Adaptive neural network output feedback control of incommensurate fractional-order PMSMs with input saturation via comma
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ORIGINAL ARTICLE
Adaptive neural network output feedback control of incommensurate fractional-order PMSMs with input saturation via command filtering and state observer Senkui Lu1 • Xingcheng Wang1 Received: 19 February 2020 / Accepted: 3 September 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In this paper, an adaptive neural network (NN) output feedback control is investigated for incommensurate fractional-order permanent magnet synchronous motors under the condition of input saturation. First, a NN state observer is presented to obtain the ‘virtual estimate’ of angle speed, where the unknown function is approximated by the NN. Then, in order to solve the input saturation problem, an auxiliary system is developed under fractional-order framework. Next, the command filtered technology with an error compensation mechanism is used to handle the ‘explosion of complexity’ in backstepping and remove the filtering errors. In addition, the frequency distributed model is utilized such that the Lyapunov theory is available in the backstepping design and the system stability is demonstrated. Finally, numerical simulations confirm the availability of the proposed design. Keywords Permanent magnet synchronous motors (PMSMs) Incommensurate fractional-order Command filtering Observer Input saturation
1 Introduction Permanent magnet synchronous motors (PMSMs) have received great attention in the industry application owing to its high efficiency and simple construction [1, 2]. For applications such as industrial processes, robotics, elevators and electric vehicles, a high control performance for the PMSM drives is required [3]. To meet the high-performance requirements for PMSMs systems, a large number of control methodologies have been investigated and references therein [4–10]. As one of the most efficient control methods for nonlinear systems, backstepping technique has been widely adopted to construct controllers, especially for those systems with unmatched conditions [11, 12]. However, the
& Xingcheng Wang [email protected] Senkui Lu [email protected] 1
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
backstepping method suffers from the fact that the explosion of complexity problem is ignored. By employing a first-order filter at the output of controller, dynamic surface control (DSC) [13, 14] shows a high potential to work out the explosion of complexity problem. Many researchers have studied the NNs theory and made significant achievements [15–18]. Furthermore, adaptive NNs techniques combined with backstepping offer resultful methods to cope with the issue of unknown parameters. In [19–21], the DSC method associated with NNs was put forward for nonlinear uncertain systems, in which the NNs are introduced to estimate the uncertain terms. However, the DSCbased adaptive NN approach does not consider the impact of unavoidable errors brought by the first-order filters. Fortunately, the command filte
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