The focus of this paper is a fault-estimation issue for switched interconnected nonlinear systems (SINSs) subjected to external disturbances. First, to estimate the fault of all subsystems under external disturbances, a distributed iterative learning observer (DILO) is designed by utilizing related information among subsystems. Then, a novel variable weighted iterative learning (VWIL)-based fault-estimation law is proposed to fast-track the fault signals and weaken the disturbance effects. Subsequently, the convergence conditions are achieved using λ-norm method and mathematical induction. In addition, the gain matrices of the DILO and VWIL law are calculated simultaneously. Finally, simulation results are given to verify the feasibility of the proposed method, which show that accurate fault-estimation results can be obtained after about the 20th iteration.

This paper develops a novel sliding mode control technology and a comprehensive evaluation methodology for speed control of permanent magnet synchronous motor (PMSM). In most of the existing literature, only control precision of the speed corrector is investigated. However, the control strategy should be stable to the noise and disturbance caused by the environment and parameter uncertainties. In order to possessing faster dynamic response, stronger anti-interference ability and high stability of input, a highly stable sliding surface is proposed for siding mode control. Unlike most of reaching laws that requires the variation of designed sliding surfaces, this method takes state vectors in consideration. In addition, a comprehensive evaluation algorithm is presented to obtain reasonable assessment on speed control for synchronous PMSMs in different operating phases (starting, transient, steady-state rating). Finally, the simulation results verify the effectiveness and validity of the proposed sliding mode speed controller.

In this study, a novel intelligent inverter fault diagnosis approach based on a stacked denoising autoencoder–generative adversarial network–long short-term memory (SDAE-GAN-LSTM) under an imbalanced sample is proposed for a three-phase permanent-magnet synchronous motor (PMSM) drive system. The proposed method can address the problem of unbalanced fault data samples and improve the accuracy of fault classification. Concretely speaking, firstly, the stacked denoising autoencoder (SDAE) is pre-trained to obtain the optimum decoder network. Afterward, a new generator of generative adversarial networks (GANs) is designed to generate high-quality samples by migrating the pre-trained optimal decoder network to the hidden layer and output layer of the generator of GANs. Additionally, a new model of long short-term memory (LSTM) based on the second discriminator of the GANs is presented for fault diagnosis. The generator of GANs is cross-trained using the reconstruction error gained by SDAE and the fault diagnosis error obtained by LSTM, resulting in the generation of high-quality samples for fault discrimination. Simulation and experimental results demonstrate the effectiveness of the proposed fault diagnosis approach, and the average fault identification accuracy reaches 98.63%.

P(AM-DMC) (PAD) was synthesized by ultraviolet- (UV-) initiated copolymerization with methacryloxyethyl trimethyl ammonium chloride (DMC) and acrylamide (AM) as the monomers and initiator 2,2-azobis [2-(2-imidazolin-2-yl) propane] dihydrochloride (VA-044) as the photoinitiator. Parameters that affect the molecular weight were reviewed by using the single-factor approach. The results showed that the molecular weight (MW) of PAD could come to 7.88 × 106 Da with the optimum polymerization conditions as follows: monomer concentration of 30%, monomer mass ratio m(AM) : m(DMC) of 3 : 1, initiator concentration of 0.6‰, illumination time of 80 min, solution pH value of 4.5, and incident light intensity of 1000 μW cm⁻². The PAD was represented by several instruments. The results of FTIR and ¹H NMR showed that PAD was indeed polymerized by AM and DMC. The results of TGA showed that PAD was very stable at room temperature while the result of SEM revealed that PAD had a porous structure and rough surface. For PAD used as flocculant in kaolin wastewater treatment, the results confirmed that, at optimal conditions, the turbidity and the floc size d₅₀ could reach to 5.9 NTU and 565.936 μm, respectively, at the optimal conditions (pH = 7.0 and dosage = 2 mg l⁻¹). Kaolin wastewater flocculation test outcome reveals that the PAD with high cationic degree and intrinsic viscosity could boost the charge neutralization and bridging capability. Consequently, the result is an excellent flocculation performance of treating kaolin wastewater.