Robust Hierarchical Adaptive Fuzzy Relative Motion Coordination for Feature Points of Two Rigid Bodies With Input and Output Constraints

Abstract

Jingjing Jiang received the B.E. Degree in Electrical and Electronic Engineering from the University of Birmingham, UK, and the Harbin Institute of Technology, China, in 2010, the M.Sc. degree in Control Systems from Imperial College London, UK, in 2011, and the Ph.D degree from Imperial College London, in 2016. She carried out research as part of the Control and Power Group at Imperial College and joined Loughborough University as a Lecturer in September 2018. Her current research interests include driver assistance control and autonomous vehicle control design, control design of systems with constraints and human-in-the-loop.

Observer-Based Adaptive Fuzzy Finite-Time Attitude Control for Quadrotor UAVs

“…where u = [u 1 ; u 2 ; u 3 ] and J = diag{J x , J y , J z } are the control input and inertial matrix, respectively; d = [d 1 ; d 2 ; d 3 ] denotes the disturbance disturbance factor. Assumption 1 [21]: With the consideration of the structural flexibility and load changes, the inertial matrix J can be described as J = J 0 + J ∆ , where J 0 = J 0,x ; J 0,y ; J 0,z and J ∆ = J ∆,x ; J ∆,y ; J ∆,z denote the ideal part and uncertain part of J , respectively, which is reasonable to assume ∥J ∆ ∥ ≤ J with J > 0 denoting an unknown scalar.…”

“…Assumption 2 [21]: The external disturbance d is unknown but bounded by an unknown constant d > 0, i.e., ∥d∥ ≤ d.…”

“…Remark 2: For Assumptions 1-4, it is necessary to make further discussions: (i) The uncertain inertia matrix is usually caused by the deployment of sensors, the structural flexibility and the change in payloads, Assumption 1 is thus general [21]. (ii) Since the external disturbances like wind gusts, aerodynamic friction and gyroscopic effect are constantly changing and have the limited energy influence, the external disturbances acting on the quadrotor UAV can thus be regarded as unknown time-varying yet bounded commands.…”

“…Ma et al [20] presented a hybrid flight controller for unmanned helicopters, where the FLS was constructed to cope with the parametric uncertainties. The fuzzy SMCs were proposed to realize the chattering attenuation and improve the disturbance rejection capability [17], [21], [22]. Although the accurate system dynamics is unnecessary, many logic rules are required for the fuzzy controller design, which would lead to structural complexity and computational pressure.…”

“…These infer the AFTNNO can achieve better estimation performance. 2) In comparison with the conventional fuzzy SMCs in [3], [18], [22] and fuzzy logic controllers in [15]- [17], [19]- [21], the designed adaptive FLS-based NFTSMC strategy not only has a simpler fuzzy structure, fewer logic rules and free singularity, but also updates fuzzy gain automatically, achieves the finite-time stability and improves the convergence speed, which helps overcome the undesired chattering and enhance the steady-state performance. When the tracking errors are close to the sliding mode surface, the fuzzy switching control part is removed to decrease the unnecessary energy loss.…”