Aircraft landing control in wind shear condition
ABSTRACT Most aircraft accidents occurred during final approach or landing. This study proposes cerebellar model articulation controller (CMAC) to improve the performance of automatic landing system (ALS). The atmospheric disturbances affect not only flying qualities of an airplane but also flight safety. If the flight conditions are beyond the preset envelope, the automatic landing system (ALS) is disabled and the pilot takes over. An inexperienced pilot may not be able to guide the aircraft to a safe landing at the airport when wind shear is encountered. An adaptive type-2 fuzzy CMAC (FCMAC) is applied to PID control to construct intelligent landing system which can guide the aircraft to a safe landing in severe wind shear environment.
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ABSTRACT: Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.IEEE TRANSACTIONS ON CYBERNETICS 07/2004; 34(3):1462-77. DOI:10.1109/ICSMC.2003.1244458 · 3.47 Impact Factor
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ABSTRACT: In this paper, a type-2 fuzzy switching control system is proposed for a biped robot, which includes switched nonlinear system modeling, type-2 fuzzy control system design, and a type-2 fuzzy modeling algorithm. A new switched system model is proposed to represent the continuous-time dynamic and discrete-event dynamic of a walking biped as a whole, which is helpful to analyze the closed-loop stability of the biped locomotion. A type-2 fuzzy switching control system is proposed for the switched system model to guarantee the gait stability and to achieve a robust control performance with a simplified control scheme. Finally, we propose a new fuzzy c-mean variance algorithm for the type-2 fuzzy system modeling to capture the variance of each clustering means, which can translate random uncertainties of original data into rule uncertainties. Simulation results are reported to show the performance of the proposed control system model and algorithms.IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 12/2007; 37(6-37):1202 - 1213. DOI:10.1109/TSMCC.2007.900649 · 1.53 Impact Factor
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ABSTRACT: This paper presents an intelligent aircraft automatic landing controller that uses recurrent neural networks (RNN) with genetic algorithms (GAs) to improve the performance of conventional automatic landing system (ALS) and guide the aircraft to a safe landing. Real-time recurrent learning (RTRL) is applied to train the RNN that uses gradient-descent of the error function with respect to the weights to perform the weights updates. Convergence analysis of system error is provided. The control scheme utilizes five crossover methods of GAs to search optimal control parameters. Simulations show that the proposed intelligent controller has better performance than the conventional controller.Neurocomputing 10/2008; 71(16-18-71):3224-3238. DOI:10.1016/j.neucom.2008.04.044 · 2.01 Impact Factor