Robust Sugeno Type Adaptive Fuzzy Neural Network Backstepping Control for Two-Axis Motion Control System
نویسندگان
چکیده
A robust Sugeno type adaptive fuzzy neural network (RSAFNN) backstepping control for a two-axis motion control system is proposed in this paper. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The single-axis motion dynamics with the introduction of a lumped uncertainty, which includes parameter variations, external disturbances, cross coupled interference between the two PMLSMs and fiction force, is derived first. Then, a backstepping control approach is proposed to compensate the lumped uncertainty occurred in the two-axis motion control system. Moreover, to improve the control performance in the tracking of the reference contours, a RSAFNN backstepping control is proposed where a Sugeno type adaptive fuzzy neural networks (SAFNN) is employed to estimate the lumped uncertainty directly. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer.
منابع مشابه
Robust Control of Encoderless Synchronous Reluctance Motor Drives Based on Adaptive Backstepping and Input-Output Feedback Linearization Techniques
In this paper, the design and implementation of adaptive speed controller for a sensorless synchronous reluctance motor (SynRM) drive system is proposed. A combination of well-known adaptive input-output feedback linearization (AIOFL) and adaptive backstepping (ABS) techniques are used for speed tracking control of SynRM. The AIOFL controller is capable of estimating motor two-axis inductances ...
متن کاملRobust Backstepping Control of Induction Motor Drives Using Artificial Neural Networks and Sliding Mode Flux Observers
In this paper, using the three-phase induction motor fifth order model in a stationary twoaxis reference frame with stator current and rotor flux as state variables, a conventional backsteppingcontroller is first designed for speed and rotor flux control of an induction motor drive. Then in orderto make the control system stable and robust against all electromechanical parameter uncertainties a...
متن کاملHigh-Precision Intelligent Adaptive Backstepping H∞ Control for PMSM Servo Drive Using Dynamic Recurrent Fuzzy-Wavelet- Neural-Network
This paper proposes a high-precision intelligent adaptive backstepping control system (HPIABCS) for the position control of permanent-magnet synchronous motor (PMSM) servo drive. The HPIABCS incorporates an ideal backstepping controller, a dynamic recurrent-fuzzy-wavelet-neural-network (DRFWNN) uncertainty observer and a robust H∞ controller. First, a backstepping position controller is designe...
متن کاملAdaptive fuzzy sliding mode and indirect radial-basis-function neural network controller for trajectory tracking control of a car-like robot
The ever-growing use of various vehicles for transportation, on the one hand, and the statistics ofsoaring road accidents resulting from human error, on the other hand, reminds us of the necessity toconduct more extensive research on the design, manufacturing and control of driver-less intelligentvehicles. For the automatic control of an autonomous vehicle, we need its dynamic...
متن کاملAdaptive RBF network control for robot manipulators
TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...
متن کامل