Design of Reduced‐Order Multiple Observers for Uncertain Systems with Unknown Inputs

SummaryIn this article, we develop a novel method to design a controller for nonlinear systems represented by Takagi‐Sugeno (T‐S) fuzzy model in the presence of unknown dynamics, uncertainties in parameters of nonlinear system and external disturbances. The control law is constituted two segments. The first segment derives from parallel distributed compensation (PDC) procedure, in which each control rule is drawn from the respective rule of T‐S fuzzy model. The second segment stems from fuzzy wavelet neural network (FWNN) estimator which is evoked by the hypothesis of multiresolution analysis (MRA) of wavelet transforms and fuzzy notions, so as to approximate the uncertainties and external disturbances in T‐S fuzzy model accurately. In this regard, the Lyapunov stability theorem is applied to acquire the adaptive learning laws for training and tuning online FWNN parameters such as the dilations, translations, and the weights of networks. Moreover, the asymptotic stability of the closed loop system is guaranteed based on the Lyapunov stability theorem. Furthermore, a development of the proposed method to observer‐based controller design for uncertain nonlinear systems descripted by T‐S fuzzy model is provided. The efficiency and robustness of the proposed method are illustrated by simulation outcomes. It is noteworthy that the proposed controller remarkably handles the uncertainties and external disturbances in T‐S fuzzy model without employing traditional conservative lemma and without considering bounds on uncertainties.

Section: The Observer‐based Controller For Uncertain Disturbed T‐s Fumentioning

confidence: 99%

Observer‐based controller design for uncertain disturbed Takagi‐Sugeno fuzzy systems: A fuzzy wavelet neural network approach

Ebrahimi

Asemani

Safavi

2020

“…5 However, in practice, the ground speed and course from a low-cost GPS module may be degraded, affecting the overall system performance. Another manner is to consider the influence of wind when designing the controller, by estimating it through particular methods, such as the adaptive method 24 and disturbance observer-based control approach 25,26 proposed a new reduced-order multiple observers to estimate the state of uncertain systems with unknown inputs, where some compensation terms were applied to reduce the system's uncertainty and improve the observer's robustness. Moreover, for linear systems with unknown inputs, a full-order observer is proposed to implement the system state reconfiguration in finite time.…”

Section: Introductionmentioning

confidence: 99%

Fixed‐time disturbance observer‐based fixed‐time path following control for small fixed‐wing UAVs under wind disturbances

Cui,

Zhou,

Huang

et al. 2023

SummaryIn this paper, the three‐dimensional path following control problem for small‐scale fixed‐wing unmanned aerial vehicles (UAVs) subject to external wind disturbances is investigated. A fixed‐time disturbance observer is designed to estimate the external wind disturbance. The estimates are then incorporated into the fixed‐time integral terminal sliding mode controller such that a composite control scheme is proposed. Under the proposed control scheme, the UAV system not only guarantees a fixed‐time convergence rate but also possesses stronger disturbance rejection ability and better robustness. Finally, the effectiveness of this strategy is verified by numerical simulations.

“…Further, the flight experiments are conducted without any disturbance considerations. The simulation based work incorporating the reduced and full order observers for various motions of the aircraft is also available in the literature 42,43 . Such an approach can be deployed when the system's performance is severely affected by the unknown inputs.…”

Section: Introductionmentioning

confidence: 99%

Disturbance rejection enhancement using predictive control for the fixed‐wing UAV with multiple ailerons

Sattar

Wang

Ansari

et al. 2023

Summary The performance of small fixed‐wing unmanned aerial vehicles (UAVs) is easily degraded by exogenous disturbances. In an attempt to improve the performance, the structural change is made in conventional small fixed‐wing UAV through segmentation of each aileron control surface into multiples. Detailed system identification experiments are performed on each aileron pair in the wind tunnel to acquire linear dynamic models for the roll attitude of the UAV. These experiments have provided the transfer functions for the aileron control surfaces based on corresponding frequency response. Three distinctive model predictive controllers are designed and deployed in real‐time hardware to achieve the roll attitude control. The roll attitude control experiments are validated in wind tunnel under both normal and turbulent environments. The results show that multiple input and single output control system provides significant improvement in the roll attitude and disturbance rejection performance. The collective actuation of multiple control surfaces improves roll stability by 10.1%$$ 10.1\% $$ to 67.87%$$ 67.87\% $$ when compared to the single aileron pair based conventional control in the presence of turbulent flight conditions.