13 research outputs found
Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning
In recent years, advancements have been made towards the goal of using
chaotic coverage path planners for autonomous search and traversal of spaces
with limited environmental cues. However, the state of this field is still in
its infancy as there has been little experimental work done. Current
experimental work has not developed robust methods to satisfactorily address
the immediate set of problems a chaotic coverage path planner needs to overcome
in order to scan realistic environments within reasonable coverage times. These
immediate problems are as follows: (1) an obstacle avoidance technique which
generally maintains the kinematic efficiency of the robot's motion, (2) a means
to spread chaotic trajectories across the environment (especially crucial for
large and/or complex-shaped environments) that need to be covered, and (3) a
real-time coverage calculation technique that is accurate and independent of
cell size. This paper aims to progress the field by proposing algorithms that
address all of these problems by providing techniques for obstacle avoidance,
chaotic trajectory dispersal, and accurate coverage calculation. The algorithms
produce generally smooth chaotic trajectories and provide high scanning
coverage of environments. These algorithms were created within the ROS
framework and make up a newly developed chaotic path planning application. The
performance of this application was comparable to that of a conventional
optimal path planner. The performance tests were carried out in environments of
various sizes, shapes, and obstacle densities, both in real-life and Gazebo
simulations
Nonlinear Dynamic Model-Based Adaptive Control of a Solenoid-Valve System
In this paper, a nonlinear model-based adaptive control approach is proposed for a solenoid-valve system. The challenge is that solenoids and butterfly valves have uncertainties in multiple parameters in the nonlinear model; various kinds of physical appearance such as size and stroke, dynamic parameters including inertia, damping, and torque coefficients, and operational parameters especially, pipe diameters and flow velocities. These uncertainties are making the system not only difficult to adjust to the environment, but also further complicated to develop the appropriate control approach for meeting the system objectives. The main contribution of this research is the application of adaptive control theory and Lyapunov-type stability approach to design a controller for a dynamic model of the solenoid-valve system in the presence of those uncertainties. The control objectives such as set-point regulation, parameter compensation, and stability are supposed to be simultaneously accomplished. The error signals are first formulated based on the nonlinear dynamic models and then the control input is developed using the Lyapunov stability-type analysis to obtain the error bounded while overcoming the uncertainties. The parameter groups are updated by adaptation laws using a projection algorithm. Numerical simulation results are shown to demonstrate good performance of the proposed nonlinear model-based adaptive approach and to compare the performance of the same solenoid-valve system with a non-adaptive method as well
Underactuated Source Seeking by Surge Force Tuning: Theory and Boat Experiments
We extend source seeking algorithms, in the absence of position and velocity
measurements, and with tuning of the surge input, from velocity-actuated
(unicycle) kinematic models to force-actuated generic Euler-Lagrange dynamic
underactuated models. In the design and analysis, we employ a symmetric product
approximation, averaging, passivity, and partial-state stability theory. The
proposed control law requires only real-time measurement of the source signal
at the current position of the vehicle and ensures semi-global practical
uniform asymptotic stability (SPUAS) with respect to the linear motion
coordinates for the closed-loop system. The performance of our source seeker
with surge force tuning is illustrated with both numerical simulations and
experiments of an underactuated boat
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm
The primary focus of this paper is centered on the numerical analysis and
optimal control of vertical axis wind turbines (VAWT) using Bayesian
reinforcement learning (RL). We specifically tackle small-scale wind turbines
with permanent magnet synchronous generator, which are well-suited to local and
compact production of electrical energy in small scale such as urban and rural
infrastructure installations. Through this work, we formulate and implement an
RL strategy using Markov chain Monte Carlo (MCMC) algorithm to optimize the
long-term energy output of the wind turbine. Our MCMC-based RL algorithm is a
model-free and gradient-free algorithm, where the designer does not have to
know the precise dynamics of the plant and their uncertainties. The method
specifically overcomes the shortcomings typically associated with conventional
solutions including but not limited to component aging, modeling errors and
inaccuracies in the estimation of wind speed patterns. It has been observed to
be especially successful in capturing power from wind transients; it modulates
the generator load and hence rotor torque load so that the rotor tip speed
reaches the optimum value for the anticipated wind speed. This ratio of rotor
tip speed to wind speed is known to be critical in wind power applications. The
wind to load energy efficiency of the proposed method is shown to be superior
to the classical maximum power point tracking method
An adaptive and energy-maximizing control of wave energy converters using extremum-seeking approach
In this paper, we systematically investigate the feasibility of different
extremum-seeking (ES) control schemes to improve the conversion efficiency of
wave energy converters (WECs). Continuous-time and model-free ES schemes based
on the sliding mode, relay, least-squares gradient, self-driving, and
perturbation-based methods are used to improve the mean extracted power of a
heaving point absorber subject to regular and irregular waves. This objective
is achieved by optimizing the resistive and reactive coefficients of the power
take-off (PTO) mechanism using the ES approach. The optimization results are
verified against analytical solutions and the extremum of reference-to-output
maps. The numerical results demonstrate that except for the self-driving ES
algorithm, the other four ES schemes reliably converge for the two-parameter
optimization problem, whereas the former is more suitable for optimizing a
single-parameter. The results also show that for an irregular sea state, the
sliding mode and perturbation-based ES schemes have better convergence to the
optimum, in comparison to other ES schemes considered here. The convergence of
PTO coefficients towards the performance-optimal values are tested for widely
different initial values, in order to avoid bias towards the extremum. We also
demonstrate the adaptive capability of ES control by considering a case in
which the ES controller adapts to the new extremum automatically amidst changes
in the simulated wave conditions
Nonlinear Model-based Adaptive Control Of A Solenoid-Valve System
In this paper, a model-based control algorithm is developed for a solenoid-valve system. Solenoids and butterfly valves have uncertainties in multiple parameters in the model, which make the system difficult to adjust to the environment. These are further complicated by combining the solenoid and butterfly dynamic models. The control objective of a solenoid-valve system is to position the angle of the butterfly valve through the electric-driven actuator in spite of the complexity presented by uncertainties. The novelty of the controller design is that the current source of the solenoid valve from the model of the electromagnetic force is substituted for the control input in order to reach the set-point of the butterfly disk based on the error signals, overcoming the uncertainties represented by lumped parameters groups, and a stable controller is designed via the Lyapunov-based approach for the stability of the system and obtaining the control objective. The parameter groups are updated by adaptation laws using a projection algorithm. Numerical simulation is shown to demonstrate good performance of the proposed approach
Coupled Operational Optimization of Smart Valve System Subject to Different Approach Angles of a Pipe Contraction
In this paper, we focus on interconnected trajectory optimization of two sets of solenoid actuated butterfly valves dynamically coupled in series. The system undergoes different approach angles of a pipe contraction as a typical profile of the so-called “Smart Valves” network containing tens of actuated valves. A high fidelity interconnected mathematical modeling process is derived to reveal the expected complexity of such a multiphysics system dealing with electromagnetics, fluid mechanics, and nonlinear dynamic effects. A coupled operational optimization scheme is formulated in order to seek the most efficient trajectories of the interconnected valves minimizing the energy consumed enforcing stability and physical constraints. We examine various global optimization methods including Particle Swarm, Simulated Annealing, Genetic, and Gradient based algorithms to avoid being trapped in several possible local minima. The effect of the approach angles of the pipeline contraction on the amount of energy saved is discussed in detail. The results indicate that a substantial amount of energy can be saved by an intelligent operation that uses flow torques to augment the closing efforts