Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

Modeling of a Variable-BVR Rotary Valve Free Piston Expander/Compressor

The concept of a free-piston expansion/compression unit with a variable Built-in Volume Ratio (BVR) is proposed. This device has no crankshaft mechanism which provides a possibility to optimize the expansion process free of mechanical limitations. An additional degree of freedom is used, namely the rotation to control the in- and the outlet ports timing. Further, the operation in the expander mode will be described. In most of the existing linear expanders/compressors, bouncing chambers or devices are used to reverse the piston movement at extreme positions. This approach is characterized by relatively high energy losses due to irreversibility of such a process. As an alternative, a fully controlled movement of the piston is proposed. This paper is focused on the control algorithm based on rules, which have been obtained and based on the insight in the system. Including the rotation timing, resulting in an optimal expansion process with an outlet pressure matching with the required one

Identification and path following control of an AR.drone quadrotor

This paper describes the process of identification and closed-loop control of an Parrot AR. Drone Unmanned Aerial Vehicle (UAV) as well as a path following application based on IMC position controllers. The research issue is to achieve position control of the AR.Drone quadrotor movement via its on-board sensory equipment and external webcam video stream. Firstly, transfer functions are detailed for pitch and altitude movements and a comparison is made between implemented PID and IMC controller performance for both simulation and practice. Furthermore, using IMC controllers, a path following application exhibits controller behavior from a practical point of view. It is concluded that the dynamic model and the controllers implemented on the quadrotor can serve as a reliable basis for more advanced applications

Multivariable EPSAC predictive control for organic rankine cycle technology

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Data-driven Modeling and Control of Waste Heat Recovery Organic Rankine Cycle Systems

Humanity has experienced a drastic growth during the last decades, faster than what was experienced in previous centuries. Increment concerns not only the population number but the amount of developments in areas such as computation capacity, automation, robotics, precision agriculture, material science, among others. These innovative solutions bring together a higher energy demand, hence in order to diminish the green house effect, global warming and other impacts caused by industry and oil based transportation system to the natural resources, it is deemed necessary to achieve sustainable and more efficient manufacturing processes. Regarding energy efficiency organic Rankine cycle (ORC) power systems appear as an interesting technology to recover waste heat available at low-grade temperature, thus increasing the overall system efficiency. The ORC unit operates under the same principle as classical Rankine cycle for electricity generation, the main difference being the use of refrigerants with low boiling point as working fluid instead of water, allowing to reach superheated state for low temperature heat source conditions, thus becoming ideal for waste heat recovery (WHR) applications in the range from 100 400 C. The optimal thermodynamic design of such machines represents a first challenge, since cycle architecture, components and refrigerant selection, are an important aspect to look at. However such decisions are often considered at steady-state design, while ORC operates outside the designed range due to varying waste heat profiles, thus making necessary to consider a suitable control strategy aiming to guarantee safety operation and optimal performance during transient conditions. The main contributions of this PhD thesis include: experimental validation of the proposed strategies, open-loop tests to illustrate the dynamics that represent a real challenge for modeling and control, presenting the conditions to achieve optimal ORC operation by building an optimizer, defining gain-scheduling and adaptive strategies based on classical PID and more advanced Model Predictive Control (MPC) to deal with nonlinear time-varying ORC dynamics. In order to provide additional robustness against modeling errors a multiple model predictive controller is designed, where a Bayesian weighting scheme is applied to obtain an average prediction trajectory from a model bank built with models identified at different operating points. In order to better explain the complex ORC dynamics an sparse identification algorithm is proposed aiming to built a global nonlinear description of the process. This research work is thus an attempt to present a user-friendly methodology for waste heat recovery organic Rankine cycle (WHR-ORC) modeling and control trol, a guide for practitioners and researchers interested on understanding from a data-driven perspective why is this power unit a nonlinear time-varying system, how to define a suitable low-order model for control, and how to design an advanced control strategy to achieve optimal performance under drastic waste heat variations. As well as to provide some ideas and future perspectives to optimize the ORC performance.The Next Generation of Organic Rankine Cycles7. Affordable and clean energ