Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Stable adaptive systems
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Modern Control Engineering
Robust Control: The Parametric Approach
Robust Control: The Parametric Approach
Linear Systems
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Feedback Control Theory
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Model-reference adaptive control based on neurofuzzy networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Nonlinear control structures based on embedded neural system models
IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Neural-network predictive control for nonlinear dynamic systems with time-delay
IEEE Transactions on Neural Networks
Stable adaptive neurocontrol for nonlinear discrete-time systems
IEEE Transactions on Neural Networks
Memory neuron networks for identification and control of dynamical systems
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
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This paper presents an efficient control scheme using a Hammerstein recurrent neural network (HRNN) based on the minimum description length (MDL) principle for controlling nonlinear dynamic systems. In the proposed control approach, an unknown system is first identified by the MDL-based HRNN, which consists of a static nonlinear model cascaded by a dynamic linear model and can be expressed in a state-space representation. For high-accuracy system modeling, we have developed a self-construction algorithm that integrates the MDL principle and recursive recurrent learning algorithm for constructing a parsimonious HRNN in an efficient manner. To ease the control of the system, we have established a nonlinearity eliminator that functions as the inverse of the static nonlinear model to remove the global nonlinear behavior of the unknown system. If the system modeling and the inverse of the nonlinear model are accurate, the compound model, the unknown system cascaded with the nonlinearity eliminator, will behave like the linear dynamic model. This assumption turns the task of complex nonlinear control problems into a simple feedback linear controller design. Hence, well-developed linear controller design theories can be applied directly to achieve satisfactory control performance. Computer simulations on unknown nonlinear system control problems have successfully validated the effectiveness of the proposed MDL-based HRNN and its control scheme as well as its superiority in control performance.