Takumi Namba

Takumi Namba, Kiyotsugu Takaba

This paper proposes a self-triggered synchronization control method of a general high-order linear time-invariant multi-agent system through a cloud repository. In the cloud-mediated self-triggered control, each agent asynchronously accesses the cloud repository to get past information on its neighboring agents. Then, the agent predicts future behaviors of its neighbors as well as of its own, and locally determines its next access time to the cloud repository. In the case of a general high-order linear agent dynamics, each agent has to estimate exponential evolution of its trajectory characterized by eigenvalues of a system matrix, which is different from single/double integrator or first-order linear agents. Our proposed method deals with exponential behaviors of the agents by tightly evaluating the bounds on matrix exponentials. Based on these bound, we design the self-triggered controller through a cloud which achieves bounded state synchronization of the closed-loop system without exhibiting any Zeno behaviors. The effectiveness of the proposed method is demonstrated through the numerical simulation.

Takumi Namba

In this paper, we study robust distributed sub-optimal coordination of linear agents subject to input nonlinearities. Inspired by the robust agreement literature, we formulate a bounded distributed sub-optimal coordination problem, in which each agent converges to a neighborhood of the optimizer of a global optimization problem defined over a communication network. We propose a novel control protocol, and analyze convergence by employing a robust control approach, in which both the input nonlinearities and the gradients of the objective functions are treated in a unified manner via sector conditions. In particular, we derive sufficient conditions for the solvability of the considered problem and characterize them in terms of matrix inequalities. The effectiveness of the proposed method is demonstrated through a numerical simulation.

Kohei Ayaka, Takumi Namba, Kiyotsugu Takaba

This paper proposes a novel informativity-based data-driven synthesis method for a sub-optimal linear quadratic (LQ) regulator for linear input-delay systems from noisy input-state data. Exploiting the augmented state structure of input-delay systems with a known delay length, we derive a linear matrix inequality (LMI) condition for the data-driven synthesis of the augmented state-feedback controller that achieves a prescribed LQ performance level for every plant model consistent with the data. The proposed LMI condition enables efficient controller synthesis via convex optimization. Numerical simulations demonstrate the effectiveness of the proposed method. The trade-off between the achievable LQ performance and the uncertainty in the data is also clarified through a numerical example.