This paper investigates the unknown input observability problem in the nonlinear case. Specifically, the systems here analyzed are characterized by dynamics that are nonlinear in the state and linear in the inputs and characterized by a single unknown input and multiple known inputs. Additionally, it is assumed that the unknown input is a differentiable function of time (up to a given order). The goal of the paper is not to design new observers but to provide a simple analytic condition in order to check the weak local observability of the state. In other words, the goal is to extend the well known observability rank condition to these systems. Specifically, the paper provides a simple algorithm to directly obtain the entire observable codistribution. As in the standard case of only known inputs, the observable codistribution is obtained by recursively computing the Lie derivatives along the vector fields that characterize the dynamics. However, in correspondence of the unknown input, the corresponding vector field must be suitably rescaled. Additionally, the Lie derivatives must be computed also along a new set of vector fields that are obtained by recursively performing suitable Lie bracketing of the vector fields that define the dynamics. In practice, the entire observable codistribution is obtained by a very simple recursive algorithm. However, the analytic derivations required to prove that this codistribution fully characterizes the weak local observability of the state are complex and, for the sake of brevity, are provided in a separate technical report. The proposed analytic approach is illustrated by checking the weak local observability of several nonlinear systems driven by known and unknown inputs.
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