Flight control design using non-linear inverse dynamics
Automatica (Journal of IFAC)
Neural networks for control
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Optimal nonlinear estimation for aircraft flight control in wind shear
Automatica (Journal of IFAC)
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks
Automatica (Journal of IFAC)
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Output feedback control of nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
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An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing ''nominal model patients'' (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing ''realistic model patients'' (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.