Yuki Minami

Prediction governors: Optimal solutions and application to electric power balancing control

This paper focuses on control systems with unknown reference signals, and considers a shaping problem of predicted reference signals under the situation that past actual reference signals are available. We first propose a signal shaping mechanism called prediction governor, and formulate an optimal design problem of prediction governors. The design problem finds a prediction governor such that the system composed of a given plant and the governor is an optimal approximation of a given ideal plant in the sense of input-output relation. Then, we analytically derive an optimal prediction governor which is characterized by plant parameters. Finally, the optimal prediction governor is applied to electric power demand prediction problem for power balancing control, and its usefulness is illustrated by a numerical example.

Differential Evolution-Based Synthesis of Dynamic Quantizers with Fixed-Structures

In the discrete-valued input control of dynamical systems, quantizers, which convert continuous-valued signals into discrete-valued ones, play an important role. This paper focuses on the design problem of dynamic quantizers with fixed-structures such as a fixed-order and a decentralized structure. It is known to be difficult to solve the problem since it is NP-hard. Therefore, in order to solve the problem in practical steps, we adopt differential evolution (DE) which is one of the evolutionary computation algorithms. We first propose a DE-based design method of quantizers with fixed structures. Then, the effectiveness of the proposed method is verified by examples in the discrete-valued input control of dynamical systems. Moreover, we compare the performance of the proposed DE-based method with that of a particle swarm optimization (PSO)-based method, and show that DE is an easy-to-use and reliable tool for quantizer design problem.