Abstract
In this paper, an observer-based command filtered adaptive neural network tracking control problem is addressed for a fractional-order chaotic permanent magnet synchronous motor (PMSM) with the immeasurable state, parameter uncertainties, and external load disturbance. First, the Chebyshev neural networks are introduced to approximate the nonlinear and unknown functions. Next, a neural network reduced-order state observer is designed to obtain the unmeasured state. Then, the command filtering approach based on the first-order Levant differentiator is developed to solve the “explosion of complexity” issue of backstepping, and a novel fractional-order error compensation mechanism is employed, which can remove the filtering errors in finite time. After that, the continuous frequency distributed model is investigated to design proper Lyapunov function, and it is demonstrated that the proposed control method not only ensures that all signals in the fractional-order PMSM system are bounded but also suppresses chaotic oscillation. Finally, the simulation studies are provided to verify the correctness and effectiveness of the proposed scheme.
Highlights
The applications of fractional calculus have been extensively studied in many areas of research, covering dynamics modeling, materials, dielectric polarization, automatic control and so on [1], [2]
In [9], the difference between integer-order system and fractional-order system is revealed, it is found that the dynamics of the system may be influenced by the systemic order in many aspects, such as bifurcation behavior, chaos pattern, and shape of strange attractor
Motivated by the aforementioned investigations, this paper considers the position tracking control problem for the fractional-order chaotic permanent magnet synchronous motor (PMSM) with immeasurable state, parameter uncertainties and external load disturbance
Summary
The applications of fractional calculus have been extensively studied in many areas of research, covering dynamics modeling, materials, dielectric polarization, automatic control and so on [1], [2]. 3) Integrating state observer, command filter, error compensation mechanism and backstepping technology into position tracking controller in the domain of fractional calculus, which suppresses chaotic motion of the fractional-order PMSM and guarantees the boundedness of all signals. The objective of this paper is to construct an observerbased command filtered adaptive neural network controller for fractional-order chaotic PMSM with immeasurable state, parameter uncertainties and external load disturbance, such that the state x1 tracks the reference signal xd. Assumption 1: The reference signal xd and its fractional derivative Dαxd are known and smooth
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