Title :
Neural-net-based direct adaptive control for a class of nonlinear plants
Author_Institution :
E/E Eng., DaimlerCrysler Corp., Auburn Hills, MI, USA
fDate :
1/1/2000 12:00:00 AM
Abstract :
A direct adaptive control algorithm is presented for a class of nonlinear plants. No restriction has been imposed on the plant structure. The only condition the plant must satisfy is that the instantaneous input-output gain be positive. An artificial neural network (ANN)-based nonlinear controller structure has been employed. In line with the gain scheduling principle, however, the controller also has a pseudolinear time-varying structure with the parameters being the functions of the operating point. Simulation studies are also presented to validate the theoretical findings
Keywords :
adaptive control; controllers; neural nets; nonlinear control systems; direct adaptive control algorithm; instantaneous input-output gain; neural-net-based direct adaptive control; nonlinear controller structure; nonlinear plants; pseudolinear time-varying structure; simulation studies; Adaptive control; Artificial neural networks; Control systems; Convergence; Function approximation; Neural networks; Nonlinear control systems; Nonlinear systems; Robustness; Shape control;
Journal_Title :
Automatic Control, IEEE Transactions on