merits of offline or online feedforward inverse compensation and adaptive control. The procedure of the proposed control strategy is programmed in MATLAB/Simulink, and is then compiled to a real-time PC with Microsoft Visual Studio.NET for implementation. A better tracking performance for the proposed control strategy is achieved in experiments by using actual EST.

Design and implementation of an adaptive inverse controller for a micro-permanent magnet synchronous motor control system

An adaptive inverse controller design for a micro-permanent magnet synchronous motor control system is proposed. The adaptive inverse controller is constructed by using an adaptive model and an adaptive controller. The parameters of the adaptive model and adaptive controller are on-line tuned. By using the proposed adaptive inverse controller, the transient responses, load disturbance responses and tracking responses of the control system are improved. To detect the shaft rotor position, a micro-encoder is attached with the micro-permanent magnet synchronous motor. The micro-encoder provides only 100 pulses/revolution because of its space limitation. As a result, the resolution of the position signal and speed signal is not good enough. In order to improve the resolution, a state estimator is proposed here. By using the proposed state estimator, the control system can be operated from 1 to 25 000 r/min. The adaptive inverse control algorithm and the state estimation algorithm are executed by a digital signal processor, TMS320F28335. In addition, the proposed adaptive inverse control algorithm can be applied to the position control for the micro-permanent magnet synchronous motor as well. Several experimental results validate the theoretical analysis. The experimental results show that the proposed system has good performance including transient responses, load disturbance responses, and tracking responses.

Online SVM regression algorithm-based adaptive inverse control

An adaptive inverse control algorithm is proposed by combining fast online support vector machine regression (SVR) algorithm with straight inverse control algorithm. Because training speed of standard online SVR algorithm is very slow, a kernel cache-based method is developed to accelerate the standard algorithm and a new fast online SVR algorithm is obtained. Then the new algorithm is applied in straight inverse control for constructing the inverse model of controlled system online, and output errors of the system are used to control online SVR algorithm, which made the whole control system a closed-loop one. Simulation results show that the new algorithm has good control performance.

A nonlinear model reference adaptive inverse control algorithm with pre-compensator

In this paper, the reduced-order modeling (ROM) technology and its corresponding linear theory are expanded from the linear dynamic system to the nonlinear one, and H ∞ control theory is employed in the frequency domain to design some nonlinear system s pre-compensator in some special way. The adaptive model inverse control (AMIC) theory coping with nonlinear system is improved as well. Such is the model reference adaptive inverse control with pre-compensator (PCMRAIC). The aim of that algorithm is to construct a strategy of control as a whole. As a practical example of the application, the numerical simulation has been given on matlab software packages. The numerical result is given. The proposed strategy realizes the linearization control of nonlinear dynamic system. And it carries out a good performance to deal with the nonlinear system.

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.

Model reference adaptive inverse control of a single link flexible robot

A new model reference adaptive inverse control algorithm is proposed for the end point position control of flexible manipulators. This controller forms a series model reference adaptive system where an estimated system inverse is placed in the feed forward path to provide an inverse control through regulation of controller parameters. It differs from other model reference control strategies in that a signal synthesis loop is included to compensate for the estimation error of the inverse controller and the output tracking error, and to improve stability. The stability of the overall control system is shown to be guaranteed using hyperstability theory. The proposed control scheme is applied to control the tip position of a single link flexible robot. A non-dimensionalized model is developed. Its zero-order hold equivalence in discrete time domain is described by an auto-regressive and moving average form. A lattice filter is utilized as a deconvolution filter. The time-delay behavior from the driving torque to the robot tip position due to robot structural flexibility is investigated.