Abstract:This article proposes an application of a discrete-time adaptive backstepping control strategy for a hydraulic process pumping station. The proposed solution leads to improved control system performances in terms of pressure and flow tracking in transient and standstill operation and improvement of pressure response time. The proposed design methodology is based on accurate model for pumping station, which is developed in previous works using fuzzy-C means algorithm. The control law design is based on discrete… Show more
“…Theorem 1. Consider the closed-loop system consisting of plants (10), (12), and (13) with Assumptions 1 and 2, controllers (21), (32), and (38), and adaptive laws (22), (33), and (39). Then, the tracking errors e 1 , e 2 , and e 3 are bounded.…”
Section: Design the Following Lyapunov Function Candidatesmentioning
confidence: 99%
“…Motivated by the previous results, a novel adaptive neural controller is presented based on back-stepping scheme [19][20][21][22][23] for the longitudinal dynamics of FAHVs to provide robust tracking of velocity and altitude commands. For the altitude subsystem which is decomposed into two newly functional systems, namely the altitude-flight-path-angle (h-g) subsystem and the pitch-angle-pitch-rate (u-Q) subsystem.…”
Control system is significant for making flight safety. In this study, a novel adaptive neural back-stepping controller is exploited for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle. A combined neural network approach and back-stepping scheme is utilized for developing an output-feedback controller that provides robust tracking of the velocity and altitude commands. For each subsystem, only one neural network is employed to approximate the lumped system uncertainty by updating its weight vector adaptively while the problem of possible control singularity is eliminated. The uniformly ultimately boundedness is guaranteed for the closed-loop control system by means of Lyapunov stability theory. The main contribution is that the design complexity is reduced and less neural networks are required. Finally, simulation results illustrate that the proposed control strategy achieves satisfying tracking performance in spite of flexible effects and system uncertainties.
“…Theorem 1. Consider the closed-loop system consisting of plants (10), (12), and (13) with Assumptions 1 and 2, controllers (21), (32), and (38), and adaptive laws (22), (33), and (39). Then, the tracking errors e 1 , e 2 , and e 3 are bounded.…”
Section: Design the Following Lyapunov Function Candidatesmentioning
confidence: 99%
“…Motivated by the previous results, a novel adaptive neural controller is presented based on back-stepping scheme [19][20][21][22][23] for the longitudinal dynamics of FAHVs to provide robust tracking of velocity and altitude commands. For the altitude subsystem which is decomposed into two newly functional systems, namely the altitude-flight-path-angle (h-g) subsystem and the pitch-angle-pitch-rate (u-Q) subsystem.…”
Control system is significant for making flight safety. In this study, a novel adaptive neural back-stepping controller is exploited for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle. A combined neural network approach and back-stepping scheme is utilized for developing an output-feedback controller that provides robust tracking of the velocity and altitude commands. For each subsystem, only one neural network is employed to approximate the lumped system uncertainty by updating its weight vector adaptively while the problem of possible control singularity is eliminated. The uniformly ultimately boundedness is guaranteed for the closed-loop control system by means of Lyapunov stability theory. The main contribution is that the design complexity is reduced and less neural networks are required. Finally, simulation results illustrate that the proposed control strategy achieves satisfying tracking performance in spite of flexible effects and system uncertainties.
“…Recently, several studies have been devoted to development of non-linear control techniques for induction motor. These techniques include: control based on the technique of input-output linearization [6], sliding mode control [7], [8], backstepping control [9]- [13]. The major drawback of these control techniques is their sensitivity to parametric variations, in particularly, the rotor resistance (Rr) which can change with the temperature [14].…”
This paper presents the indirect field vector control of induction motor (IM) which is controlled by an adaptive Proportional-Integral (PI) speed controller. The proposed solution can overcome the rotor resistance variation, which degrades the performance of speed control. To solve this drawback, an adaptive PI controller is designed with gains adaptation based on fuzzy logic in order to improve the performances of IM with respect to parameters variations, particularly the rotor resistance (Rr). The proposed control algorithm is validated by simulation tests. The obtained results show the robustness towards the load torque disturbances and rotor resistance variation of the adaptive Proportional-Integral fuzzy logic control with respect to classical PI control, and adaptive control based on rotor resistance observer.
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