Efficient computation of almost invisible inputs for linear input redundant systems

Input redundant systems have more inputs than needed to control the output and offer advantages such as fault tolerance and improved performance. However, identifying such redundant inputs in high-dimensional systems can be computationally intensive and is prone to inaccuracies. This article proposes an approach that uses model reduction by balanced truncation to simplify input redundancy analysis. By reducing the system order, we identify "invisible inputs" for the reduced-order model and design a reduced order state feedback mechanism to decouple them from the "visible inputs." Then, error bounds from balanced truncation provide guarantees for applying this decoupling to the original system, as a feedforward input. The approach offers significant computational savings, as demonstrated in numerical examples.