Adaptive control systems

Today’s mainstream vehicles are partially automated via an Advanced Driver Assistance Feature (ADAS) known as Adaptive Cruise Control (ACC). ACC relies on data from onboard sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations for automated vehicles, ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. Fortunately, when ACC is paired with fully electric vehicles (EVs), EV’s unique powertrain characteristics such as instantaneous torque and aggressive regenerative braking could allow ACC to adopt shorter headways and accelerate more swiftly to maintain shorter headways during queue discharge, therefore reverse the negative impact on capacity. This has been verified in a series of car following field experiments. Field experiments demonstrate that EVs with ACC can achieve a capacity as high as 3333 veh/hr/lane when cruising in steady state conditions at typical freeway speeds (60 mph and 55 mph) and arterial speeds (45 mph and 35 mph). Furthermore, speed fluctuations and disturbances that may come from queues forming at or near the bottleneck do not reduce the capacity, unlike ACC-equipped internal combustion engine (ICE) vehicles, making ACC-equipped EVs outperform ICE vehicles with ACC, as well as human drivers.

One of the limiting factors restricting aircraft landings at maJor airports is the
minimum spacing requirements due to vortex wake avoidance. If it can be shown that the
separation requirements are too conservative, then it may be possible to increase the rate
of landings on a given runway. During August/September 2003, NASA and the (United
States Department of Transportation) USDOT sponsored a wake acoustics test at the
Denver International Airport. The central instrument of the test was a large microphone
phased array. Different types of aircrafts were recorded during landing and the acoustic
data obtained was stored. From acoustic data the spectrograms were generated using the
technique of AutoRegressive (AR) spectral estimation from multitaper autocorrelation
estimates.
Several sources of sound that are recorded in the audio files can be observed in the
spectrograms. Some these signals, such as the noise generated from the aircraft engine can be identified easily because of their strength and the Doppler shift they undergo. In
contrast to this, the wake vortex signal is weaker and does not exhibit a Doppler shift
because it's stationary in space. Therefore it may not be identified easily because of the
existence of stronger signals. The motive in our research is to develop methods to
determine these strong signals that appear as spectral lines in the spectrogram. In the
future, the results obtained in this work can be used to eliminate these strong signals from
the spectrogram thus allowing us to see and identify wake vortex signal which is more
important to us.

Macroscopic fundamental diagram is the concept of the highest importance in traffic flow theory used for development of network-wide control strategies. Previous studies showed that so called Arterial Fundamental Diagrams (AFDs) properly depict relationships between major macroscopic traffic variables on urban arterials. Most of these studies used detector’s occupancy as a surrogate measure to represent traffic density. Nevertheless, detector’s occupancy is not very often present in the field data. More frequently, field data from arterial streets provide performance metrics measured at the stop lines of traffic signals, which represent a hybrid of flow and occupancy. When such performance measures are used in lieu of density, the outcomes of the relationships between macroscopic fundamental variables can be confusing. This study investigates appropriateness of using degree of saturation, as a representative surrogate measure of traffic density, obtained from an adaptive traffic control system that utilizes stop-line detectors, for development of AFDs.

This thesis concerns the design, construction, control, and testing of a novel self-contained soft robotic vehicle; the JenniFish is a free-swimming jellyfish-like soft robot that could be adapted for a variety of uses, including: low frequency, low power sensing applications; swarm robotics; a STEM classroom learning resource; etc. The final vehicle design contains eight PneuNet-type actuators radially situated around a 3D printed electronics canister. These propel the vehicle when inflated with water from its surroundings by impeller pumps; since the actuators are connected in two neighboring groups of four, the JenniFish has bi-directional movement capabilities. Imbedded resistive flex sensors provide actuator position to the vehicle’s PD controller. Other onboard sensors include an IMU and an external temperature sensor. Quantitative constrained load cell tests, both in-line and bending, as well as qualitative free-swimming video tests were conducted to find baseline vehicle performance capabilities. Collected metrics compare well with existing robotic jellyfish.

This report describes the development of a low cost open source semiautonomous
robotic car and a way to communicate with it. It is a continuation of
prior research done by other students at FAU and published in recent ASEE
conferences.
The objective of this project was the development of a new robotic
platform with improved precision over the original, while still keeping the cost
down. It was developed with the aim to allow a hands-on approach to the
teaching of mathematics topics that are taught in the K-12 syllabus.
Improved robustness and reliability of the robotic platform for visually
solving math problems was achieved using a combination of PID loops to keep
track of distance and rotation. The precision was increased by changing the
position of the encoders to the shafts of each motor. A mobile application was developed to allow the student to draw the
geometric shapes on the screen before the car draws them. The mobile
application consists of two parts, the canvas that the user uses to draw the figure
and the configure section that lets the user change the parameters of the
controller.
Results show that the robot can draw standard geometric and complex
geometric shapes. It has high precision and sufficient accuracy, the accuracy can
be improved with some mechanical adjustments. During testing a Pythagorean
triangle was drawn to show visually the key mathematics concept.
The eventual goal of this project will be a K-12 class room study to obtain
the feedback of the teachers and students on the feasibility of using a robotic car
to teach math. Subsequent to that necessary changes will be made to
manufacture a unit that is easy to assemble by the teacher.

novel approach to extend the decision-making capabilities of unmanned surface vehicles
(USVs) is presented in this work. A multi-objective framework is described where separate
controllers command different behaviors according to a desired trajectory. Three behaviors
are examined – transiting, station-keeping and reversing. Given the desired trajectory, the
vehicle is able to autonomously recognize which behavior best suits a portion of the
trajectory. The USV uses a combination of a supervisory switching control structure and a
reinforcement learning algorithm to create a hybrid deliberative and reactive approach to
switch between controllers and actions. Reinforcement learning provides a deliberative
method to create a controller switching policy, while supervisory switching control acts
reactively to instantaneous changes in the environment. Each action is restricted to one
controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping
controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom.
Field experiments are presented to validate this system on the water with a physical USV
platform under Sea State 1 conditions. Main outcomes of this work are that the proposed
system provides better performance than a comparable gain-scheduled nonlinear controller
in terms of an Integral of Absolute Error metric. Additionally, the deliberative component
allows the system to identify dynamically infeasible trajectories and properly
accommodate them.

The design and validation of a low-level backstepping controller for speed and
heading that is adaptive in speed for a twin-hulled underactuated unmanned surface
vessel is presented. Consideration is given to the autonomous launch and recovery of an
underwater vehicle in the decision to pursue an adaptive control approach. Basic system
identification is conducted and numerical simulation of the vessel is developed and
validated. A speed and heading controller derived using the backstepping method and a
model reference adaptive controller are developed and ultimately compared through
experimental testing against a previously developed control law. Experimental tests show
that the adaptive speed control law outperforms the non-adaptive alternatives by as much
as 98% in some cases; however heading control is slightly sacrificed when using the
adaptive speed approach. It is found that the adaptive control law is the best alternative
when drag and mass properties of the vessel are time-varying and uncertain.

This research is concerned with the application of system identification and adaptive control to Functional Electrical Stimulation. The work consists of developing a model which describes EMG (Electromyogram) activity to forearm motion. Although several EMG models presently exist, the goal was to produce a model more suitable for on-line applications while also taking into account the system nonlinearities. The parameters of this model were estimated using a least squares algorithm. The model was tested by simulation and experimentally collected data. The developed model explains well the forearm movement. From the developed model, an adaptive controller was designed using a model reference control scheme. This adaptive controller was used for generating the suitable stimulus pattern. The simulation results showed good tracking and indicated the controllers ability to adapt to changes in the arm's nonlinear gain.

Many positioning systems with varying loads or geometries, such as robotic systems, could take advantage of the class of non-linear controllers known as Adaptive Controls. Model Reference and Pole Placement Adaptive Controllers are usually the preferred techniques for position control systems. Pole Placement is the more universally applicable technique. Adaptive controllers must be able to change control parameters as the system's parameters change (i.e., as is the case with a load or geometry change). The most common and perhaps the fastest converging technique uses the Least Squares Identification Algorithm. Many positioning systems cannot tolerate overshoot. These systems should use an adaptive velocity controller in conjunction with a conventional position controller. This will minimize system overshoot during the learning period. Adaptive controllers tend to be very complex and require a great number of computations. With today's advances in computer technology, adaptive controllers can now be economically considered for many industrial, consumer and military positioning applications.

This dissertation is concerned with the relevant research in developing finite dimensional indirect adaptive schemes to control vibrations in flexible smart structures based on the finite element approximation of the infinite dimensional system. The advantage of this type of modeling is that the dominant modes of vibrations wherein the total energy is concentrated are accommodated thereby avoiding the so-called "spillover" phenomenon. Further, the mass, stiffness and damping coefficients associated with each element appear explicitly in the model facilitating the derivation of the ARMA parametric representation which is suitable for on-line estimation of the structural parameters. The state-space representation of the finite dimensional model is used to design an indirect linear quadratic self tuning regulator algorithm using the parameter estimation, indicated above. Further, a method to choose the control and state weighting matrices (required to design the controller) to yield a stable closed-loop system, is presented. Simulation results demonstrating the performance of the adaptive control system are presented. Another algorithm based on the model reference technique is also developed by considering the discrete time approximation of the finite dimensional model. This control algorithm in conjunction with the parameter estimation constitute an indirect model reference adaptive control system. Simulation results are presented to demonstrate the effect of the reference model parameters, which may impose certain constraints on the force requirements causing actuator saturation and thereby affecting the stability of the closed-loop system. In order to overcome the problem of using bulky and expensive sensors to measure transverse displacement and velocity, a new spatial recursive technique to estimate these variables alternatively by using a distributed set of (measured) strain data, is developed. Relevant algorithm enables the use of smart materials to sense the strain developed at various locations along the length of the structure leading to the development of flexible smart structures. Experimental results on the personal computer based control of vibrations in an aluminum beam using patches of polyvinyldene fluoride (PVDF), and lead zirconate titanate (PZT) as sensors and control actuators respectively, are furnished to demonstrate the feasibility of real-time implementation of the above mentioned control algorithms.