High Stiffness Control Research Articles

Simulation of linear oscillating motor control based on model predictive control

Linear motor has promising application prospects because of its direct motion and thrust output. Linear oscillating motor, whose mover is inserted with a high stiffness spring, can provide high frequency reciprocating motion with high working efficiency. Former research indicates that combination strategy of feedforward control and feedback control has unique advantages on close loop control of high stiffness system. Control law of model predictive consists of feedforward and feedback to cope with the high stiffness control system with limited sample time. In this paper, model predictive control (MPC) is introduced to linear oscillating motor (LOM) based state observer. According to the law of MPC, stability analysis is proved and simulation results show that MPC can provide excellent dynamic performance with high control accuracy for LOM.

High-Stiffness Control of Series Elastic Actuators Using a Noise Reduction Disturbance Observer

Rendering a high impedance on a series elastic actuator (SEA) can improve motion control performance while retaining collision safety by a lower physical stiffness. The safe impedance range can be increased with high-gain velocity feedback control, but, in practice, this is limited by noise. This article applies the noise reduction disturbance observer to inner-loop velocity control of an SEA, attenuating noise and allowing higher safe rendered stiffness compared with standard torque and torque/velocity hierarchical control. Closed-form expressions for maximum passive stiffness and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Z$</tex-math></inline-formula> -region are found, shown to depend strongly on the high-frequency noise gain, and used to optimize the control gains. Performance is experimentally verified on a reaction-force SEA, validating the passivity of the high-stiffness control in impact (free space and stiff environment) while rendering a safe stiffness 3.0 times the intrinsic stiffness.

Reduced-order model based active disturbance rejection control of hydraulic servo system with singular value perturbation theory

Hydraulic servomechanism is the typical mechanical/hydraulic double-dynamics coupling system with the high stiffness control and mismatched uncertainties input problems, which hinder direct applications of many advanced control approaches in the hydraulic servo fields. In this paper, by introducing the singular value perturbation theory, the original double-dynamics coupling model of the hydraulic servomechanism was reduced to a integral chain system. So that, the popular ADRC (active disturbance rejection control) technology could be directly applied to the reduced system. In addition, the high stiffness control and mismatched uncertainties input problems are avoided. The validity of the simplified model is analyzed and proven theoretically. The standard linear ADRC algorithm is then developed based on the obtained reduced-order model. Extensive comparative co-simulations and experiments are carried out to illustrate the effectiveness of the proposed method.

1A1-D1 均一系ER流体を用いたDDシステムの高剛性制御 : むだ時間を考慮した安定性解析(45. 機能性流体およびその応用)

1A1-D1 均一系ER流体を用いたDDシステムの高剛性制御 : むだ時間を考慮した安定性解析(45. 機能性流体およびその応用)

Precision motion control of a magnetic suspension actuator using a robust nonlinear compensation scheme

This paper presents a robust nonlinear compensation algorithm for realizing large travel in magnetic suspension systems suffering from parameter variations and external disturbance forces. A geometric feedback linearization technique that utilizes the complete nonlinear description of the electromagnetic field distribution is employed to obtain large travel. Robustness to uncertainties in the feedback linearized system is achieved through the development of a discrete-time delay-control-based compensation algorithm. In comparison to previous developments, the new scheme removes the constraints of triangularity conditions in compensation of unmatched uncertainties. The performance of this algorithm is experimentally investigated on a magnetic suspension system. In each of the experiments, the controller is designed using the approximate nonlinear model of the system, which is significantly different from the actual plant model. For a fixed set of gains, the robust nonlinear controller accurately stabilizes the system for a large range of ball positions. In trajectory tracking performance evaluation, the controller provides tracking accuracies that are of the same order of magnitude as the accuracy of the position sensor. Finally, when the suspended ball is impressed with an external disturbance force, the controller provides adequate model regulation and rejection of disturbance forces, demonstrating high stiffness control. The experimental results, therefore, verify the consistent performance of the algorithm in realizing large travel in spite of parameter variations and external disturbances.