Journal of Optimization Theory and Applications
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Bayesian regularization and pruning using a Laplace prior
Neural Computation
A receding-horizon regulator for nonlinear systems and a neural approximation
Automatica (Journal of IFAC)
Model selection in neural networks
Neural Networks
Nonparametric Estimation and Adaptive Control of Functional Autoregressive Models
SIAM Journal on Control and Optimization
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
A stable one-step-ahead predictive control of non-linear systems
Automatica (Journal of IFAC)
Editorial: Introduction to the special issue on neural network feedback control
Automatica (Journal of IFAC)
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
Multilayer discrete-time neural-net controller with guaranteed performance
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Bayesian nonlinear model selection and neural networks: a conjugate prior approach
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Neural modelling and control of a Diesel engine with pollution constraints
Journal of Intelligent and Robotic Systems
| Hi-index | 22.14 |
We consider the problem of predictive control of uncertain stochastic discrete I/O systems. Given a model identification procedure able to give accurate output system estimates, e.g. a neural network approximation, we use another feedforward neural network to generate at each time step a constrained optimal control. Dynamic backpropagation is used to improve when necessary the controller network parameters. Both system and controller neural structures are first selected off-line by a statistical Bayesian procedure in order to make the predictive control minimizing process more efficient. The issue of stochastic stability of the closed-loop is considered. We developed this approach for the tracking control of such uncertain systems as biotechnological processes. Actual and simulated predictive neuro-control case studies in this field of application are proposed as illustrations. A comparison with a more classic quasi-Newton-based approach is also proposed, showing the interest of this neuro-control approach.