Predictive Neural Network Modeling for Almond Harvest Dust Control.

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network's architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Design of an Integrated Controller for a Sweeping Mechanism of a Low-Dust Almond Pickup Machine.

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.

A reinforcement learning-based near-optimal hierarchical approach for motion control: Design and experiment.

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics.

Optimal full-state feedback observer integrated backstepping control of chemical processes with unknown internal dynamics.

This paper studies the estimation and control problems of chemical processes with unknown internal dynamics. An observer with optimal full-state feedback characteristics for estimating the state variables and unknown dynamics is presented. Unlike other observers that need to know the frequency characteristics of the system, the pole of the proposed observer is determined automatically in a LQR formulation and the observer stability is also inherently ensured. In order to suppress the unknown internal dynamics, the proposed observer is then applied to the control design leading to an observer integrated backstepping control method. The proposed method does not depend on the detailed mathematical model of the system while the stability of the closed-loop system is guaranteed. The stability of the closed-loop system is proven in the Lyapunov sense. Extensive numerical simulations are presented to validate the proposed method.

New periodic-chaotic attractors in slow-fast Duffing system with periodic parametric excitation.

A new type of responses called as periodic-chaotic motion is found by numerical simulations in a Duffing oscillator with a slowly periodically parametric excitation. The periodic-chaotic motion is an attractor, and simultaneously possesses the feature of periodic and chaotic oscillations, which is a new addition to the rich nonlinear motions of the Duffing system including equlibria, periodic responses, quasi-periodic oscillations and chaos. In the current slow-fast Duffing system, we find three new attractors in the form of periodic-chaotic motions. These are called the fixed-point chaotic attractor, the fixed-point strange nonchaotic attractor, and the critical behavior with the maximum Lyapunov exponent fluctuating around zero. The system periodically switches between one attractor with a fixed single-well potential and the other with time-varying two-well potentials in every period of excitation. This behavior is apparently the mechanism to generate the periodic-chaotic motion.

A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems.

This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window along with a piecewise linear model to estimate the RUL for each mechanical component. Tuning the data-related parameters in the optimization framework allows for the use of simple models, e.g. neural networks with few hidden layers and few neurons at each layer, which may be deployed in environments with limited resources such as embedded systems. The proposed method is evaluated on the publicly available C-MAPSS dataset. The accuracy of the proposed method is compared against other state-of-the art methods in the literature. The proposed method is shown to perform better than the compared methods while making use of a compact model.

Adaptive force regulation of muscle strengthening rehabilitation device with magnetorheological fluids.

In rehabilitation from neuromuscular trauma or injury, strengthening exercises are often prescribed by physical therapists to recover as much function as possible. Strengthening equipment used in clinical settings range from low-cost devices, such as sandbag weights or elastic bands to large and expensive isotonic and isokinetic devices. The low-cost devices are incapable of measuring strength gains and apply resistance based on the lowest level of torque that is produced by a muscle group. Resistance that varies with joint angle can be achieved with isokinetic devices in which angular velocity is held constant and variable torque is generated when the patient attempts to move faster than the device but are ineffective if a patient cannot generate torque rapidly. In this paper, we report the development of a versatile rehabilitation device that can be used to strengthen different muscle groups based on the torque generating capability of the muscle that changes with joint angle. The device is low cost, is smaller than other commercially available machines, and can be programmed to apply resistance that is unique to a particular patient and that will optimize strengthening. The core of the device, a damper with smart magnetorheological fluids, provides passive exercise force. A digital adaptive control is capable of regulating exercise force precisely following the muscle strengthening profile prescribed by a physical therapist. The device could be programmed with artificial intelligence to dynamically adjust the target force profile to optimize rehabilitation effects. The device provides both isometric and isokinetic strength training and can be developed into a small, low-cost device that may be capable of providing optimal strengthening in the home.