Abstract: In this work, a novel event-triggered iterative learning model predictive control (ILMPC) framework is developed for a class of linear systems subject to input saturation and initial shift.

Banks share a growing desire to use AI to become faster and more innovative. Yet, for many organizations, the real challenge ...

Abstract: This paper investigates the issue of event-triggered distributed model predictive control for coupled nonlinear systems with additive disturbances. Specifically, this paper proposes two ...

China is pioneering a distinct AI approach, prioritizing systems for real-time urban, industrial, and logistical coordination ...

The future of equitable AI in community health centers will be measured by how effectively we are able to demand partnership ...

Artificial intelligence is no longer an emerging technology in mortgage servicing. For many servicers, it has already become ...

For investors, the 2026 World Cup is a deployment of some of the technologies driving the next structural growth cycle within ...

Recent advances in artificial intelligence have yielded remarkable demonstrations in speech, language, code generation, and ...

Around the 2026 FIFA World Cup, Hyundai and Boston Dynamics have taught footwork to the Atlas humanoid, while the Spot robot ...

AI inference gateways offer key growth opportunities fueled by industrial automation, smart factory deployment, and real-time analytics needs. Trends include real-time edge AI, low-latency ...

Discover how pharmaceutical intelligence is transforming medicine regulation in Africa, moving beyond traceability to enhance ...

Fuel card analytics and telematics reveal hidden inefficiencies in fleet programs. Explore real-time insights, predictive ...