Backstepping Sliding Mode RBF Network Adaptive Control for Quadrotor UAV

Shen, Weihao; Li, Zhong

doi:10.1109/cac48633.2019.8996354

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Course error e w <15°, cross tracking error l <10m and end up at 0.6 m. Measurement estimation error e m <20m, control forces T u and T r change smoothly. For comparison, adaptive sliding mode control (ASMC [39]) is applied. Relevant performance indices are drawn together in Figure 8 in red dash lines.…”

Section: Simulation Resultsmentioning

confidence: 99%

Berthing Trajectory Tracking of Underactuated Surface Vehicle Based on NMPC and Position Estimation

Wu,

Zhao,

Han

et al. 2024

IEEE Access

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In order to realize waypoint-based automatic berthing alongside or astern, a generation algorithm of dynamic virtual ships is proposed to guide the underactuated surface vehicle (USV). In this paper, a nonlinear model predictive control (NMPC) problem is set up with multiple shooting method, which lifts the system and improves convergency. Measuring error and disturbances are reduced by moving horizon estimation (MHE) where the weights of sensors are auto-tuned according to their variation. Simulations of berthing alongside and astern of ship BAY CLASS illustrates the position error is smaller than 0.6 m and course error is smaller than 0.5°. Compared to adaptive sliding mode control, proposed method has advantage on state smoothness and the control performance meets the requirements of berthing accuracy. INDEX TERMSUSV; trajectory tracking; berthing; motion planning; NMPC; MHE I. INTRODUCTION

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Section: Simulation Resultsmentioning

confidence: 99%