Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks

Abstract

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzyneural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

Coordination control of greenhouse environmental factors

“…In their work, a Takagi-Sugeno (T-S) fuzzy model was constructed from a simplified nonlinear dynamic model of the greenhouse climate. The fuzzy control was able to accomplish control actions without any precise mathematical model, but it had several shortcomings such as low control accuracy and the hardness in determining and adjusting fuzzy rules [14,15] .…”

Optimal Control of Nonlinear Inverted Pendulum System Using PID Controller and LQR: Performance Analysis Without and With Disturbance Input

“…The optimal control design method of linear quadratic Gaussian (LQG), which is a combination of a linear quadratic estimator (LQE) (i.e., Kalman filter) and a linear quadratic regulator (LQR), has been used for optimal control of pneumatic Stewart-Gough platform in [25]. In recent trends even the various advance control approaches [8][9][10][11][12][13][14][15][16][17][18][19][20]24] are developing and being tried for many dynamical systems control, the simplicity of control algorithms along with the fulfillment of control objectives is further desired. The neural network based control design requires a large data set collected from experiments for networks training and testing; the fuzzy control requires framing of rules, which becomes complex for higher order systems; and the evolutionary computational techniques are slow in computation.…”

Adaptive neural network tracking control of robot manipulators with prescribed performance

“…The backstepping design procedure breaks down complex systems into smaller subsystems, and designs partial Lyapunov functions and fictitious controllers for these subsystems, and integrates these individual controllers into the actual controller by back stepping. The backstepping-based adaptive control technique, which is mainly used to deal with the robust control of the non-linear systems with parametric uncertainties and the non-linear functions assumed to be known, has become one of the most popular design methods for a large class of non-linear systems [15]. This paper is organized as follows.…”