title: Hybrid feedback feedforward: An efficient design of adaptive neural network control.
abstract: This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost.
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore. Electronic address: biepany@nus.edu.sg.
authors.forename: Yongping
authors.initials: Y
authors.lastname: Pan
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China. Electronic address: aulyq@scut.edu.cn.
authors.forename: Yiqi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: binxu@nwpu.edu.cn.
authors.forename: Bin
authors.initials: B
authors.lastname: Xu
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore. Electronic address: bieyhy@nus.edu.sg.
authors.forename: Haoyong
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D002755:Choice Behavior; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D019359:Knowledge; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009048:Motor Skills / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D017711:Nonlinear Dynamics; D035501:Uncertainty
keywords: Adaptive control; Euler–Lagrange system; Feedforward compensation; Human motor learning control; Neural network