A class of event-triggered stabilization technique works with a small gain theorem when the controlled system has a certain input-to-state stability (ISS) property from the measurement (sensor) disturbance to state. However, such an ISS property is not always achievable for nonlinear systems. In particular, early research suggests that global internal stabilizability does not imply global external stabilizability even for small sensor disturbances. This note proposes a new event-triggered stabilization technique that requires a milder condition that the controlled system has a certain ISS property from the input (actuator) disturbance to state. The technique is further applied to explicitly construct an event-triggered controller for the output regulation problem of a class of nonlinear systems. With the design, Zeno behavior is avoided.

Controlling closed architecture industrial robot manipulators poses significant challenges due to limited access to inner controller configurations and specific control gain structures. The absence of open torque or voltage interfaces further compounds these difficulties. Consequently, traditional methods such as the computed-torque approach often prove inadequate when applied to closed architecture robots, widening the gap between advanced control algorithms and practical industrial requirements. In response, this paper introduces a unified framework that utilizes adaptive neural networks to tackle these challenges in controlling closed architecture industrial manipulators. Our approach operates independently of the robot’s dynamics, inner controller configuration, and control gain structure. We provide thorough evidence showcasing the boundedness of all control variables. Moreover, the proposed approach is versatile, allowing for the use of a single joint velocity controller across robotic manipulators employing closed control architecture, even under varying conditions. Our strategy streamlines implementation without requiring complex calculations for updating control variables. Experimental results and comparative studies are provided to illustrate the applicability and effectiveness of our proposed control strategy compared to existing approaches.

This paper examines a consensus problem in multiagent discrete-time systems, where each agent can exchange information only from its neighbor agents. A decentralized protocol is designed for each agent to steer all agents to the same vector. The design condition is expressed in the form of a linear matrix inequality. Finally, a simulation example is presented and a comparison is made to demonstrate the effectiveness of the developed methodology.