Neural networks for control systems: a survey
Automatica (Journal of IFAC)
A deterministic annealing neural network for convex programming
Neural Networks
Robust backstepping control of nonlinear systems using neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief Augmented gradient flows for on-line robust pole assignment via state and output feedback
Automatica (Journal of IFAC)
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
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Pole assignment is a basic design method for synthesis of feedback control systems. In this paper, a gradient flow approach is presented for robust pole assignment in synthesizing output feedback control systems. The proposed approach is shown to be capable of synthesizing linear output feedback control systems via on-line robust pole assignment. Convergence of the gradient flow can be guaranteed. Moreover, with appropriate design parameters the gradient flow converges exponentially to an optimal solution to the robust pole assignment problem and the closed-loop control system based on the gradient flow is globally exponentially stable. These desired properties make it possible to apply the proposed approach to slowly time-varying linear control systems. Simulation results are shown to demonstrate the effectiveness and advantages of the proposed approach.