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 Systems Man and Cybernetics Part B (Cybernetics) 07/2004; 34(3):1462-77. · 3.24 Impact Factor
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ABSTRACT: Optimal abort landing trajectories of an aircraft under different windshear-downburst situations are computed and discussed. In order to avoid an airplane crash due to severe winds encountered by the aircraft during the landing approach, the minimum altitude obtained during the abort landing maneuver is to be maximized. This maneuver is mathematically described by a Chebyshev optimal control problem. By a transformation to an optimal control problem of Mayer type, an additional state variable inequality constraint for the altitude has to be taken into account; here, its order is three. Due to this altitude constraint, the optimal trajectories exhibit, depending on the windshear parameters, up to four touch points and also up to one boundary arc at the minimum altitude level. The control variable is the angle of attack time rate which enters the equations of motion linearly; therefore, the Hamiltonian of the problem is nonregular. The switching structures also includes up to three singular subarcs and up to two boundary subarcs of an angle of attack constraint of first order. This structure can be obtained by applying some advanced necessary conditions of optimal control theory in combination with the multiple-shooting method. The optimal solutions exhibit an oscillatory behavior, reaching the minimum altitude level several times. By the optimization, the maximum survival capability can also be determined; this is the maximum wind velocity difference for which recovery from windshear is just possible. The computed optimal trajectories may serve as benchmark trajectories, both for guidance laws that are desirable to approach in actual flight and for optimal trajectories may then serve as benchmark trajectories both for guidance schemes and also for numerical methods for problems of optimal control.Journal of Optimization Theory and Applications 03/1995; 85(1):21-57. · 1.42 Impact Factor
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ABSTRACT: In the last three decades, optimality-based auto-landing designs have been considered to the most effective way by many authors. However, it is known that the straight forward solution to the optimal control problem leads to Two Point Boundary Value Problem (TPBVP) (Riccati equation), which is usually too complex in solution, backward in the time, and real-time onboard implementation, or the final time, as a boundary condition, may also not be known precisely. To avoid these problems, first, a suboptimal solution by assuming t<sub>f</sub>→∞ has been considered and its inapplicability has been discussed. Then an optimal controller for landing phase of a typical commercial aircraft has been designed. Finally, seven neural networks were being trained to learn the costates of the system to estimate the costates in similar scenarios without using the final time value, which usually is needed in solving the optimal control problems.Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on; 07/2003