
In this paper, we present an empirical balanced truncation method for nonlinear systems with linear time-invariant input vector field components. First, we define differential reachability and observability Gramians. They are matrix valued functions of the state trajectory (i.e. the initial state and input trajectory) of the original nonlinear system, and it is difficult to find them as functions of the initial state and input. The main result of this paper is to show that for a fixed state trajectory, it is possible to compute the values of these Gramians by using impulse and initial state responses of the variational system. Therefore, balanced truncation is doable along the fixed state trajectory without solving nonlinear partial differential equations, differently from conventional nonlinear balancing methods. We further develop an approximation method, which only requires trajectories of the original nonlinear systems. Our methods are demonstrated by an RL network along a trajectory.
Observability, Model reduction, balanced truncation, Balanced truncation, System structure simplification, Systems and Control (eess.SY), Proper orthogonal decomposition, proper orthogonal decomposition, Attainable sets, reachability, Optimization and Control (math.OC), Optimization and Control, Nonlinear systems, model reduction, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Nonlinear systems in control theory, nonlinear systems, Systems and Control
Observability, Model reduction, balanced truncation, Balanced truncation, System structure simplification, Systems and Control (eess.SY), Proper orthogonal decomposition, proper orthogonal decomposition, Attainable sets, reachability, Optimization and Control (math.OC), Optimization and Control, Nonlinear systems, model reduction, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Nonlinear systems in control theory, nonlinear systems, Systems and Control
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