Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers the identification of Linear Parameter Varying (LPV) systems in Linear Fractional Transformation (LFT) form, where the “forward” part is a Linear Time Invariant (LTI) system, while the “feedback” part is given by a single scheduling variable available for measurement. Our main result shows that, despite the nonlinear dependence on the scheduling parameter, the system can be identified using convex optimization, provided that this parameter can be suitably manipulated. These results are illustrated with examples showing the effectiveness of the proposed method.
['Yongfang Cheng', 'Mario Sznaier']
I am writing a research paper on a idea Identification of LPV systems with LFT parametric dependence via convex optimization
[{"from": "human", "value": "I am writing a research paper on a idea Identification of LPV systems with LFT parametric dependence via convex optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers the identification of Linear Parameter Varying (LPV) systems in Linear Fractional Transformation (LFT) form, where the \u201cforward\u201d part is a Linear Time Invariant (LTI) system, while the \u201cfeedback\u201d part is given by a single scheduling variable available for measurement. Our main result shows that, despite the nonlinear dependence on the scheduling parameter, the system can be identified using convex optimization, provided that this parameter can be suitably manipulated. These results are illustrated with examples showing the effectiveness of the proposed method."}]