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California is the world’s biggest producer and exporter of almonds. Currently, the sweeping of almonds during the harvest creates a significant amount of dust, causing air pollution in the neighboring urban areas. A low-dust sweeping system was designed to reduce the dust during the sweeping of almonds in the orchard. The system includes a feedback control system to control the sweeper brushes’ height and their angular velocity by adjusting the forward velocity of the harvester and the brushes’ rotational speeds to avoid any extra overlapping sweeping, which increases dust generation. The governing kinematic equations for sweepers’ angular velocity and vehicle forward speed were derived. The feedback controllers for synchronizing these speeds were designed to optimize brush/dust contact to minimize dust generation. The sweepers’ height controller was also designed to stabilize the gap between the brushes and the orchard floor and track the road trajectory. Controllers were simulated and tuned for a fast response for agricultural applications with less than a second response delay. Results showed that the designed system has acceptable performance and generates low amounts of dust within the acceptable range of California ambient air quality standards.

This paper presents a hybrid control system that is able to improve dimensional accuracy of geometrically complex parts manufactured by direct metal deposition process. The melt pool height is monitored by three high-speed charged couple device cameras in a triangulation setup. The melt pool temperature is monitored by a dual-color pyrometer. A two-input single-output hybrid control system including a master height controller and a slave temperature controller is used to control both height growth and melt pool temperature at each deposition layer. The height controller is a rule-based controller and the temperature controller uses a generalized predictive control algorithm with input constraints. When the melt pool height is above a prescribed layer thickness, the master height controller blocks control actions from the temperature controller and decreases laser power to avoid over-building. When the melt pool height is below the prescribed layer thickness, the temperature controller bypasses the height controller and dynamically adjusts laser power to control the melt pool temperature. This hybrid controller is able to achieve stable layer growth by avoiding both over-building and under-building through heat input control. A complex 3-D turbine blade with improved geometrical accuracy is demonstrated using the hybrid control system.

Standing up from a chair is a key daily life activity that is sensitive to functional limitations as we age and associated with falls, frailty, and institutional living. Predictive neuromusculoskeletal models can potentially shed light on the interconnectivity and interdependency of age-related changes in neuromuscular capacity, reinforcement schemes, sensory integration, and adaptation strategies during stand-up. Most stand-up movements transfer directly into walking (sit-to-walk). The aim of this study was to develop and validate a neuromusculoskeletal model with reflex-based muscle control that enables simulation of the sit-to-walk movement under various conditions (seat height, foot placement). We developed a planar sit-to-walk musculoskeletal model (11 degrees-of-freedom, 20 muscles) and neuromuscular controller, consisting of a two-phase stand-up controller and a reflex-based gait controller. The stand-up controller contains generic neural pathways of delayed proprioceptive feedback from muscle length, force, velocity, and upper-body orientation (vestibular feedback) and includes both monosynaptic an antagonistic feedback pathways. The control parameters where optimized using a shooting-based optimization method, based on a high-level optimization criterium. Simulations were compared to recorded kinematics, ground reaction forces, and muscle activation. The simulated kinematics resemble the measured kinematics and muscle activations. The adaptation strategies that resulted from alterations in seat height, are comparable to those observed in adults. The simulation framework and model are publicly available and allow to study age-related compensation strategies, including reduced muscular capacity, reduced neural capacity, external perturbations, and altered movement objectives.

The quadrotor Unmanned Aerial Vehicle (UAV) belongs to an open-loop unstable nonlinear system, which also has the characteristics of underdrive, strong coupling and external disturbance. In the height control of quadrotor UAVs, the traditional sliding mode control (SMC) and PID methods cannot quickly and effectively eliminate disturbance effects caused by gust, aerodynamic drag and other factors, which indicates that the quadrotor UAV cannot return to its predetermined trajectory. To this end, this paper proposes a dual closed-loop finite-time height control method for the quadrotor UAV. The proposed method is able to estimate and compensate for the disturbance in the height control and make up for the lack of anti-disturbance ability in the control process. More specifically, a finite-time Extended State Observer (ESO) and a finite-time super-twisting controller are designed for the velocity control system to compensate for the total disturbance and track the rapidly changing expected signal. An integral sliding mode controller is designed for the height control system. Simulation results show that the proposed method can reduce the chattering phenomenon of traditional SMC and improve both control accuracy and convergence speed.

Ceilometers are used routinely at aerodromes worldwide to derive the height and sky coverage fraction of cloud layers. This information, possibly combined with direct observations by human observers, contributes to the production of meteorological aerodrome reports (METARs). Here, we present ampycloud, a new algorithm, and its associated Python package for automatic processing of ceilometer data with the aim of determining the sky coverage fraction and base height of cloud layers above aerodromes. The ampycloud algorithm was developed at the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) as part of the AMAROC (AutoMETAR/AutoReport rOund the Clock) program to help in the fully automatic production of METARs at Swiss civil aerodromes. ampycloud is designed to work with no direct human supervision. The algorithm consists of three distinct, sequential steps that rely on agglomerative clustering methods and Gaussian mixture models to identify distinct cloud layers from individual cloud base hits reported by ceilometers. The robustness of the ampycloud algorithm stems from the first processing step, which is simple and reliable. It constrains the two subsequent processing steps that are more sensitive but also better suited to handling complex cloud distributions. The software implementation of the ampycloud algorithm takes the form of an eponymous, pip-installable Python package developed on GitHub and made publicly accessible.

In this paper, a robust control algorithm is designed to achieve a finite-time vehicle height and posture control through electronically controlled air suspension (ECAS) system subject to actuator faults, uncertainties under non-stationary condition. To achieve simultaneous position control of four corners of vehicle, synchronization errors between corners are taken to form a synchronization control strategy. Furthermore, to improve the system convergence speed and robustness, finite-time stability constrain is applied and H ∞ index is designed strategically in order to develop a novel robust finite-time controller. Since the solenoid valves in the ECAS system may degrade with the frequent switching, actuator fault and uncertain parameters are considered in this study to design the proposed fault-tolerant control methodology. Meanwhile, road disturbance is applied to the vehicle with the ECAS system to provide a non-stationary condition. Several software-in-the-loop tests and hardware-in-the-loop test are conducted to illustrate the effectiveness of the proposed controller.

Wire and arc additive manufacturing (WAAM) shows a great application potential to manufacture large-size metal components rapidly. However, the research on WAAM to build structural parts on uneven substrates in some practical engineering applications is still a challenging issue. Controlling torch height and deposition height is the major barrier that limits the high level of automatic manufacturing of parts directly on uneven substrates. This study proposes a cooperative sensing and control strategy to achieve the process stability and deposition height control in robotic pulsed gas tungsten arc additive manufacturing (P-GTA AM) on an uneven substrate. The feedforward and feedback heights during the deposition process are monitored by a passive vision sensor. A feedforward controller and a feedback controller are designed to adjust the GTA torch height and the wire feed speed in real time, respectively. A thin-wall part was fabricated on a V-type substrate. The results demonstrate that the developed cooperative sensing and control strategy can ensure the process stability and realize the deposition height control in robotic P-GTA AM.

As is well known, intelligence and efficiency are important development directions for modern agriculture. The harvesting header, as key components of crop harvesters, have significant implications for achieving intelligent control of their working height, which has a notable impact on reducing harvest loss. To understand the current state of intelligent control technology for the working height of a crop harvesting header, and to explore their application potential, this article provides a relatively systematic literature review. Firstly, we analyzed the structure and principle of the harvesting header of typical grain and oil crops such as rice and peanuts. Secondly, we briefly described the current methods for controlling the working height of the harvesting header. They mainly use two methods: mechanical profiling and electro-hydraulic profiling. Thirdly, we focused on researching and analyzing the measurement methods and control algorithms for the working height of the harvesting header. Finally, we pointed out the problems in the current height control of the harvesting header. These problems mainly include insufficient measurement accuracy of working height in complex terrain, slow response and large delay of working height hydraulic control system, incompatibility between working height control models and strategies, and relatively single working height measurement methods.

To reduce the damages of pavement, vehicle components and agricultural product during transportation, an electric control air suspension height adjustment system of agricultural transport vehicle was studied by means of simulation and bench test. For the oscillation phenomenon of vehicle height in driving process, the mathematical model of the vehicle height adjustment system was developed, and the controller of vehicle height based on single neuron adaptive PID control algorithm was designed. The control model was simulated via Matlab/Simulink, and bench test was conducted. Results show that the method is feasible and effective to solve the agricultural vehicle body height unstable phenomenon in the process of switching. Compared with other PID algorithms, the single neuron adaptive PID control in agricultural transport vehicle has shorter response time, faster response speed and more stable switching state. The stability of the designed vehicle height adjustment system and the ride comfort of agricultural transport vehicle were improved.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov–Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.