Tham khảo tài liệu 'advances in flight control systems part 15', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Comparison of Flight Control System Design Methods in Landing 267 Vpd Kp Kd e 32 The compensator gain matrices Kp Kd e R are chosen so that the tracking error dynamics given by e Ae B vad -A 33 0 I 0 A B .-Kp -Kd . . I _ 34 are stable . the eigenvalues of A are prescribed. It is evident from Eq. 33 that the role of the adaptive component vad is to cancel A .The adaptive signal is chosen to be the output of a single hidden layer 26 . V- WTa VTx 35 where V and w are the input and output weighting matrices respectively and o is a sigmoid activation function. Although ideal weighting matrices are unknown and usually cannot be computed they can be adapted in real time using the following NN weights training rules 27 w - ja-a VTx eTPB k ew rW 36 V -rV xeTPBWT k e V 37 where rw rV and rw rV are the positive definite learning rate matrices and K is the emodification parameter. Here P is a positive definite solution of the Lyapunov equation AtP PA Q 0 for any positive definite Q . B. Fuzzy Logic-Based Control Design The existing applications of fuzzy control range from micro-controller based systems in home applications to advanced flight control systems. The main advantages of using fuzzy are as follows 1. It is implemented based on human operator s expertise which does not lend itself to being easily expressed in conventional proportional integral-derivative parameters of differential equations but rather in action rules. 2. For an ill-conditioned or complex plant model fuzzy control offers ways to implement simple but robust solutions that cover a wide range of system parameters and to some extent can cope with major disturbances. The aircraft landing procedures admit a linguistic describing. This is practiced for example in case of guiding for landing in non-visibility conditions or in piloting learning. This approach permits to build a model for landing control based on the reasoning rules using the fuzzy logic. The process requires the control of the following .
