Neural networks for control
Hybrid Intelligent Systems
Theoretical Models in Biology: The Original of Life, the Immune System, and the Brain
Theoretical Models in Biology: The Original of Life, the Immune System, and the Brain
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Neural Computation
Handbook of Neural Computation
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
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Over the past decade much progress has been made in the development of adaptive, model-following flight control systems. These systems are being designed to account for the degradation and even the failure of the actuators used to implement the control laws within aircraft. Typically, these adaptive, model-following flight control systems require software components capable of (1) monitoring system performance, (2) quantifying changes occurring in the performance characteristics of actuators, and (3) adapting control laws based on changes in actuator performance. Interestingly enough, the challenges facing natural immune systems also require the successful completion of three similar tasks: (1) monitoring organism performance, (2) identification of antigens, and (3) distribution of targeted antibodies. Thus, the characteristics inherent in natural immune systems have been captured and employed in computational systems called artificial immune systems (AISs). This paper describes an adaptive, model-following flight control system based on an artificial immune system. The effectiveness of the approach is demonstrated in a system designed to maintain cruise conditions in the simulation of a Boeing 747 aircraft in the presence of atmospheric turbulence and degradations in the performance characteristics of actuators used to manipulate various control surfaces.