System identification: theory for the user
System identification: theory for the user
Continuous-time self-tuning control
Continuous-time self-tuning control
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Discrete-time control systems (2nd ed.)
Discrete-time control systems (2nd ed.)
Self-Tuning Systems: Control and Signal Processing
Self-Tuning Systems: Control and Signal Processing
A new neural network-based approach for self-tuning control of nonlinear SISO discrete-time systems
International Journal of Systems Science
Stabilizing controller design for uncertain nonlinear systems using fuzzy models
IEEE Transactions on Fuzzy Systems
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
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This paper presents a new neural network-based approach for self-tuning control of nonlinear MIMO dynamic systems. According to the approach, a neural network ARMAX (NN-ARMAX) model of the system is identified and continuously updated, using an online training algorithm. Control design is accomplished by solving an optimal discrete-time linear quadratic tracking problem using an observable block companion form Kalman innovation model, which is built from the parameters of a local linear version of the NN-ARMAX model. The state-feedback control law is implemented using the Kalman estimated state, which is calculated without estimating the noise covariance properties. The effectiveness of the proposed control approach is illustrated using a simulation example.