Direct Adaptive Fuzzy Backstepping Control for Stochastic Nonlinear SISO Systems with Unmodeled Dynamics

In this work, we consider the problem of controlling a single‐phase on‐board battery electric vehicle (BEV) charger with vehicle‐to‐grid (V2G) technology. The BEV charger consists of a bidirectional ac‐dc power converter connected to the single‐phase power grid, followed by a bidirectional dc‐dc power converter interfacing an EV battery pack. The main control objectives are fourfold: (i) Unitary Power Factor (UPF) in grid‐side; (ii) tight dc‐bus voltage regulation; (iii) safety battery charge and battery discharge during the grid‐to‐vehicle (G2V) mode and V2G mode, respectively; and (iv) asymptotic stability of the closed loop system. After an accurate system modelling, a nonlinear controller is designed using a backstepping design technique. The point is that the battery inner voltage is not accessible to measurement. Therefore, a nonlinear observer is invoked in order to estimate all non‐measured variables making the solution cheaper and noiseless. It is shown using a formal analysis and numerical simulations, that the proposed output feedback controller (combining a nonlinear controller and a nonlinear observer) meets all control objectives.

Section: State Feedback Controller Designmentioning

confidence: 99%

Nonlinear output feedback control of V2G single‐phase on‐board BEV charger

Rachid

Fadil

Giri

et al. 2019

“…Many tracking control methods have been proposed, such as PID control [1], computed-torque control [2], decentralized control [3], feedback linearization (otherwise known as inverse dynamics control) [4], adaptive control [5], intelligent control (fuzzy control [6], neural-network control [7]), optimal control [8], H ∞ control, [9] iterative learning control [10], model predictive control [11], passive-based control [12], adaptive neural network control [13,14], robust control [15], and sliding-mode control (SMC) [16][17][18][19][20][21][22][23][24][25][26][27]. Among the methods, SMC has attracted significant interest due to its computational simplicity for implementation, high robustness to external disturbances, low sensitivity to parameter variations, and fast dynamic response.…”

Section: Introductionmentioning

confidence: 99%

Full‐Order Sliding‐Mode Control of Rigid Robotic Manipulators

Feng

Zhou

et al. 2018

This paper proposes a full-order sliding-mode control for rigid robotic manipulators. The output signals of the proposed controller are continuous. Therefore, the controller can be directly applied in practice. A time-varying gain is constructed to regulate the gain of the signum function in the sliding-mode control so as to avoid the overestimation of the upper-bounds of the uncertainties in the systems and reduce the waste of the control power. The chattering is attenuated by using a novel full-order sliding manifold and establishing a novel ideal sliding motion. The proposed method is robust to the load disturbance and unmodeled parameters, especially to the unknown portion in the control matrix. Simulation results validate the proposed methods.

Section: Introductionmentioning

confidence: 99%

“…Simulation studies demonstrate that the control accuracy is improved obviously compared with the PID method. The fuzzy control method is an efficient control strategy and the stability analysis of fuzzy control systems has achieved some results [13][14][15][16][17]. Traore et al [6] proposed a fuzzy control method to track the DO concentration in a sequencing batch reactor pilot plant.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Variable Direct Self‐Organizing Fuzzy Neural Network Control for Wastewater Treatment Process

Zhang

Qiao

2018

A multi‐variable direct self‐organizing fuzzy neural network control (M‐DSNNC) method is proposed for the multi‐variable control of the wastewater treatment process (WWTP). In this paper, the proposed control system is an essential multi‐variable control method for the WWTP. No exact plant model is required, which avoids the difficulty of establishing the mathematics model of WWTP. The M‐DSNNC system is comprised of a fuzzy neural network controller and a compensation controller. The fuzzy neural network is used for approximating the ideal control law under a general nonlinear system. Moreover, the neural network is designed in a self‐organizing mode to adapt the uncertainty environment. Simulation results, based on the international benchmark simulation model No.1 (BSM1), demonstrate that the control accuracy is improved under the proposed M‐DSNNC method, and the controller has a much stronger decoupling ability.