Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer

This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.

Adaptive interval type-2 fuzzy recurrent RBFNN control design using ellipsoidal membership functions with application to MEMS gyroscope

In this article, a hybrid control approach is provided to control the micro-electro-mechanical system (MEMS) triaxial gyroscope as a multi-input multi-output (MIMO) system. Control design includes a fast non-singular terminal sliding mode control (FNTSMC) as a main part of the proposed hybrid control method and since the MEMS gyroscope performance is affected by parameter variations, quadrature errors, and external disturbances in the core of the main controller, adaptive interval type-2 recurrent fuzzy radial basis function neural network (IT2-RFRBF-NN) is employed to estimate the lumped uncertainties. The proposed hybrid approach has four main attributes: (1) it lies in the category of model-free control structures; (2) There is no negative power involved. Hence, the suggested method does not have the singularity problem; (3) to enhance the capability of the proposed method in the present of noise the ellipsoidal membership functions are employed to design adaptive IT2-RFRBF-NN; (4) the Fourier series expansion as a function approximation technique is efficiently used to online estimate the discontinuous component by establishing a soft switching in the proposed controller. With the help of the Lyapunov stability theory, guaranteeing the closed-loop control system stability by the suggested control design is confirmed. The findings of the simulations and comparison with other approaches confirm the superiority of the suggested hybrid approach.

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.

A review on type-2 fuzzy neural networks for system identification

In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

Hybrid intelligent adaptive controller for tiltrotor UAV

Purpose

In this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.

Design/methodology/approach

In the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.

Findings

The proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.

Originality/value

Novel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

Composite Neural Learning-Based Nonsingular Terminal Sliding Mode Control of MEMS Gyroscopes

The efficient driving control of MEMS gyroscopes is an attractive way to improve the precision without hardware redesign. This paper investigates the sliding mode control (SMC) for the dynamics of MEMS gyroscopes using neural networks (NNs). Considering the existence of the dynamics uncertainty, the composite neural learning is constructed to obtain higher tracking precision using the serial-parallel estimation model (SPEM). Furthermore, the nonsingular terminal SMC (NTSMC) is proposed to achieve finite-time convergence. To obtain the prescribed performance, a time-varying barrier Lyapunov function (BLF) is introduced to the control scheme. Through simulation tests, it is observed that under the BLF-based NTSMC with composite learning design, the tracking precision of MEMS gyroscopes is highly improved.

A solution for enhancement of transient performance in nonlinear adaptive control: Optimal adaptive reset based on barrier Lyapunov function

In this paper, an adaptive controller based on barrier Lyapunov function combined with an optimal reset rule is devised in order to improve the transient performance of nonlinear adaptive control. A novel reset rule is designed such that the estimated parameters of the adaptive controller jump to the optimal values in a way that optimizes a cost function representing the transient performance index. It is proved that asymptotic tracking is achieved and the output remains in a desired bound by ensuring boundedness of the barrier Lyapunov function. Besides, the convergence rate is increased by resetting the estimated parameters to optimal values. A regularly referred example is simulated to demonstrate the effectiveness of the proposed method and the results are compared with the existing investigations.