Gain-Scheduled Drive-based Damping Control for Industrial Robots

<p>The drivetrain flexibility of industrial robots limits their accuracy. To open up new areas of application for industrial robots, an increased dynamic path accuracy has to be obtained. Therefore, this paper addresses this issue by a gain-scheduled drive-based damping control for industrial robots with secondary encoders. For this purpose, a linear parameter-varying (LPV) model is derived as well as a system identification method is presented. Based on this, a gain-scheduled drive-based LPV damping control design is proposed, which guarantees stability and performance under variation of the manipulator configuration. The control performance of the approach is experimentally validated for the three base joints of a KUKA KR210-2 industrial robot. The approach realizes a trade-off between ease of implementation and control performance as well as robustness.</p>

Gain-Scheduled Drive-based Damping Control for Industrial Robots

<p>The drivetrain flexibility of industrial robots limits their accuracy. To open up new areas of application for industrial robots, an increased dynamic path accuracy has to be obtained. Therefore, this paper addresses this issue by a gain-scheduled drive-based damping control for industrial robots with secondary encoders. For this purpose, a linear parameter-varying (LPV) model is derived as well as a system identification method is presented. Based on this, a gain-scheduled drive-based LPV damping control design is proposed, which guarantees stability and performance under variation of the manipulator configuration. The control performance of the approach is experimentally validated for the three base joints of a KUKA KR210-2 industrial robot. The approach realizes a trade-off between ease of implementation and control performance as well as robustness.</p>

Gain-Scheduled Control of Asymmetric Thrust Magnetic Bearing

The thrust position of the magnetic levitation rotor can be changed, bringing convenience to the practical application of cold compressors. This paper derives the mathematical model of asymmetric thrust magnetic bearings for a cold compressor and analyzes the changes in the system characteristics with the equilibrium position. By constructing PID controllers associated with the structural parameters of the magnetic bearing, the adaptive adjustment of the control parameters under different balanced position commands is realized. The simulation and experimental results prove that the gain-scheduled control method proposed in this paper can achieve a robust stability of the rotor in the range of 50 to 350 μm, and not at the cost of the response speed, adjustment time, and overshoot. The research results have reference significance for the structure design of asymmetric thrust magnetic bearings and play an important role in the commissioning and performance improvement of cold compressors.

Robust Controller Designing for an Air-Breathing Hypersonic Vehicle with an HOSVD-Based LPV Model

This paper focuses on the linear parameter varying (LPV) modeling and controller design for a flexible air-breathing hypersonic vehicle (AHV). Firstly, by selecting the measurable altitude and velocity as gain-scheduled variables, the original longitudinal nonlinear model for AHV is transformed into the LPV model via average gridding division, vertex trimming, Jacobian linearization, and multiple linear regression within the entire flight envelope. Secondly, using the tensor product model transformation method, the obtained LPV model is converted into the polytopic LPV model via high-order singular value decomposition (HOSVD). Third, the validity and applicability of the HOSVD-based LPV model are further demonstrated by designing a robust controller for command tracking control during maneuvering flight over a large envelope.