Gain-Scheduled Two-Loop Autopilot for an Aircraft

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Laleh Ravanbod ◽  
Dominikus Noll
Keyword(s):  
Nonlinear Systems ◽  
Flight Control ◽  
New Method ◽  
Optimal Controller ◽  
Control Law ◽  
Vertical Acceleration ◽  
Operational Conditions ◽  
Gain Scheduled

We present a new method to compute output gain-scheduled controllers for nonlinear systems. We use structured H∞-control to precompute an optimal controller parametrization as a reference. We then propose three practical methods to implement a control law which has only an acceptable loss of performance with regard to the optimal reference law. Our method is demonstrated in longitudinal flight control, where the dynamics of the aircraft depend on the operational conditions velocity and altitude. We design a structured controller consisting of a PI-block to control vertical acceleration, and another I-block to control the pitch rate.

scholarly journals Design of Gain Scheduled H.INF. Flight Control Law Satisfying Specified Stability Margin.

2000 ◽  
Vol 48 (561) ◽  
pp. 322-328 ◽  
Author(s):  
Yasuhiro YAMAGUCHI ◽  
Masahiro OHNO ◽  
Takashi HATA ◽  
Atsumi OHARA ◽  
Masakazu IDE
Keyword(s):  
Flight Control ◽  
Stability Margin ◽  
Control Law ◽  
Gain Scheduled

Automated procedure for gain scheduled flight control law design

2000 ◽  
Author(s):  
M. Oosterom ◽  
G. Schram ◽  
R. Babuska ◽  
H. Verbruggen
Keyword(s):  
Flight Control ◽  
Control Law ◽  
Gain Scheduled

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

A Design Method of LS-SVM Based Stable Adaptive Controller for a Class of Nonlinear Systems

2011 ◽  
Vol 48-49 ◽  
pp. 17-20
Author(s):  
Chun Li Xie ◽  
Tao Zhang ◽  
Dan Dan Zhao ◽  
Cheng Shao
Keyword(s):  
Nonlinear Systems ◽  
Design Method ◽  
Nonlinear Function ◽  
Lyapunov Stability Theory ◽  
Adaptive Controller ◽  
Control Law ◽  
Continuous Systems ◽  
Control Schemes ◽  
Closed Systems ◽  
Simulation Results

A design method of LS-SVM based stable adaptive controller is proposed for a class of nonlinear continuous systems with unknown nonlinear function in this paper. Due to the fact that the control law is derived based on the Lyapunov stability theory, the scheme can not only solve the tracking problem of this class of nonlinear systems, but also it can guarantee the asymptotic stability of the closed systems, which is superior to many LS-SVM based control schemes. The effectiveness of the proposed scheme is demonstrated by simulation results.

Application of Practical Observer to Semi-Active Suspensions

1998 ◽  
Vol 122 (2) ◽  
pp. 284-289 ◽  
Author(s):  
H. Nakai ◽  
S. Oosaku ◽  
Y. Motozono
Keyword(s):  
Ride Comfort ◽  
Difficult Problem ◽  
Nonlinear Observer ◽  
Vertical Acceleration ◽  
Acceleration Sensor ◽  
Active Suspensions ◽  
Linear Observer ◽  
Gain Scheduled

This paper presents the development of gain-scheduled observers for semi-active suspensions. The states of the semi-active suspensions must be accurately obtained because the accuracy directly affects system performances such as ride comfort. Nonlinearity in the absorber of the semi-active suspensions is a difficult problem for estimating the accurate states using conventional linear observer theories. To solve this problem, we have designed a new gain-scheduled observer by introducing two improvements. The validity of this nonlinear observer was confirmed by simulations and experiments. The results indicate that the present observer can accurately estimate the suspension stroke velocity using the vertical acceleration sensor on the sprung mass. [S0022-0434(00)02302-9]

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