A bilinear observer is proposed for a class of singular bilinear system subject to unknown input disturbance. Based on singular value decomposition technique, the existence of the solution to the decomposed system is presented. Then a bilinear observer is proposed for the decomposed system based on an algebraic Riccati equation, and the domain of attraction of the state estimation error is derived. Finally, a detailed design procedure is given to design a bilinear observer for a model of ?exible joint robot, which demonstrates the effectiveness of the proposed method.
The robust stability of a class of Hopfield neural networks with multiple delays and parameter perturbations is analyzed. The sufficient conditions for the global robust stability of equilibrium point are given by way of constructing a suitable Lyapunov functional. The conditions take the form of linear matrix inequality (LMI), so they are computable and verifiable efficiently. Furthermore, all the results are obtained without assuming the differentiability and monotonicity of activation functions. From the viewpoint of system analysis, our results provide sufficient conditions for the global robust stability in a manner that they specify the size of perturbation that Hopfield neural networks can endure when the structure of the network is given. On the other hand, from the viewpoint of system synthesis, our results can answer how to choose the parameters of neural networks to endure a given perturbation.
