LI-CHEN FUChang, W.-D.W.-D.ChangYang, J.-H.J.-H.YangKuo, T.-S.T.-S.Kuo2018-09-102018-09-10199707315090https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031103518&doi=10.2514%2f2.4044&partnerID=40&md5=ebb17c204976486a6dec747e18025c5ahttp://scholars.lib.ntu.edu.tw/handle/123456789/332235An adaptive robust neural-network-based control approach is proposed for bank-to-turn missile autopilot design. Feedforward neural networks with sigmoid hidden units are analyzed in detail for controller design. Without prior knowledge of the so-called optimal neural networks, we design a controller that exploits the advantages of both neural networks and robust adaptive control theory. For this scheme, a stable adaptive law is determined by using the Lyapunov theory, and the boundedness of all signals in the closed-loop system is guaranteed. No prior offline training phase is necessary, and only a single neural network is employed. It is shown that the tracking errors converge to a neighborhood of zero. Performance of the controller is demonstrated by means of simulations.[SDGs]SDG7Closed loop control systems; Computer simulation; Control theory; Convergence of numerical methods; Errors; Feedforward neural networks; Lyapunov methods; Missiles; Performance; Robustness (control systems); Closed loop system; Optimal neural networks; Robust adaptive control theory; Adaptive control systemsjournal article10.2514/2.40442-s2.0-0031103518