The cement kiln is one of the major energy-intense processes that need efficient controllers
to minimise fuel consumption, enhance clinker production, and improve cement quality. A …

This survey paper provides a review and perspective on intermediate and advanced
reinforcement learning (RL) techniques in process industries. It offers a holistic approach by …

Autonomous vehicles are expected to achieve self-evolution in the real-world environment
to gradually cover more complex and changing scenarios. Reinforcement learning focuses …

Abstract Model predictive control (MPC) is increasingly being considered for control of fast
systems and embedded applications. However, MPC has some significant challenges for …

The current trajectory planning methods for multi‐robot systems face challenges due to high
computational burden and inadequate adaptability in complex constrained environments …

This paper investigates the problem of event-triggered model predictive control for
constrained nonlinear systems. A dual-mode control strategy combined with two different …

Stable gait generation is a crucial problem for legged robot locomotion as this impacts other
critical performance factors such as, eg mobility over an uneven terrain and power …

For past several years the resiliency of the power grid is severely challenged by extreme
events. Black start and restoration scheme (BS&RS) is critical to enhance distribution grid …

Propagating uncertainties through nonlinear system dynamics in the context of Stochastic
Nonlinear Model Predictive Control (SNMPC) is challenging, especially for high …

Data-driven models are efficient decision support tools for monitoring harmful algal blooms
(HABs), particularly with the advent of the Internet of Things (IoT) and continuous data …