Admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system for synchronous reluctance motor driving continuously variable transmission system

Abstract

To cut down influence of nonlinear time-varying uncertainty action in a synchronous reluctance motor driving continuously variable transmission system, an admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system is posed for improving control performance. The admixed recurrent Gegenbauer polynomials neural network with mended particle swarm optimization control system involves an observer control, a recurrent Gegenbauer polynomial neural network control and a remunerated control. The weights of recurrent Gegenbauer polynomials neural network controller are regulated by using the adaptive law and the gradient descent technology. The remunerated control with a reckoned law is derived and computed by means of the Lyapunov stability theorem so as to pledge stability of the control system. Likewise, to speedup convergence of weights in the recurrent Gegenbauer polynomial neural network, the mended particle swarm optimization algorithm is used for regulating two kinds of learning rates. At last, three kinds of experimental results are demonstrated to confirm the usefulness of the put forward control system with comparative studies.

Neural network parameter optimization for model predictive direct speed control of PMSM

“…Optimizing the weighting parameters for MPDSC under different work conditions is a key challenge in achieving superior control performance. Particle Swarm Optimization (PSO) is a widely-used optimization algorithm known for its successful application in parameter optimization for motor control systems [6][7][8] . Inspired by the collective behavior of bird flocking or fish schooling, PSO is a population-based stochastic optimization technique that iteratively updates particle positions and velocities to search for the optimal solution within the solution space.…”

“…Through simulations, the proposed MPDSC's effectiveness is validated. However, existing MPDSC research often neglects the detailed investigation of the weighting parameters' influence on PMSM performance under varying conditions, such as speed reference and load torque [5][6][7] . The weighting parameters play a crucial role in balancing speed tracking accuracy, control effort, and response to load disturbances.…”

Disturbance-Observer-Based Sliding-Mode Speed Control for Synchronous Reluctance Motor Drives via Generalized Super-Twisting Algorithm

“…To date, various nonlinear control theories have been proposed to stabilize the nonlinear system, either asymptotically or in finite time, under time-varying disturbances. Consequently, various nonlinear speed controllers based on these control theories have been developed for the vector-controlled SynRM drive system, including adaptive speed controllers [30][31][32], backstepping speed controllers [33,34], predictive speed controllers [29,35,36], neural network speed controllers [37,38], and sliding-mode speed controllers [39][40][41][42][43][44][45].…”

Robust control strategy for networked vehicle system with fake data injection attack

“…So as to suppressing system disturbances, many studies have done many investigations in this area. [12][13][14] Li et al 15 studied the external disturbances and networkinduced disturbances of vehicles' follow-up control and proposed a discrete-time sliding-mode control arithmetic which can be adaptive. For the overhead crane systems, Wu et al 16 proposed a sliding-mode controller and an observer of nonlinear perturbance for the adjustment and perturbation reckoning.…”