This paper presents a method to improve the computational efficiency of data-driven controller design in encrypted control systems. The method reduces design time by simplifying computations with a filter that has an infinite impulse response structure, where the coefficients are derived from input-output data. As a result, the proposed approach significantly reduces design time compared to conventional methods and improves tracking performance.

This paper proposes a novel structured kernel that enhances the interpretability of a matrix-variate Gaussian process model for multi-step-ahead prediction of vehicle battery state of charge (SOC). Whereas conventional Kronecker-product structures mix contributions along the row and column directions of matrix data, making it difficult to separate those features, the proposed method employs a Kronecker sum to decompose the kernel matrix into distinct row and column components. As a result, the correlation structures in the row and column can each be interpreted independently. Experiments using real data from 100 vehicles show that the proposed model achieves superior prediction accuracy compared to both a baseline model and a Kronecker-product-based model. Furthermore, clustering based on the column kernel reveals meaningful vehicle groups characterized by their day-of-week usage patterns.

A fast method for computing controllers designed with hierarchical optimal control (HOC) is proposed. Algebras, which are the mathematical algebraic structures utilized in HOC, are decomposed to achieve the finest possible block-diagonalization of the algebraic Riccati equations that should be solved in controller computation. Furthermore, to reduce computational complexity, the block-diagonalization process is divided into several minor steps by using the property that hierarchies in HOC are characterized by Kronecker products. The computational complexity of the method is analyzed. For the hierarchies expected in HOC, notable reductions in complexity are anticipated. The proposed method facilitates the application of HOC to large-scale systems.