Disturbance Decoupling Control for Flexible Air‐Breathing Hypersonic Vehicles with Mismatched Condition

In this paper, the localization problem of the mobile robot (MR) with an energy harvesting sensor (EHS) is studied. A specific situation is regarded, which is that the MR equips with an EHS who owns the capacity for absorbing the energy from the surroundings. When the sensor energy is sufficient, the sensor can send its measurement to the filter. Otherwise, the sensor energy‐induced measurement missing phenomenon will occur. For the sake of acquiring the measurement missing rate, the relationship between the amount of the sensor energy and its probability distribution is derived recursively at each time instant. The purpose of this paper is to seek out a feasible solution to the addressed MR localization (MRL) problem (MRLP) by devising the desired time‐varying filter. Firstly, in the presence of the sensor energy‐induced measurement missing phenomenon, an upper bound (UB) for the filtering error (FE) covariance (FEC) is acquired recursively. Then, the acquired UB is minimized by characterizing the desired filter gain adequately. Finally, a simulation is conducted to testify the availability of the proposed MRL algorithm (MRLA).

Section: Discussionmentioning

confidence: 99%

Recursive filtering for mobile robot localization under an energy harvesting sensor

Shen

2021

“…In some flight control schemes for AHVs [1,2], linearized models were employed, and the resulting controllers may become invalid when vehicle states are far from the selected operating points. For the purpose of achieving satisfactory performance at a wider range of operating points, many nonlinear control methodologies have been applied to AHVs, such as model predictive control [3], adaptive dynamic programming [4,5], disturbance-observer-based control [6,7], sliding mode control [8], and backstepping control [9,10]. Particularly, to cope with the uncertainties inherent in AHVs, adaptive control techniques have been incorporated into the design.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fault‐tolerant control for a class of flexible air‐breathing hypersonic vehicles

Huang

Wang

et al. 2023

In this paper, a novel adaptive fault‐tolerant control (FTC) scheme is proposed for a class of flexible air‐breathing hypersonic vehicles with unknown inertial and aerodynamic parameters and even input constraints. The fault model under consideration covers the case that all actuators suffer from unknown time‐varying faults. In the controller design and stability analysis, we introduce new Lyapunov functions, some differentiable auxiliary functions, a bound estimation approach, and a Nussbaum function, which help us successfully circumvent the obstacle caused by the faults, input constraints, and flexible modes. In addition to higher reliability, the proposed scheme is able to ensure that all closed‐loop signals are globally uniformly bounded and to steer the tracking errors of altitude and velocity into predefined arbitrarily small residual sets. As a result, the tracking accuracy can be designated in advance. Simulation results illustrate the effectiveness of the proposed scheme.

“…HFV is a kind of vehicle with big flight envelope and acutely varying flight parameters, so uncertain parameters and unmodeled dynamics are inevitable. The robust control of HFV is a challenging task, and lots of results are available, such as robust control for uncertain HFV [12], disturbance decoupling control [13], and fuzzy and neural network based nonlinear controller for HFV with unmodeled dynamics [14,15]. T-S fuzzy SMC has also been utilized for HFV in Hu et al [10], but the sliding surface is an integral sliding surface.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode learning control for T‐S fuzzy system and an application to hypersonic flight vehicle

Guo

et al. 2022

Sliding mode‐based learning control is presented for T‐S fuzzy system. A T‐S fuzzy model with both uncertainties and unmodeled dynamics is proposed firstly, in which the information of uncertainties and unmodeled dynamics are assumed to be unknown. Then, according to a given reference model, state‐tracking error system is built. Respecting facts, the input matrices of the built T‐S fuzzy model are different from each other. An extended state observer is built for estimating the unknown uncertainties and unmodeled dynamics, and a corresponding sliding surface is proposed. A learning controller is then presented for the closed loop system. Moreover, a numerical simulation result on hypersonic flight vehicles is considered to testify the controller's availability.