Adaptive Backstepping Design of a Microgyroscope

A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.

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.

Wearable Inertial Sensors for Range of Motion Assessment

This paper presents the design and development of wearable inertial sensors (WIS) for real-time simultaneous triplanar motion capture of the upper extremity (UE). The sensors simultaneously capture in the frontal, sagittal, and horizontal planes UE range of motion (ROM), which is critical to assess an individual's movement limitations and determine appropriate rehabilitative treatments. Off-the-shelf sensors and microcontrollers are used to develop the WIS system, which wirelessly streams real-time joint orientation for UE ROM measurement. Key developments include: 1) two novel approaches, using earth's gravity (EG approach) and magnetic field (EGM approach) as references, to correct misalignments in the orientation between the sensor and its housing to minimize measurement errors; 2) implementation of the joint coordinate system (JCS)-based method for triplanar ROM measurements for clinical use; and 3) an in-situ guided mounting technique for accurate sensor placement and alignment on human body. The results 1) compare computational time between two orientation misalignment correction approaches (EG approach = 325.05 μs and EGM approach = 92.05μs); 2) demonstrate the accuracy and repeatability of measurements from the WIS system (percent deviation of measured angle from applied angle is less than ±6.5% and percent coefficient of variation is less than 11%, indicating acceptable accuracy and repeatability, respectively); and 3) demonstrate the feasibility of using the WIS system within the JCS framework for providing anatomically-correct simultaneous triplanar ROM measurements of shoulder, elbow, and forearm movements during several upper limb exercises.

Adaptive Backstepping Fractional Fuzzy Sliding Mode Control of Active Power Filter

An adaptive fractional-order fuzzy control method for a three-phase active power filter (APF) using a backstepping and sliding mode controller is developed for the purpose of compensating harmonic current and stabilizing the DC voltage quickly. The dynamic model of APF is changed to an analogical cascade system for the convenience of the backstepping strategy. Then a fractional-order sliding mode surface is designed and a fuzzy controller is proposed to approximate the unknown term in the controller, where parameters can be adjusted online. The simulation experiments are conducted and investigated using MATLAB/SIMULINK software package to verify the advantage of the proposed controller. Furthermore, the comparison study between the fractional-order controller and integer-order one is also conducted in order to demonstrate the better performance of the proposed controller in total harmonic distortion (THD), a significant index to evaluate the current quality in the smart grid.

Adaptive fractional fuzzy sliding mode control of microgyroscope based on backstepping design

In this paper, a robust sliding mode control (SMC) based on backstepping technique is studied for a microgyroscope in the presence of unknown model uncertainties and external disturbances using adaptive fuzzy compensator and fractional calculus. At first, the dynamic of microgyroscope is transformed into analogically cascade system to guarantee the application of backstepping design. Then a novel fractional differential sliding surface is proposed which integrates the capacities of the fractional calculus and SMC. In order to reduce the chattering in SMC, a fuzzy logical system is utilized to approximate the external disturbances. In addition, fractional order adaptive laws are derived to estimate the damping and stiffness coefficients and angular velocity online based on Lyapunov stability theory which also guarantees the stability of the closed loop system. Finally, simulation results signify the robustness and effectiveness of the proposed control schemes and the comparison of root mean square error under different fractional orders and integer order are given to demonstrate the better performance of proposed controller.

Adaptive Intelligent Sliding Mode Control of a Photovoltaic Grid-Connected Inverter

Adaptive intelligent sliding mode control methods are developed for a single-phase photovoltaic (PV) grid-connected transformerless system with a boost chopper and a DC-AC inverter. A maximum power point tracking (MPPT) method is implemented in the boost part in order to extract the maximum power from the PV array. A global fast terminal sliding control (GFTSMC) strategy is developed for an H-bridge inverter to make the tracking error between a grid reference voltage and the output voltage of the inverter converge to zero in a finite time. A fuzzy-neural-network (FNN) is used to estimate the system uncertainties. Intelligent methods, such as an adaptive fuzzy integral sliding controller and a fuzzy approximator, are employed to control the DC-AC inverter and approach the upper bound of the system nonlinearities, achieving reliable grid-connection, small voltage tracking error, and strong robustness to environmental variations. Simulation with a grid-connected PV inverter model is implemented to validate the effectiveness of the proposed methods.