The magnitude of atmospheric torque in Very Low Earth Orbit (VLEO) can be very significant, affecting the control accuracy of the satellite and easily causing the saturation of the reaction wheels. The shifting mass on the large surface of a Disk-shaped Microsatellite (Disk-sat) is the first attempt to actively control the atmospheric torque, which can significantly improve the anti-interference and unloading capabilities. This paper provides a solution to overcome the lack of direction of atmospheric torque and inability to orient the orbital system in all three axes by using magnetic torquers compensation. By using adaptive model predictive control (MPC), the amount of real-time computation on the satellite is balanced by simplified dynamics and piecewise linearization while planning two constrained moments. And the controllability and stability analyses of the method are given. Simulation results show that the method can achieve stable three-axis orientation to the orbital system.

As agricultural PV (APV) production advances, optimization frameworks are needed to simulate and evaluate designs without experimentation. Existing models assess specific metrics like crop growth and energy production, but no single framework accounts for all variations. Many previous models fail to capture the complexities of APV systems. This study integrates an agrivoltaic-responsive crop model with sub-leaf ray tracing for accurate photosynthetic performance assessment. Validated with field data from Davis, California, the framework identified an optimal design with minimal crop yield reduction (14.5%) and a 24.6% reduction in PV energy potential, offering insights into seasonal growth, crop performance, and power production.

We describe a control structure that is commonly used in the process industry, e.g. chemical and petrochemical industries, for switching between manipulated variables (MVs), but which has received little attention in academia. It has one controller for each MV, typically PID-controllers, that control the same process value ( y ) but with different manipulated variables (u i ) and different setpoints ( r i ). The scheme is sometimes called “separate controllers with different setpoints”, but we suggest that a better name is “split-parallel control” (SPC), since the two controllers are placed in parallel in the block diagram, but the active control action is split between the two controllers, similar to in split-range control (SRC). SPC is an alternative to SRC, but it does have some advantages compared to SRC, including ease of implementation and the possibility to have different PID tunings for each MV. We also state some yet unresolved questions regarding the SPC structure, especially in regard to stability. Split-parallel control (SPC) uses setpoint separation to perform the switching, which is an advantage in some cases, for example, for bidirectional inventory control.

Tilt-rotor quadcopter UAV (TRQUAV) has more forward momentum with H-configuration fuselage and tiltable longitudinal rotors. A predefined-time backstepping sliding mode controller (PTBSMC) is designed to address the rapid tracking of the position and attitude of TRQUAV. Firstly, this paper establishes the dynamics model of TRQUAV through the Newton-Euler equations. Then, a position predefined-time sliding mode controller is proposed to meet the requirement of fast position tracking and feed the desired roll value of attitude. Moreover, an attitude PTBSMC is proposed to improve the attitude error tracking speed. By the predefined-time exponential reaching law, the error of position and attitude will converge to the vicinity of zero within the predefined time. The PTBSMC’s stability is proven by Lyapunov theory, and simulation results demonstrate the effectiveness and superiority of the faster convergence of the proposed controller.

Ship path planning is one of the critical tasks of autonomous navigation, requiring the identification of safe, efficient, and collision-free routes in complex maritime environments. Traditional path planning methods focus solely on generating a static route from a starting point to a destination, neglecting the inherent maneuverability characteristics of different ships. This oversight results in the issue that the generated paths are often ill-suited or impractical to the actual navigation requirements. To address this issue, a novel ship path planning algorithm based on Monte Carlo Tree Search (MCTS) is proposed in this study, which is a prominent decision-making method widely applied in different fields. The capability of MCTS to balance exploration and exploitation makes it particularly effective for complex decision-making tasks. By integrating the ship maneuvering motion model, specific maneuvering performance of a tanker model is considered and incorporated into the planning process, which effectively improves the feasibility and smoothness of the planned path. A series of simulations are designed and conducted to evaluate the performance of the proposed MCTS-based planning algorithm. The results demonstrate that the proposed algorithm provides smoother, more efficient, and practically implementable solutions for ship path planning tasks.

This article presents an application of the Smith predictor-based control strategy for controlling the feed rate of a primary crushing circuit in an iron ore beneficiation plant, focusing on compensating for system dead time. The methodology includes problem characterization, plant modeling, and application at Vale S.A.’s Fabrica Nova unit, Brazil. The results show a 4.3-minute reduction in the time to reach the feed setpoint and a 9.2% increase in the plant’s productivity, without compromising process safety. The proposed strategy contributes to the process control literature by demonstrating the practical application of the Smith predictor for dead time compensation, providing a replicable alternative for other industrial systems.