Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

Abstract

This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with timevarying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

Speech Enhancement Control Design Algorithm for Dual‐Microphone Systems Using β‐NMF in a Complex Environment

“…where 1 I×K is a I × K matrix of ones. As is shown by Formulas (14), (15), and (16) derived above, it can reduce to singlechannel counterparts (4) and (5) if only one microphone is used, and the interchannel matrix H is a unit matrix.…”

“…Firstly, two noisy speeches y 1 t and y 2 t are used as input signals of this stage after delay compensation, and then we obtain the magnitude spectra of noise by applying STFT, namely, Y 1 and Y 2 . Next, they are factorized via the extension of NMF with the fixed joint dictionary matrix T = T S T N , which is just derived from the training stage via using the update rules given in (15) and (16). Accordingly, the magnitude spectra can be approximately decomposed into an interchannel matrix H = H S H N and a coefficient matrixV…”

“…With the unceasing development of human-computer interaction recently, higher requirements for speech recognition and computer vision are put forward in a complex environment. In [14][15][16]; the control scheme for improving the convergence speed is developed to optimize system performance.…”

Q-learning optimized diagonal recurrent neural network control strategy for brushless direct current motors

“…The algorithms based on neural network have shown promising results. [17][18][19][20][21] The PID gain updating algorithm based on neural network has been successfully applied to the control of servo motor, 21 computerized numerical control machine tool, 18 etc. In Xia et al, 22 a single neuron PI controller is designed for the control system of BLDCM.…”

Adaptive Finite-Time NN Control for 3-DOF Active Suspension Systems With Displacement Constraints

“…In fact, discussing the displacements of seat and active suspensions is viewed as the state constraints for 3-DOF ASSs. A lot of works have been carried out on state constraints of various nonlinear systems, for instance, see [38]- [41]. In order to ensure the safety of the hydraulic servo-system, the authors showed a time-varying state constraints NNs control method, and this method guaranteed that the inertial load's displacement and velocity didn't exceed their limits in [40].…”