This paper investigates the vulnerability of bilat-eral teleoperation systems to perfectly undetectable False Data Injection Attacks (FDIAs). Teleoperation, one of the major applications in robotics, involves a leader manipulator operated by a human and a follower manipulator at a remote site, connected via a communication channel. While this setup en-ables operation in challenging environments, it also introduces cybersecurity risks, particularly in the communication link. The paper focuses on a specific class of cyberattacks: perfectly un-detectable FDIAs, where attackers alter signals without leaving detectable traces at all. Compared to previous research on linear and first-order nonlinear systems, this paper examines bilateral teleoperation systems with second-order nonlinear manipulator dynamics. The paper derives mathematical conditions based on Lie Group theory that enable such attacks, demonstrating how an attacker can modify the follower manipulator's motion while the operator perceives normal operation through the leader device. This vulnerability challenges conventional detection methods based on observable changes and highlights the need for advanced security measures in teleoperation systems. To validate the theoretical results, the paper presents experimental demonstrations using a teleoperation system connecting robots in the US and Japan.

We consider a gray-box modeling of a McKibben pneumatic artificial muscle (PAM) actuated by a proportional directional control valve. This paper presents a hybrid nonlinear model of the PAM system and then proposes a systematic parameter identification procedure that uses a game-theoretic learning algorithm to obtain the appropriate parameter values for the modeling. With a practical example, finally, we verify the proposed method by illustrating a process of searching for the parameter values together with figures of after-and-before learning. As a result, we see that the resulting parameters are better than ones obtained by our previously-proposed heuristic and trial-and-error-based algorithm.

This paper proposes a procedure for identifying a hybrid system model of a pneumatic artificial muscle that vertically suspends a mass. The obtained model involves if-then formulation and nonlinearities such as flow dynamics and friction because it is derived from mechanical and hydrodynamic equations and is developed with empirical modifications. Via analysis of the hybrid nonlinear system, we can clarify several dominant parameters characterizing the PAM dynamics in terms of steady-state and transient characteristics respectively. Based on the analysis result, then, a reasonable identification procedure can be composed. Furthermore, with a practical testbed system of the PAM system, we validate the proposed procedure producing the hybrid model under several load conditions.

In this paper, in order to manage both computing and network resources effectively in optical grid networks, we propose a dynamic management method which is implemented in a job manager. The proposed method adjusts the number of tasks which are transmitted to a computing site with PID control so that a resource manager can store a constant number of tasks, resulting in an effective utilization of computing resources. Moreover, lightpaths are established and released dynamically by comparing output signal of PID controller with the maximum number of transmitted tasks, resulting in an effective utilization of network resources. We evaluate the performance of the proposed method, and then we investigate the impact of PID parameters. In numerical examples, we show that the proposed method can manage computing and network resources at the same time more effectively than a conventional method.