Adaptive Variable Structure Observer for System States and Disturbances Estimation with Application to Building Climate Control System in a Smart Grid

In order to reach the ambitious net-zero emission target by 2050, various technological solutions need to be developed to ensure efficient utilisation of energy. Commercial and residential buildings are a big source of greenhouse gas emissions, where efficient utilisation of energy can play a major role towards decarbonisation of the buildings sector. Heat pumps have recently emerged as an effective solution for space heating applications in buildings. Energy-efficient operation of heat pumps will make a significant contribution toward making buildings energy-efficient. In this context, heat pump control systems have a major role. Some of the existing literature on the heat pump control systems assume that various system states are available to measure. This may not always be true and/or economical to measure all the states. Moreover, the system is subject to various disturbances which cannot be directly measured. To reduce the number of sensors in heat pump control systems, an adaptive observer is developed in this paper to estimate inaccessible system states and disturbances simultaneously. An advantage of the proposed approach is that it does not require any bound on the disturbance itself, however, only assumes that the rate of change of disturbance is bounded. This is always the case in practice. In the developed method, adaptive control techniques and variable structure control techniques are combined to implement the proposed observer. In order to estimate the unknown disturbance, an augmented systems model is considered. Globally uniformly ultimately bounded property of the error dynamical systems is established by suitably designing the adaptive laws. The developed method is applied to a model of the heat dynamics of a house floor heating system connected to a ground source-based heat pump. Different disturbance signals formats and amplitudes are considered to show the effectiveness of the proposed technique. Simulation results are given to demonstrate the suitability of the proposed method

State and fault estimation scheme based on sliding mode observer for a Lithium-ion battery

In electric vehicles, voltage and temperature sensors installed at the battery cell level or pack level are crucial for providing accurate information so the battery management system (BMS) can perform its functions properly. In this paper, a model-based sensor fault estimation scheme using a sliding mode technique has been proposed. Voltage and temperature models have been developed for a Lithium-ion battery cell. Then, a sliding mode observer has been proposed to estimate the systems’ states as well as sensors fault signals independently and simultaneously. Nissan Leaf Gen4 2018 Lithium-ion cells have been selected to evaluate the performance of the proposed estimation scheme. Simulation results under different test scenarios have confirmed the feasibility and effectiveness of the developed method

Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review

This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey