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Neurocomputing
Neural networks for control
Adaptation in natural and artificial systems
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Neural Networks: A Comprehensive Foundation
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Artficial Immune Systems and Their Applications
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In this paper, an adaptive nonlinear control strategy derived from a biological control system is developed and its applications to the automotive engine are presented. The biological adaptive nonlinear control strategy inspired by the functions of baroreceptor reflex is realized by a parallel controller. The controller consists of a linear controller and a nonlinear controller that interact via a reciprocal lateral inhibitory mechanism. The linear controller design is based on a PID controller, while the nonlinear controller is constructed from neural networks which are updated online. In order to provide superior control performances, the controller must be robust to external unknown disturbances, un-modeled dynamics and plant uncertainties and also be able to perform well under a wide range of operating conditions. In the linear operating region, the linear controller takes control. If the controlled process is far away from the linear regime or is disturbed by the noise, the output of linear controller may be inappropriate, and therefore the nonlinear controller is activated to compensate for the inadequacy of the linear controller in a dynamic environment and in the presence of distances and process parameter variations. These situations can be addressed by adjusting the amount of lateral inhibition and learning the characteristics of the controlled system such that desirable controller outputs are produced in any particular operating region. The novelty of the biological adaptive nonlinear control strategy is that each controller modulates the other controller via the reciprocal lateral inhibitory connections. The good transient performance, computational efficiency, real-time adaptability and superior learning ability are illustrated through extensive numerical simulations for engine torque management driven by the biological adaptive nonlinear control strategy.