System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm

Nonlinear Model Predictive Control (NMPC) offers a powerful approach for controlling complex nonlinear systems, yet faces two key challenges. First, accurately modeling nonlinear dynamics remains difficult. Second, variables directly related to control objectives often cannot be directly measured during operation. Although high-cost sensors can acquire these variables during model development, their use in practical deployment is typically infeasible. To overcome these limitations, we propose a Predictive Virtual Sensor Identification (PVSID) framework that leverages temporary high-cost sensors during the modeling phase to create virtual sensors for NMPC implementation. We validate PVSID on a Two-Degree-of-Freedom (2-DoF) direct-drive robotic arm with complex joint interactions, capturing tip position via motion capture during modeling and utilize an Inertial Measurement Unit (IMU) in NMPC. Experimental results show our NMPC with identified virtual sensors achieves precise tip trajectory tracking without requiring the motion capture system during operation. PVSID offers a practical solution for implementing optimal control in nonlinear systems where the measurement of key variables is constrained by cost or operational limitations.

Tracking Control foe Multi-Agent Systems Using Broadcast Signals Based on Positive Realness

Broadcast control is one of decentralized control methods for networked multi-agent systems. In this method, each agent does not communicate with the others, and autonomously determines its own action using only the same signal sent from a central controller. Therefore, it is effective for systems with numerous agents or no-communication between agents. However, it is difficult to manage the stochastic action process of agents considering engineering applications. This paper proposes a decentralized control such that agents autonomously select the deterministic actions. Firstly, a non-linear controller with a binary output of each agent including 0 is introduced in order to express stop actions autonomously when the target is achieved. The asymptotic stability to the target is proved. Secondly, the controller can adjust the tendency of actions in order to make it easier to manage the actions. Thirdly, the controller is extended to that with a continuous output in order to reduce the tracking error to the target and the output vibration. Finally, the effectiveness of the proposed control is verified by numerical experiments.