333 research outputs found
Flight Data Validation of an Icing Accretion Estimation Scheme using Super-twisting Observers
This paper develops a generalised multivariable super-twisting observer for a class of nonlinear systems in which the unmeasured variables linked to the known state dependent matrix function appear multiplicatively. A sufficient condition is given to guarantee that the reconstruction errors associated with the unmeasurable variables converge to zero in finite time. This approach is then used to address the aircraft icing accretion estimation problem despite unreliable sensor measurement. The efficacy of the approach has been evaluated via real flight data recorded under natural icing conditions. Results show that the observer has the capability to estimate the change of the drag coefficient induced by icing accretion and to reconstruct the unreliable pitch rate sensor measurement simultaneously
Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges
Continuum soft robots are mechanical systems entirely made of continuously
deformable elements. This design solution aims to bring robots closer to
invertebrate animals and soft appendices of vertebrate animals (e.g., an
elephant's trunk, a monkey's tail). This work aims to introduce the control
theorist perspective to this novel development in robotics. We aim to remove
the barriers to entry into this field by presenting existing results and future
challenges using a unified language and within a coherent framework. Indeed,
the main difficulty in entering this field is the wide variability of
terminology and scientific backgrounds, making it quite hard to acquire a
comprehensive view on the topic. Another limiting factor is that it is not
obvious where to draw a clear line between the limitations imposed by the
technology not being mature yet and the challenges intrinsic to this class of
robots. In this work, we argue that the intrinsic effects are the continuum or
multi-body dynamics, the presence of a non-negligible elastic potential field,
and the variability in sensing and actuation strategies.Comment: 69 pages, 13 figure
Flight data validation of an icing accretion estimation scheme using super-twisting observers
This paper develops a generalised multivariable super-twisting observer for a class of nonlinear systems in which the unmeasured variables linked to the known state dependent matrix function appear multiplicatively. A sufficient condition is given to guarantee that the reconstruction errors associated with the unmeasurable variables converge to zero in finite time. This approach is then used to address the aircraft icing accretion estimation problem despite unreliable sensor measurement. The efficacy of the approach has been evaluated via real flight data recorded under natural icing conditions. Results show that the observer has the capability to estimate the change of the drag coefficient induced by icing accretion and to reconstruct the unreliable pitch rate sensor measurement simultaneously
A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles
Vehicle control is one of the most critical challenges in autonomous vehicles
(AVs) and connected and automated vehicles (CAVs), and it is paramount in
vehicle safety, passenger comfort, transportation efficiency, and energy
saving. This survey attempts to provide a comprehensive and thorough overview
of the current state of vehicle control technology, focusing on the evolution
from vehicle state estimation and trajectory tracking control in AVs at the
microscopic level to collaborative control in CAVs at the macroscopic level.
First, this review starts with vehicle key state estimation, specifically
vehicle sideslip angle, which is the most pivotal state for vehicle trajectory
control, to discuss representative approaches. Then, we present symbolic
vehicle trajectory tracking control approaches for AVs. On top of that, we
further review the collaborative control frameworks for CAVs and corresponding
applications. Finally, this survey concludes with a discussion of future
research directions and the challenges. This survey aims to provide a
contextualized and in-depth look at state of the art in vehicle control for AVs
and CAVs, identifying critical areas of focus and pointing out the potential
areas for further exploration
Investigation into sustainable technologies for mitigating urban heat island effects in subtropical monsoon climate
The urban heat island (UHI) is a concerning environmental phenomenon, and mitigation strategies have been proposed to reduce its adverse effects. This study conducted a literature review of UHI and UHI mitigation strategies, finding that urban cooling technologies such as green roofs, cool roofs, and urban vegetation can comprehensively affect meteorological parameters (temperature, sky view factor, radiation, etc.), urban building energy use, carbon emissions and improve human comfort. Additionally, thermal energy storage technologies were also reviewed, with a focus on mitigating UHI. The study assessed the effectiveness of conventional and thermal energy storage-based UHI mitigation strategies through meteorological simulations using the software ENVI-met and a novel model called UHIMS-ECHE. The simulation results showed that conventional UHI mitigation scenarios can reduce UHI intensity, building cooling energy use, carbon emissions, and improve human thermal comfort. Moreover, the integration of phase change materials (PCMs) and photovoltaic (PV) systems was analysed by the UHIMS-ECHE model, which demonstrated that the integration of PCM and PV technologies can significantly reduce UHI, improve energy efficiency, and enhance human thermal comfort in urban environments. A case study has been conducted in Osaka, Japan, which is a typical city under subtropical monsoon weather condition. Consequently, the PCM-Roof -A36H -10cm model reduced outdoor air temperatures by up to 7.09°C and total urban building cooling energy use cooling loads by up to 23.68% compared to the baseline case. These findings provide insights for policymakers, urban planners, and building designers to create sustainable urban environments. Further research is recommended to investigate the feasibility and cost-effectiveness of these technologies in different urban contexts and climatic conditions
Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots
This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative.
Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set.
Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance.
Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference.
Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles
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