The role of hydraulic systems is quite evident especially in the case of heavy machineries employed in industries, where the utilisation of high forces amid large stiffness is the prerequisite. Nevertheless, there has been substantial effort put forward in the development of advanced control strategies which finally addressed the issue of the position control. Proportional–integral–derivative control strategy happens to be one among them, which is a versatile and widely renowned approach involved in the position control in this study. Although, it is quite frequently observed that the hydraulic actuation system possesses strong nonlinearities. In this article, two different actuator position control strategies, that is, swash plate control of main pump and speed control strategy of prime mover are compared. In swash plate control strategy, the proportional–integral–derivative controller adjusts the swash plate of main pump through servo mechanism, whereas in the speed control strategy, the proportional–integral–derivative controller adjusts the speed of the electric motor through variable-frequency drive. For this purpose, two MATLAB-Simulink models are developed and validated experimentally. It is found that swash plate control strategy has better dynamic and control performance than the speed control strategy catering same position demand of the linear actuator.

Heavy earth-moving machinery is essential for construction, mining, and infrastructure development, but its traditional hydraulic systems, powered by diesel engines, are major contributors to energy losses and inefficiencies. Hydraulic circuits typically account for significant parasitic losses due to throttling, leakage, and low energy recovery, resulting in high fuel consumption and emissions. Recent innovations are transforming hydraulic technology to improve energy efficiency and sustainability. This review highlights advancements such as electro-hydraulic actuators, independent metering systems, and digital hydraulics, which enable precise flow control and minimize throttling losses. The integration of energy recovery systems, including hydraulic accumulators and hybrid architectures, further enhances efficiency by capturing and reusing energy during braking and lowering operations. Additionally, the adoption of smart sensors, predictive analytics, and advanced control algorithms enables real-time optimization of hydraulic performance, reducing idle losses and improving overall system responsiveness. Emerging trends such as fluid power electrification, compact high-pressure components, and the use of eco-friendly hydraulic fluids are also discussed. By synthesizing current research and industrial practices, this paper provides insights into the challenges, opportunities, and future prospects for achieving substantial energy efficiency gains through next-generation hydraulic technologies in heavy earth-moving equipment.

This paper highlights development of 2-DOF and 3-DOF energy management controllers using a Machine Learning (ML) algorithm for closed-circuit hydrostatic transmission of Front-End Loader (FEL) machine. Objective of energy management controller, is to keep prime-mover, pump and hydro-motor in its efficient zone irrespective of variation of load-speed at drive end. Based on duty cycles of the transmission and sensitivity analysis of drive components, GPR algorithm shows better prediction (within 3%) of optimized commands of hydro-motor and pump displacements and their operating speeds, which results in its efficient operation. Validated model of hydraulic transmission components is integrated with verified model of IC engine to investigate saving in fuel consumption through designed controllers. Comparative study between 2-DOF and 3-DOF controlled transmission systems infers that fuel consumption for engine in 3-DOF controlled system decreases up to 34% of that of 2-DOF with a 20%–30% relative increase in overall drive efficiency.

The increasing need for renewable energy has catalyzed advancements in wind turbine technology, especially in automation and robotic networking for the monitoring and control of wind turbines. This article discusses about the innovative turbine control techniques, that optimize efficiency, decrease the maintenance expenses, and improve safety. The integration of Automation and AI/ML facilitate predictive maintenance and offers robust control systems. Advanced control algorithms enhance turbine performance through wake steering, yaw alignment, and shadow flicker reduction. Real-time data sharing and guaranteed connectivity are enabled by wireless sensor networks and communication infrastructures, including Long Term Evolution (LTE), 5G, and Ethernet passive optical networks (EPON). Robots systems as, BladeBUG inspection robot and the Aerones cleaning robot execute efficient autonomous maintenance tasks. Emerging trends depend on AI/ML-driven predictive analytics and flexibility in robotics to enhance turbine performance, reduce downtime, and ensure the reliability of wind power supply.