1,195 research outputs found
Advanced Mathematics and Computational Applications in Control Systems Engineering
Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
Technical Report on: Tripedal Dynamic Gaits for a Quadruped Robot
A vast number of applications for legged robots entail tasks in complex,
dynamic environments. But these environments put legged robots at high risk for
limb damage. This paper presents an empirical study of fault tolerant dynamic
gaits designed for a quadrupedal robot suffering from a single, known
``missing'' limb. Preliminary data suggests that the featured gait controller
successfully anchors a previously developed planar monopedal hopping template
in the three-legged spatial machine. This compositional approach offers a
useful and generalizable guide to the development of a wider range of tripedal
recovery gaits for damaged quadrupedal machines.Comment: Updated *increased font size on figures 2-6 *added a legend, replaced
text with colors in figure 5a and 6a *made variables representing vectors
boldface in equations 8-10 *expanded on calculations in equations 8-10 by
adding additional lines *added a missing "2" to equation 8 (typo) *added mass
of the robot to tables II and III *increased the width of figures 1 and
Geometric and Feedback Linearization on UAV: Review
The pervasive integration of Unmanned Aerial Vehicles (UAVs) across
multifarious domains necessitates a nuanced understanding of control
methodologies to ensure their optimal functionality. This exhaustive review
meticulously examines two pivotal control paradigms in the UAV landscape,
Geometric Control and Feedback Linearization. Delving into the intricate
theoretical underpinnings, practical applications, strengths, and challenges of
these methodologies, the paper endeavors to provide a comprehensive overview.
Geometric Control, grounded in the principles of differential geometry, offers
an elegant and intuitive approach to trajectory tracking and mission execution.
In contrast, Feedback Linearization employs nonlinear control techniques to
linearize UAV dynamics, paving the way for enhanced controllability. This
review not only dissects the theoretical foundations but also scrutinizes
real-world applications, integration challenges, and the ongoing research
trajectory of Geometric Control and Feedback Linearization in the realm of
UAVs
Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree
Legged robots can pass through complex field environments by selecting gaits
and discrete footholds carefully. Traditional methods plan gait and foothold
separately and treat them as the single-step optimal process. However, such
processing causes its poor passability in a sparse foothold environment. This
paper novelly proposes a coordinative planning method for hexapod robots that
regards the planning of gait and foothold as a sequence optimization problem
with the consideration of dealing with the harshness of the environment as leg
fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the
entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve
some defeats of the standard MCTS applicating in the field of legged robot
planning. The proposed planning algorithm combines the fault-tolerant gait
method to improve the passability of the algorithm. Finally, compared with
other planning methods, experiments on terrains with different densities of
footholds and artificially-designed challenging terrain are carried out to
verify our methods. All results show that the proposed method dramatically
improves the hexapod robot's ability to pass through sparse footholds
environment
Challenges in Controllers on UAV Aircraft: Theory and Practice
This review explores the theoretical foundations and experimental dynamics of
modern tiltrotor aircraft. Emphasizing feedback linearization, the study delves
into the distinctive constraints and angular velocity ranges shaping tiltrotor
behavior. Experimental findings highlight challenges in tracking circular
trajectories, with color-coded representations illustrating the impact of
angular velocity. Practical implications for applications like unmanned aerial
vehicles are discussed, alongside identified challenges and avenues for future
research. This work contributes to both theoretical understanding and practical
considerations in the evolving field of tiltrotor control
Advanced Feedback Linearization Control for Tiltrotor UAVs: Gait Plan, Controller Design, and Stability Analysis
Three challenges, however, can hinder the application of Feedback
Linearization: over-intensive control signals, singular decoupling matrix, and
saturation. Activating any of these three issues can challenge the stability
proof. To solve these three challenges, first, this research proposed the drone
gait plan. The gait plan was initially used to figure out the control problems
in quadruped (four-legged) robots; applying this approach, accompanied by
Feedback Linearization, the quality of the control signals was enhanced. Then,
we proposed the concept of unacceptable attitude curves, which are not allowed
for the tiltrotor to travel to. The Two Color Map Theorem was subsequently
established to enlarge the supported attitude for the tiltrotor. These theories
were employed in the tiltrotor tracking problem with different references.
Notable improvements in the control signals were witnessed in the tiltrotor
simulator. Finally, we explored the control theory, the stability proof of the
novel mobile robot (tilt vehicle) stabilized by Feedback Linearization with
saturation. Instead of adopting the tiltrotor model, which is over-complicated,
we designed a conceptual mobile robot (tilt-car) to analyze the stability
proof. The stability proof (stable in the sense of Lyapunov) was found for a
mobile robot (tilt vehicle) controlled by Feedback Linearization with
saturation for the first time. The success tracking result with the promising
control signals in the tiltrotor simulator demonstrates the advances of our
control method. Also, the Lyapunov candidate and the tracking result in the
mobile robot (tilt-car) simulator confirm our deductions of the stability
proof. These results reveal that these three challenges in Feedback
Linearization are solved, to some extents.Comment: Doctoral Thesis at The University of Toky
Generalized Two Color Map Theorem -- Complete Theorem of Robust Gait Plan for a Tilt-rotor
Gait plan is a procedure that is typically applied on the ground robots,
e.g., quadrupedal robots; the tilt-rotor, a novel type of quadrotor with eight
inputs, is not one of them. While controlling the tilt-rotor relying on
feedback linearization, the tilting angles (inputs) are expected to change
over-intensively, which may not be expected in the application. To help
suppress the intensive change in the tilting angles, a gait plan procedure is
introduced to the tilt-rotor before feedback linearization. The tilting angles
are specified with time in advance by users rather than given by the control
rule. However, based on this scenario, the decoupling matrix in feedback
linearization can be singular for some attitudes, combinations of roll angle
and pitch angle. It hinders the further application of the feedback
linearization. With this concern, Two Color Map Theorem is established to
maximize the acceptable attitude region, where the combinations of roll and
pitch will give an invertible decoupling matrix. That theorem, however,
over-restricts the choice of the tilting angles, which can rule out some
feasible robust gaits. This paper gives the generalized Two Color Map Theorem;
all the robust gaits can be found based on this generalized theorem. The
robustness of three gaits that satisfy this generalized Two Color Map Theorem
(while violating Two Color Map Theorem) are analyzed. The results show that
Generalized Two Color Map Theorem completes the search for the robust gaits for
a tilt-rotor
Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans
The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product
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