Learning Variable Structure Control Research Articles

Performance evaluation of iterative learning control with different sliding mode algorithms for the slip‐ratio control of an electric vehicle

AbstractDeveloping a control strategy that can handle model uncertainties, external disturbances, and parameter fluctuations is essential for the desired operation of a nonlinear system. An attempt is made to develop such a control algorithm using the Learning Variable Structure Control technique. The proposed controllers have the advantages of both iterative learning control (ILC) and sliding mode control (SMC) such that they can compensate both structured and unstructured uncertainties. They are made comprised of modified‐twisting algorithm and super‐twisting algorithm respectively with the ILC technique. In this article, the performance of the two developed controllers is compared to a traditional SMC‐based ILC approach. Based on simulation results, the super‐twisting algorithm based ILC is found to be superior with reference to speed of convergence and tracking accuracy. This method is used to control the slip‐ratio of an electric vehicle as a specific application in this paper.

Periodic speed ripples minimization in PM synchronous motors using repetitive learning variable structure control

Abstract In this paper, we propose a simple repetitive learning variable structure control (RLVSC) scheme to reduce periodic speed ripples in a permanent magnet synchronous motor (PMSM). These speed ripples are induced by parasitic torque pulsations that vary periodically with rotor position. The conventional PI speed controller is able to reduce speed ripples to a certain extent but not sufficient enough for many high performance applications. During steady state, the RLVSC generates a reference compensation current that together with the outer loop PI speed controller is used to minimize the speed ripples. Being a plug-in module, the proposed RLVSC controller can be easily integrated to any of the existing PMSM drive systems. Experimental evaluation of the proposed scheme is carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed control scheme in reducing speed ripples by a factor of approximately 3 under various operating conditions.

On functional approximation of the equivalent control using learning variable structure control

A learning variable structure control (LVSC) approach is originated to obtain the equivalent control of a general class of multiple-input-multiple-output (MIMO) variable structure systems under repeatable control tasks. LVSC synthesizes variable structure control (VSC) as the robust part which stabilizes the system, and learning control (LC) as the plug-in intelligent part which completely nullifies the effects of the matched uncertainties on tracking error. Rigorous proof based on energy function and functional analysis shows. that the tracking error sequence converges uniformly to zero, and that the bounded LC sequence converges to the equivalent control almost everywhere.

Learning variable structure control approaches for repeatable tracking control tasks

In this paper, we consider repeatable tracking control tasks using a new control approach—learning variable structure control (LVSC). LVSC synthesizes two main control strategies: variable structure control (VSC) as the robust part and learning control as the intelligent part. The incorporation of the powerful learning function, by virtue of the internal model principle, completely nullifies the tracking error. The switching control mechanism on the other hand, retains the well appreciated properties of VSC, especially theinsensitivity to unstructured system uncertainties. Through a rigorous proof based on energy function and functional analysis, we show that the LVSC system achieves the following novel properties: (1) the tracking error sequence converges uniformly to zero; (2) the bounded learning control sequence converges to the equivalent control, i.e. the desired control profile almost everywhere; (3) the system state sequence and VSC control sequence are uniformly continuous. To address important practical considerations, the learning mechanism is implemented by means of Fourier series expansions, hence achieves better tracking performance.

A Learning Variable Structure Controller of a Flexible One-Link Manipulator

In this paper, tip regulation of a flexible one-link manipulator by Learning Variable Structure Control (LVSC) is investigated. Switching surface is designed according to a selected reference model which relocates system poles to be negative real ones, hence link vibration is eliminated. The proposed LVSC incorporates a learning mechanism to improve regulation accuracy. Rigorous proof shows: the tracking error sequence converges uniformly to zero; the uniformly bounded learning control sequence converges to the equivalent control almost everywhere. For practical considerations, the learning mechanism is further conducted in frequency domain by means of Fourier series expansion, hence achieves better regulation performance. Numerical simulations confirm the effectiveness and robustness of the proposed approach. [S0022-0434(00)01804-9]

Fourier series-based repetitive learning variable structure control of hard disk drive servos

The demand for larger capacity and higher track density of hard disk drives requires significant improvement of actuator track following control. We developed a new repetitive learning system which synthesizes variable structure control (VSC) and repetitive learning during track following to improve tracking accuracy. Proof shows it completely nullifies the tracking error caused by bias force and repeatable runout. The proposed learning scheme uses the past VSC signal for updating instead of the past tracking error, and hence achieves a fast convergence. The Fourier series-based implementation selectively attenuates disturbance at given frequencies, hence avoids the influence from noise and other nonrepeatable factors.