Chapter 8 establishes the relationship between the input and output power spectral densities of a linear system. Limitations on results are carefully detailed and the case of oscillator noise is considered. The theory is extended to multiple input-multiple output systems.

Linear System Theory

This paper reports a comparative experimental evaluation of one model-free proportional derivative (PD) three degree-of-freedom (DOF) controller and two model-based three-DOF controllers designed to enable low-speed, neutrally buoyant, and fully actuated underwater vehicles to perform trajectory tracking in the X, Y, and heading DOFs. We show the model-free PD controller provides locally asymptotically stable set-point regulation. We show the model-based controllers provide locally asymptotically stable trajectory tracking. The reported controllers were experimentally evaluated on the Johns Hopkins University remotely operated vehicle. We report the model-based controller’s mean absolute position and velocity tracking error is significantly smaller than the model-free PD controller for coupled maneuvers. We report the fixed, model-based controllers performed best using a parameter model including fully parameterized quadratic drag model parameters, and that the choice of parameter model has a significant effect on the performance of the model-based controllers during disparate experimental trials.

Nonlinear Model-Based Tracking Control of Underwater Vehicles With Three Degree-of-Freedom Fully Coupled Dynamical Plant Models: Theory and Experimental Evaluation

Large oil and gas pipelines are prone to corrosion and leakage, so in-pipe inspection is necessary. In this article, we show a novel robot mechanism for long-distance pipeline inspection. The robot consists of three crawlers and electric putters, which can adjust their speed and radius independently. Independent adjustment and system self-checking of the robot are achieved through multiple sensors. To make the robot operate efficiently, we studied the influence of size parameters on the forces between the central body and crawler. Moreover, we investigated how to adjust the attitude of the robot through the differential speed of the three crawlers. Static and dynamic simulations of internal forces are presented. The primary experiments indicate that our robot can operate stably in a large steel pipe.

Design and Analysis of Independently Adjustable Large In-Pipe Robot for Long-Distance Pipeline

Optimal Operation of the Hydrogen-based Energy Management System with P2X Demand Response and Ammonia Plant

Fault tolerant control of UMV based on sliding mode output feedback

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Smart-Spider: Autonomous self-driven in-line robot for versatile pipeline inspection

This paper presents a nonlinear input-state feedback linearization controller for tracking trajectory on horizontal plane with the rudder of an unmanned underwater vehicle (UUV). UUV system is strongly nonlinear, but the model is often simplified into linearization under some strict assumptions in some traditional linear control methods for the need of the control laws, and the linearization may induce large modeling errors and cause severe problems in the practical applications. In this paper, an input-state feedback linearization controller is designed to transform the nonlinear UUV model into an equivalent linear model. The trajectory tracking system is confirmed to be stable and UUV tracks trajectory approximately by pole placement through properly choosing the virtual input. Simulation results illustrate the system is stable and has robust with proposed control scheme.

Nonlinear feedback control for trajectory tracking of an unmanned underwater vehicle

The increasing penetration of renewables in power systems urgently entails the utilization of energy storage technologies. As the development of energy storage technologies depends highly on the profitability in electricity markets, to evaluate the economic potentials for various types of energy storage technologies under the comprehensive market environment is of great significance. To this end, this paper aims at conducting a quantitative analysis on the economic potentials for typical energy storage technologies by establishing a joint clearing model for electric energy and ancillary service (AS) markets considering the operating features of energy storage systems (ESSs). Furthermore, a test system is adopted for numerical analysis that accurately represents for the real-world operation characteristics of power systems in China, with which the market prices, and operation schedules and profitability of ESSs are comparatively studied. The proposed methodology and results could provide beneficial references for the modifications on electricity markets and the development of ESSs towards the increasing penetration of renewables in power systems. 

Economic Potentials of Energy Storage Technologies in Electricity Markets with Renewables

Photosynthesis is a crucial biochemical process for plant growth, which is determined by multiple environmental factors and other organic matter. In the horticultural industry, the environmental conditions in commercial greenhouses directly impact the quality of productions. Predicting the Net Photosynthesis (Pn) of plants based on the environmental parameters can help growers optimize the climate in greenhouse systems, thereby ensuring the quality of production. Meanwhile, due to the greenhouse climate can be controlled according to the prediction results, excess energy consumption can be avoided, so the production cost can be reduced. However, since the photosynthesis reaction is a highly nonlinear biochemical process, it is difficult for traditional algorithms to describe the hidden effects of individual elements. In previous related works, polynomial fitting was utilized for modeling the relation between Pn and environmental elements. In this paper, a Deep Learning (DL) method is explored to predict the Pn based on three inputs: light level, CO2 concentration and temperature. An exponential decay learning rate is applied in the training process to ensure convergence performance while increasing the convergence speed. Then, the performance of various Deep Neural Network (DNN) architectures is experimented and compared within this modeling problem. Finally, through a comprehensive analysis of the accuracy of individual architecture, a particular architecture is determined to solve this problem. According to the test results, the selected DNN can successfully predict the Pn based on the three environmental elements with high accuracy.

Application of Deep Neural Network on Net Photosynthesis Modeling

The amount of net photosynthesis of leaves is a significate factor for the growth of plants. Therefore, monitoring the real-time net photosynthesis plays an essential role in improving the quality of productions in commercial greenhouses. Net photosynthesis mainly depends on three environmental parameters, that are light level, temperature and CO2 concentration. However, it is challenging to calculate accurate net photosynthesis due to the highly nonlinear relation. In this paper, Deep Learning (DL) is utilized to model this relationship in order to predict the net photosynthesis based on the three inputs. Firstly, the architecture of a Deep Neural Network (DNN) model is designed according to the features of this problem, and three activation functions are concerned for the DNN model design. Secondly, a training dataset is established, and two schedules of Learning Rate (LR), fixed LR and exponential decay LR, are elaborated. Then, to select the optimal hyperparameters for the DNN model, experiments of hyperparameters tuning related to activation functions and LR schedules are implemented, respectively. Finally, through a comprehensive evaluation of the training speed and the prediction accuracy, a DNN model that is with ReLU activation function and decay LR is determined. This DNN model can perform a dramatically high prediction accuracy in a fast training convergence speed for solving the proposed net photosynthesis prediction problem.

Ying Qu

Papers

Smart-Spider: Autonomous self-driven in-line robot for versatile pipeline inspection

Nonlinear feedback control for trajectory tracking of an unmanned underwater vehicle

Economic Potentials of Energy Storage Technologies in Electricity Markets with Renewables

Application of Deep Neural Network on Net Photosynthesis Modeling

Net Photosynthesis Prediction by Deep Learning for Commercial Greenhouse Production