Self-Constructing Chebyshev Fuzzy Neural Complementary Sliding Mode Control and Its Application.

In this article, a complementary sliding mode (CSM) controller using a self-constructing Chebyshev fuzzy recurrent neural network (SCCFRNN) is proposed for harmonic suppression control of an active power filter (APF). The SCCFRNN whose structure can be automatically learned through the designed structure self-learning algorithm is introduced to approximate the unknown nonlinear term in the APF dynamic model, so as to improve modeling accuracy and reduce the burden of CSM control (CSMC). The SCCFRNN combines the advantages of a fuzzy neural network (FNN), recurrent neural network (RNN), and Chebyshev neural network (CNN), and all parameters can be adjusted according to the designed adaptive laws. Eventually, through detailed simulation, hardware experiments, and fair comparison, the feasibility and superiority of the proposed control algorithm were verified.

Diagnostic Value of the HEAD-US-C Scale for the Knees of Severe Hemophilia A Patients.

OBJECTIVES: This study aimed to assess the reliability of the Hemophilia Early Arthropathy Detection with Ultrasound in China (HEAD-US-C) scale for the knees of severe hemophilia A (SHA) patients and to determine its diagnostic accuracy for assessments of knee-joint lesions in comparison with magnetic resonance imaging (MRI). METHODS: We collected data from 32 knee joints of 21 patients diagnosed with SHA. The knees were evaluated based on the HEAD-US-C scale and the results were compared with the International Prevention Study Group (IPSG) scale. The HEAD-US-C scale was applied independently by two trained ultrasonographers blinded to the MRI results. The IPSG scale was applied independently by two radiologists blinded to the clinical data and ultrasound (US) results. RESULTS: The IPSG and HEAD-US-C scales exhibited good to excellent inter-rater reliability. Additionally, there was good to excellent agreement between the US and MRI results for the detection of knee lesions in SHA patients. The sensitivities of US for joint effusion, synovial hyperplasia, cartilage loss, and bone-surface irregularities in the knees of patients were 92.59, 100, 95.45, and 87.50%, respectively. The HEAD-US-C scale was positively correlated with the IPSG scale. CONCLUSIONS: US is important for evaluating knee lesions in patients with SHA and may potentially replace MRI.

The Correlation Between the HEAD-US-C Score and HJHS in Hemophilic Arthropathy of the Knee.

OBJECTIVES: We aimed to discuss the correlation between the Hemophilia Early Detection Ultrasound in China (HEAD-US-C) score and the Hemophilia Joint Health Score version 2.1 (HJHS 2.1) of the knee joint in patients with hemophilia. METHODS: We included 70 male patients with hemophilia admitted to The Second Hospital of Shanxi Medical University; the patients' bilateral knee joints were evaluated using the HEAD-US-C score and HJHS. We analyzed factors influencing hemophilia arthropathy of the knee and examined the correlation between the HEAD-US-C score and HJHS. RESULTS: The joint injury severity was positively correlated with age and the number of bleeds (P < .001). Further, the HEAD-US-C score and HJHS differed according to the severity (both P < .001), but not type (P = .163 and P = .283, respectively), of hemophilia. There was a significant correlation between the HEAD-US-C score and HJHS (P < .001). CONCLUSIONS: Overall, all joint lesions observed on ultrasound corresponded to clinical joint functional abnormalities. Therefore, the HEAD-US-C is important for hemophilic arthropathy evaluation and is useful in explaining abnormal joint function.

Fuzzy Multiple Hidden Layer Recurrent Neural Control of Nonlinear System Using Terminal Sliding-Mode Controller.

This study designs a fuzzy double hidden layer recurrent neural network (FDHLRNN) controller for a class of nonlinear systems using a terminal sliding-mode control (TSMC). The proposed FDHLRNN is a fully regulated network, which can be simply considered as a combination of a fuzzy neural network (FNN) and a radial basis function neural network (RBF NN) to improve the accuracy of a nonlinear approximation, so it has the advantages of these two neural networks. The main advantage of the proposed new FDHLRNN is that the output values of the FNN and DHLRNN are considered at the same time, and the outer layer feedback is added to increase the dynamic approximation ability. FDHLRNN was designed to approximate the nonlinear sliding-mode equivalent control term to reduce the switching gain. To ensure the best approximation capability and control performance, the proposed FDHLRNN using TSMC is applied for the second-order nonlinear model. Two simulation examples are implemented to verify that the proposed FDHLRNN has faster convergence speed and the FDHLRNN with TSMC has good dynamic property and robustness, and a hardware experimental study with an active power filter proves the feasibility of the method.

Modelling, Simulation and Dynamic Sliding Mode Control of a MEMS Gyroscope.

An adaptive dynamic sliding mode control via a backstepping approach for a microelectro mechanical system (MEMS) vibratory z-axis gyroscope is presented in this paper. The time derivative of the control input of the dynamic sliding mode controller (DSMC) is treated as a new control variable for the augmented system which is composed of the original system and the integrator. This DSMC can transfer discontinuous terms to the first-order derivative of the control input, and effectively reduce the chattering. An adaptive dynamic sliding mode controller with the method of backstepping is derived to real-time estimate the angular velocity and the damping and stiffness coefficients and asymptotical stability of the designed systems can be guaranteed. Simulation examples are investigated to demonstrate the satisfactory performance of the proposed adaptive backstepping sliding mode control.

Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller.

This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation.

Adaptive Backstepping Design of a Microgyroscope.

This paper presents a novel algorithm for the design and analysis of an adaptive backstepping controller (ABC) for a microgyroscope. Firstly, Lagrange⁻Maxwell electromechanical equations are established to derive the dynamic model of a z-axis microgyroscope. Secondly, a nonlinear controller as a backstepping design approach is introduced and deployed in order to drive the trajectory tracking errors to converge to zero with asymptotic stability. Meanwhile, an adaptive estimator is developed and implemented with the backstepping controller to update the value of the parameter estimates in the Lyapunov framework in real-time. In addition, the unknown system parameters including the angular velocity may be estimated online if the persistent excitation (PE) requirement is met. A robust compensator is incorporated in the adaptive backstepping algorithm to suppress the parameter variations and external disturbances. Finally, simulation studies are conducted to prove the validity of the proposed ABC scheme with guaranteed asymptotic stability and excellent tracking performance, as well as consistent parameter estimates in the presence of model uncertainties and disturbances.