Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

Xu, Weitao; Hou, Jie; Li, Jie; Yuan, Cong; Simeone, Alessandro

doi:10.1109/access.2020.3007422

Cited by 13 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, only the rotational speed of the moving axis decreases, which leads to the angular velocity synchronisation error between the axis and other axes. One of the moment’s feed back to the master axis, where an increase in

T_{s i}

will decrease the master axis

i

output speed, which causes the other axes to change with the same trend, and the synchronisation error is reduced [2, 7, 21–25]. Where

F_{i j}

defines the i th axis feedback to the j th layer, f ij , and g ij defines the transfer function of the slave axis

S_{i j}

disturbance point.…”

Section: Motor Synchronisation Control Schemementioning

confidence: 99%

“…In recent years, research achievements have also been made in the optimal cross coupled direction of multi motor control, which provides basic research for complex multi-layer and multi axis control systems [12][13][14]. With the deepening of research, robust control has also achieved research results, and the research on complex systems has taken a step further [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on torque feedback equivalent structures in the multi‐layer and multi‐axis synchronisation control model

Sun

2021

IET Circuits, Devices & Syst

View full text Add to dashboard Cite

This paper proposes a novel torque feedback equivalent structure oriented to the diversity of torque feedback in multi‐layer and multi‐axis complex systems. First, in view of the existing synchronisation control problem, this paper presents master reference synchronous model and master–slave synchronous model in a multi‐layer and multi‐axis synchronous control system. Second, the change of the direct feedback to front layer axis is lower than that of the direct feedback to master axis, and the adjustment difference of the axis output is low. Moreover, the stability and superiority of the proposed controller are theoretically analysed. By two methods, it can be seen that the control system can converge at a faster speed and run stably with good dynamic performance and synchronisation performance. The results verify the effectiveness of the adaptive control model in which the two equivalent torque feedback structures converge to zero.

show abstract

T_{s i}

will decrease the master axis

i

output speed, which causes the other axes to change with the same trend, and the synchronisation error is reduced [2, 7, 21–25]. Where

F_{i j}

defines the i th axis feedback to the j th layer, f ij , and g ij defines the transfer function of the slave axis

S_{i j}

disturbance point.…”

Section: Motor Synchronisation Control Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on torque feedback equivalent structures in the multi‐layer and multi‐axis synchronisation control model

Sun

2021

IET Circuits, Devices & Syst

View full text Add to dashboard Cite

show abstract

“…The iterative learning control problem is investigated for the distributed building automatic temperature system in [8], and a kind of mixed PD-type ILC algorithm is proposed to make the tracking error of parabolic singular distributed parameter systems converge to any tracking accuracy. In [9], a time-varying control method based on norm optimal crosscoupling iterative learning is proposed to improve the control precision of multi-axis motion control system. And a neural network-based error-track iterative learning control scheme is proposed in [10] to tackle trajectory tracking problem for tank gun control systems.…”

Section: Introductionmentioning

confidence: 99%

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

2021

View full text Add to dashboard Cite

MIT control strategy is a kind of model reference adaptive control method with the simplest structure. The reason why the structure is simple is that only the gain of the controlled object is adaptively adjusted. Because only the gain is adjusted, the ability of MIT control strategy to change the characteristics of the controlled object is limited. This also limits its application. In this paper, the simple idea of iterative learning control is introduced into MIT controller to increase the controller's ability to adjust the characteristics of the controlled object, and make it suitable for complex control objects. In the proposed control method, the output of the iterative learning controller is used to adjust the adaptive law of the MIT controller. The proposed control method is applied to the speed control system of ultrasonic motor. Experiments show that although only the gain of MIT controller can be adjusted, the learning process based on memory increases the degree of control freedom. Therefore, the dynamic characteristics of the system can be greatly changed, and the control performance can be significantly improved. Moreover, the proposed method only needs to add a simple P-type iterative learning controller to the MIT controller, and the increase of online calculation amount is small. INDEX TERMS Ultrasonic motor, iterative learning control (ILC), MIT control.

show abstract

“…This method adjusted the coupling gain coefficient in real time based on the changing rules of the position trajectory, so that the position tracking's performance on a multi-motor system increased. In [14], the proposed CCC design method was based on the optimal control theory, which can achieve high precision position control by converting the position control problem into the linear quadratic regulator problem. In addition, model predictive control (MPC) presents a fast dynamic response performance, and it has been widely applied in the PMSM drive systems, which provide an new idea for the improvement of the multi-motor control structure [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Control for Dual Permanent Magnet Motor System with Unified Nonlinear Predictive Control

Zhou

Zhang

et al. 2021

WEVJ

View full text Add to dashboard Cite

In order to improve the position tracking precision of dual permanent magnet synchronous motor (PMSM) systems, a unified nonlinear predictive control (UNPC) strategy based on the unified modeling of two PMSM systems is proposed in this paper. Firstly, establishing a unified nonlinear model of the dual-PMSM system, which contains uncertain disturbances caused by parameters mismatch and external load changes. Then, the position contour error and tracking errors are regarded as the performance index inserted into the cost function, and the single-loop controller is obtained by optimizing the cost function. Meanwhile, the nonlinear disturbance observer is designed to estimate the uncertain disturbances, which is used for feed-forward compensation control. Finally, the proposed strategy is experimentally validated on two 2.3 kW permanent magnet synchronous motors, and the experimental results show that effectiveness and feasibility of proposed strategy.

show abstract

Multi-Axis Motion Control Based on Time-Varying Norm Optimal Cross-Coupled Iterative Learning

Cited by 13 publications

References 20 publications

Research on torque feedback equivalent structures in the multi‐layer and multi‐axis synchronisation control model

Research on torque feedback equivalent structures in the multi‐layer and multi‐axis synchronisation control model

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

Cooperative Control for Dual Permanent Magnet Motor System with Unified Nonlinear Predictive Control

Contact Info

Product

Resources

About