Intelligent fractional-order backstepping control for an ironless linear synchronous motor with uncertain nonlinear dynamics.

This study aims to develop an intelligent fractional-order backstepping controller to control the mover position of an ironless permanent magnet linear synchronous motor. First, we investigated the operating principle and dynamic modeling of the linear synchronous motor based on the field-oriented control method. Next, to improve the convergence speed and control accuracy of the conventional backstepping controller, we designed a fractional-order backstepping controller that has more degrees of freedom in the control parameters. However, designing the switching control gain is difficult owing to the unknown degree of uncertainty. To address this problem, we proposed an intelligent fractional-order backstepping controller to further enhance the adaptiveness and robustness of the fractional-order backstepping controller. In this intelligent controller, we proposed a Hermite-polynomial-based functional-link fuzzy neural network as an uncertainty estimator that can directly estimate system uncertainty, thereby improving the disturbance rejection ability and requiring no uncertainty bound information. Additionally, to compensate for the estimation error introduced by the estimator, we designed an exponential compensator that employs a smooth exponential self-regulation mechanism. We utilized the Lyapunov theorem to derive estimation laws for the online tuning of the control parameters. Experimental results demonstrate the effectiveness and high positioning performance of the proposed intelligent fractional-order backstepping controller in comparison with the backstepping controller and fractional-order backstepping controller in the linear synchronous motor control system.

Precision Position Control of a Voice Coil Motor Using Self-Tuning Fractional Order Proportional-Integral-Derivative Control.

The object of this study is to develop a self-tuning fractional order proportional-integral-derivative (SFOPID) controller for controlling the mover position of a direct drive linear voice coil motor (VCM) accurately under different operational conditions. The fractional order proportional-integral-derivative (FOPID) controller can improve the control performances of the conventional integer order PID controller with respect to the additional fractional differential and integral orders; however, choosing five interdependent control parameters including proportional, integral, and derivative gains, as well as fractional differential and integral orders appropriately is arduous in practical applications. In this regard, the SFOPID controller is proposed in which the five control parameters are optimized dynamically and concurrently according to an adaptive differential evolution algorithm with a high efficiency adaptive selection mechanism. Experimental results reveal that the SFOPID controller outperforms PID and FOPID controllers with regard to the nonlinear VCM control system under both nominal and payload conditions.

Identification and characterization of a novel class of extracellular poly(3-hydroxybutyrate) depolymerase from Bacillus sp. strain NRRL B-14911.

The catalytic, linker, and denatured poly(3-hydroxybutyrate) (dPHB)-binding domains of bacterial extracellular PHB depolymerases (PhaZs) are classified into several different types. We now report a novel class of extracellular PHB depolymerase from Bacillus sp. strain NRRL B-14911. Its catalytic domain belongs to type 1, whereas its putative linker region neither possesses the sequence features of the three known types of linker domains nor exhibits significant amino acid sequence similarity to them. Instead, this putative linker region can be divided into two distinct linker domains of novel types: LD1 and LD2. LD1 shows significant amino acid sequence similarity to certain regions of a large group of PHB depolymerase-unrelated proteins. LD2 and its homologs are present in a small group of PhaZs. The remaining C-terminal portion of this PhaZ can be further divided into two distinct domains: SBD1 and SBD2. Each domain showed strong binding to dPHB, and there is no significant sequence similarity between them. Each domain neither possesses the sequence features of the two known types of dPHB-binding domains nor shows significant amino acid sequence similarity to them. These unique features indicate the presence of two novel and distinct types of dPHB-binding domains. Homologs of these novel domains also are present in the extracellular PhaZ of Bacillus megaterium and the putative extracellular PhaZs of Bacillus pseudofirmus and Bacillus sp. strain SG-1. The Bacillus sp. NRRL B-14911 PhaZ appears to be a representative of a novel class of extracellular PHB depolymerases.

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system.

In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.