2,443 research outputs found
A reconfigurable hybrid intelligent system for robot navigation
Soft computing has come of age to o er us a wide array of powerful and e cient algorithms
that independently matured and in
uenced our approach to solving problems in robotics,
search and optimisation. The steady progress of technology, however, induced a
ux of new
real-world applications that demand for more robust and adaptive computational paradigms,
tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and
to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms
and neural networks. As noted in the literature, they are signi cantly more powerful than
individual algorithms, and therefore have been the subject of research activities in the past
decades. There are problems, however, that have not succumbed to traditional hybridisation
approaches, pushing the limits of current intelligent systems design, questioning their solutions
of a guarantee of optimality, real-time execution and self-calibration. This work presents an
improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle
avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search
algorithm and the Voronoi diagram generation algorithm
Mobile Robot Feature-Based SLAM Behavior Learning, and Navigation in Complex Spaces
Learning mobile robot space and navigation behavior, are essential requirements for improved navigation, in addition to gain much understanding about the navigation maps. This chapter presents mobile robots feature-based SLAM behavior learning, and navigation in complex spaces. Mobile intelligence has been based on blending a number of functionaries related to navigation, including learning SLAM map main features. To achieve this, the mobile system was built on diverse levels of intelligence, this includes principle component analysis (PCA), neuro-fuzzy (NF) learning system as a classifier, and fuzzy rule based decision system (FRD)
A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS
Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach
Control of a hybrid electric vehicle with predictive journey estimation
Battery energy management plays a crucial role in fuel economy improvement of
charge-sustaining parallel hybrid electric vehicles. Currently available control strategies
consider battery state of charge (SOC) and driver’s request through the pedal input in
decision-making. This method does not achieve an optimal performance for saving fuel
or maintaining appropriate SOC level, especially during the operation in extreme
driving conditions or hilly terrain. The objective of this thesis is to develop a control
algorithm using forthcoming traffic condition and road elevation, which could be fed
from navigation systems. This would enable the controller to predict potential of
regenerative charging to capture cost-free energy and intentionally depleting battery
energy to assist an engine at high power demand.
The starting point for this research is the modelling of a small sport-utility vehicle by
the analysis of the vehicles currently available in the market. The result of the analysis
is used in order to establish a generic mild hybrid powertrain model, which is
subsequently examined to compare the performance of controllers. A baseline is
established with a conventional powertrain equipped with a spark ignition direct
injection engine and a continuously variable transmission. Hybridisation of this vehicle
with an integrated starter alternator and a traditional rule-based control strategy is
presented. Parameter optimisation in four standard driving cycles is explained, followed
by a detailed energy flow analysis.
An additional potential improvement is presented by dynamic programming (DP),
which shows a benefit of a predictive control. Based on these results, a predictive
control algorithm using fuzzy logic is introduced. The main tools of the controller
design are the DP, adaptive-network-based fuzzy inference system with subtractive
clustering and design of experiment. Using a quasi-static backward simulation model,
the performance of the controller is compared with the result from the instantaneous
control and the DP. The focus is fuel saving and SOC control at the end of journeys,
especially in aggressive driving conditions and a hilly road. The controller shows a
good potential to improve fuel economy and tight SOC control in long journey and hilly
terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap
between the baseline controller and the DP. However, there is little benefit in short trips
and flat road. It is caused by the low improvement margin of the mild hybrid powertrain
and the limited future journey information.
To provide a further step to implementation, a software-in-the-loop simulation model is
developed. A fully dynamic model of the powertrain and the control algorithm are
implemented in AMESim-Simulink co-simulation environment. This shows small
deterioration of the control performance by driver’s pedal action, powertrain dynamics
and limited computational precision on the controller performance
Mobile Robots Navigation
Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described
1999 Flight Mechanics Symposium
This conference publication includes papers and abstracts presented at the Flight Mechanics Symposium held on May 18-20, 1999. Sponsored by the Guidance, Navigation and Control Center of Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude determination error analysis; attitude dynamics; and orbit decay and maneuver strategy. Government, industry, and the academic community participated in the preparation and presentation of these papers
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