Zeroing neural dynamics (ZND) can be seen as an effective controller to solve various challenging scientific and engineering problems. Computing Lyapunov equation is a kind of important issue in nonlinear systems for stability analysis in control. This paper presents a systematic and constructive procedure on using ZND to design control laws based on the efficient solution of dynamic Lyapunov equation. We particularly address three important aspects in the design: 1. the global stability of ZND, to guarantee the effectiveness of the solution; 2. the robustness against additive noises, to ensure the capability of ZND for using in harsh environments; 3. the finite-time convergence of ZND, to endow ZND for real-time solution of dynamical problems. To do so, a novel formula is first designed in a unified manner of ZND. Differing from the conventional formula appeared in ZND, the proposed formula simultaneously has finite-time convergence and noise robustness property. According to this novel formula, a novel control law (termed nonlinear neural dynamics, NND) is established to compute dynamic Lyapunov equation in the presence of various additive noises. Both theoretical and simulative results ensure the finite-time convergence and noise robustness property of the NND model for computing dynamic Lyapunov equation in the front of various additive noises. As compared to the conventional ZND model for computing dynamic Lyapunov, the superior property of the NND model is further demonstrated.

Parallel robots are usually required to perform real-time tracking control tasks in the presence of external disturbances in the complex environment. Conventional zeroing neural-dynamics (ZNDs) provide an alternative solution for the real-time tracking control of parallel robots due to its capacity of parallel processing and nonlinearity handling. However, it is still a challenge for the solution in a unified framework of the ZND to deal with the external disturbances, and simultaneously possess a finite-time convergence property. In this paper, a novel ZND model by exploring the super-twisting (ST) algorithm, named ST-ZND model, is proposed. The theoretical analyses on the global stability, finite-time convergence, as well as the robustness against the external disturbances are rigorously presented. Finally, the effectiveness and superiority of the ST-ZND model for the real-time tracking control of parallel robots are demonstrated by two illustrative examples, comparisons, and convergence tests.

A microgrid with virtual inertia using master-slave control is proposed in this study to overcome the drawbacks of traditional inverter-based distributed generators for lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit respectively in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.

The synchronous reluctance motor (SynRM) is a high efficiency and low cost motor with rugged structure. However, the nonlinear and time-varying control characteristics of the SynRM with high torque ripple confine the high-performance applications of this motor. Therefore, an intelligent backstepping control using recurrent feature selection fuzzy neural network (IBSCRFSFNN) is proposed in this study to construct a high-performance SynRM position servo drive system. Firstly, the dynamic model of a vector control SynRM position servo drive is described. Secondly, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the SynRM position servo drive system is unavailable to obtain in advance, it is very difficult to design an effective BSC in practical applications. Moreover, the sign function in BSC will cause undesired chattering phenomenon which will excite unknown dynamics and wear the ball bearing of the SynRM. To alleviate the existed difficulties in the BSC, the recurrent feature selection fuzzy neural network (RFSFNN) is proposed in this study to approximate an idea BSC. In addition, to compensate the possible approximated error of the RFSFNN, an improved adaptive compensator is augmented. The online learning algorithms of the RFSFNN are derived by using the Lyapunov stability method to assure asymptotical stability. Finally, the proposed control system is implemented in a 32-bit floating-point digital signal processor (DSP). The effectiveness and robustness of the proposed intelligent backstepping control system are verified by some experimental results.

The design and analysis of a novel current angle-based adaptive backstepping (ABS) speed control system for a synchronous reluctance motor (SynRM) drive system is presented in this study. First, a proportional-integral (PI) control system with field-oriented control (FOC) is described. Owing to the unmodeled dynamics and magnetic saturation effects of the SynRM, currently there is no predominant way to design the command of d-axis current for the SynRM. Therefore, an ABS based on current angle control (ABS-CAC) system is designed for the speed tracking of SynRM. The ABS speed tracking control is proposed to generate the stator current command, and a lookup table (LUT) of the results of maximum torque per ampere (MTPA) analysis by using the finite element analysis (FEA) method is proposed to provide the current angle commands. Moreover, to improve the transient dynamic response of SynRM under MTPA operating conditions, an intelligent speed transient control (ISTC) system using a recurrent Hermite fuzzy neural network (RHFNN) is developed to generate the compensated current angle command. The proposed intelligent ABS-CAC is implemented in a 32-bit floating-point digital signal processor (DSP) TMS320F28075. Finally, some experimental results are provided to demonstrate the robustness and effectiveness of the proposed control system.

To develop a high-performance synchronous reluctance motor (SynRM) drive system, a novel maximum power factor control (MPFC) using a current angle controller with stator resistance and stator flux estimators is proposed. Firstly, a traditional maximum power factor control (TMPFC) system using a saliency ratio of SynRM to generate a fixed current angle command is described. Since the saliency ratio requires offline pre-preparation and can't be adjusted automatically, it is difficult to improve the performance of MPFC in different operating regions because of the increasing of manufacturing cost and time-consuming. Therefore, an intelligent-maximum power factor searching control (MPFSC) using a recurrent Chebyshev fuzzy neural network (RCFNN) current angle controller is developed. To search the online optimal power factor (PF) points of the SynRM under different operating conditions, the RCFNN current angle controller is designed to produce compensated current angle commands. Moreover, a proportional-integral speed controller is adopted to generate the stator current magnitude command, and the proposed intelligent-MPFSC is employed to generate the current angle command. Furthermore, the proposed intelligent-MPFSC system is implemented in a digital signal processor. Finally, the current angle commands of the optimal PF can be effectively obtained online at different speed operating commands with varied load torque.