Yumei Ma

Reduced-order observer-based adaptive fuzzy tracking control for chaotic permanent magnet synchronous motors

This paper studies an adaptive fuzzy control method combined with reduced-order observer technology for the position tracking control of chaotic permanent magnet synchronous motor (PMSM) drive system. Fuzzy logic systems (FLSs) are introduced to solve the problem of nonlinear and unknown functions appeared in the PMSM drive system, reduced-order observer is used to calculate its angle speed. Meanwhile, adaptive backstepping mechanism is applied for the design procedure of controllers. The control technique developed in this paper can ensure that the tracking error falls into a small neighborhood of origin. Compared with the existing results, the proposed algorithm can solve the explosion of complexity issue and it does not require measuring the speed signal of motors and the number of adaptive parameters has been reduced to only one. Simulation results show that the chaos of PMSM can be successfully suppressed by the proposed method and the system can track the reference signals very well.

Adaptive Fuzzy Tracking Control for a Permanent Magnet Synchronous Motor via Backstepping Approach

The speed tracking control problem of permanent magnet synchronous motors with parameter uncertainties and load torque disturbance is addressed. Fuzzy logic systems are used to approximate nonlinearities, and an adaptive backstepping technique is employed to construct controllers. The proposed controller guarantees the tracking error convergence to a small neighborhood of the origin and achieves the good tracking performance. Simulation results clearly show that the proposed control scheme can track the position reference signal generated by a reference model successfully under parameter uncertainties and load torque disturbance without singularity and overparameterization.

Robust Adaptive Fuzzy Control of Chaos in the Permanent Magnet Synchronous Motor

An adaptive fuzzy control method is developed to control chaos in the permanent magnet synchronous motor drive system via backstepping. Fuzzy logic systems are used to approximate unknown nonlinearities, and an adaptive backstepping technique is employed to construct controllers. The proposed controller can suppress the chaos of PMSM and track the reference signal successfully. The simulation results illustrate its effectiveness.

Adaptive Neural Consensus Tracking for Nonlinear Multiagent Systems Using Finite-Time Command Filtered Backstepping

This paper is concerned with the finite-time consensus tracking control problems of uncertain nonlinear multiagent systems. A neural network-based distributed adaptive finite-time control scheme is developed, which can guarantee the consensus tracking is achieved in finite time with sufficient accuracy in the presence of unknown mismatched nonlinear dynamics. Such a finite-time feature is achieved by the modified command filtered backstepping technique based on the high-order sliding mode differentiator. Moreover, the proposed control scheme is completely distributed, since the control laws only use the local information. In addition, although mismatched uncertainty nonlinear dynamics are considered, only one parameter needs to be updated for each agent in the control scheme, which will simply the computations and make the proposed scheme more effective for applications. An example is included to verify the presented method.

Barrier Lyapunov function-based adaptive fuzzy control for induction motors with iron losses and full state constraints

Abstract This paper is concerned with the problem of adaptive fuzzy control for induction motors (IMs) with iron losses and full state constraints based on the barrier Lyapunov function method. The state variables are constrained by the inherent properties of the IMs, and the barrier Lyapunov function (BLF) is introduced to guarantee that the full state constraints are not violated. In addition, fuzzy logic systems are utilized to approximate the nonlinearities. It is proved that all the signals of closed-loop system are guaranteed to be bounded and the tracking error converges to the neighborhood of the origin asymptotically. Finally, simulation results show the effectiveness of the proposed scheme.

Adaptive Fuzzy Dynamic Surface Control for AUVs via Backstepping

In this paper, a dynamic surface control (DSC) based adaptive fuzzy backstepping method is proposed for AUV (autonomous underwater vehicle) systems. The DSC is utilized to solve the “explosion of complexity” of traditional backstepping, the fuzzy logic systems(FLSs) are used to approximate unknown nonlinear function of AUV systems and the adaptive backstepping is employed to design controllers, and Matlab is used to conduct the simulation. The proposed control method can achieve position tracking effectively. The simulation results show that the adaptive fuzzy controller can overcome the influences of parameter uncertainties and load disturbance as well as achieve a good control effect on AUV system. This study has lots of practical application value.

Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with Second-Order

The transmission of weak noisy signal byparallel array of dynamical saturating nonlinearities with second-order is studied. Firstly, the numerical results demonstrate that the output SNR can be enhanced by parallel array of dynamical saturating nonlinearities with second-order by tuning the internal noise. Secondly, the SR effects can be optimized by the self-coupling coefficient of the dynamical nonlinearity.Then, the SR effects when the nonGaussian noise acts as the external noise are superior to that with external Gaussian noise.

Fuzzy approximation-based adaptive command filtered control for induction motors

This paper considers the problem of the adaptive speed regulation command filtered control for induction motors drive system with parameter uncertainties based on fuzzy-approximation. Fuzzy logic systems are used to approximate the unknown nonlinear functions. The adaptive command filtered backstepping is employed to construct controllers which can overcome the problem “explosion of complexity” inherent in the traditional backstepping design and guarantee the tracking error can converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the proposed control scheme.

Robust Coupling-Observer-Based Linear Quadratic Regulator for Air-Breathing Hypersonic Vehicles with Flexible Dynamics and Parameter Uncertainties

This paper studies the anti-disturbance control problem for air-breathing hypersonic vehicles (AHVs) with flexible dynamics and parameter uncertainties. A novel anti-disturbance control method is presented, which includes a robust coupling observer (RCO) and a linear quadratic regulator (LQR). The compensator is designed to reject the disturbance generated by rigid-flexible couplings (RFCs). The LQR is presented to track desired trajectories. Finally, simulation results show that the control performance can be improved by using the RCO-based LQR compared with the traditional linear quadratic regulator.

Position Control of Induction Motors via Adaptive Fuzzy Backstepping with Input Saturation

This paper focuses on the problem of position tracking control for field-oriented induction motors with input saturation. In the scheme, an adaptive fuzzy control based on backstepping technique is designed. Fuzzy logic systems are used to approximate the unknown nonlinearities and the adaptive backstepping control is employed to construct controllers. The proposed adaptive fuzzy controllers guarantee the tracking error converge to a small neighborhood of the origin. Compared with the conventional backstepping, the designed fuzzy controllers’ structure is very simple. The simulation results show that the new controller overcomes the influences of the parameter uncertainties and guarantee a tracking performance.