209 research outputs found
An approach to the use of neural network in the analysis of a class of flight vehicles
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1998.Includes bibliographical references (p. 163-165).by Agus Budiyono.M.S
Modelling and Model Predictive Control of Power-Split Hybrid Powertrains for Self-Driving Vehicles
Designing an autonomous vehicle system architecture requires extensive vehicle simulation prior to its implementation on a vehicle. Simulation provides a controlled environment to test the robustness of an autonomous architecture in a variety of driving scenarios. In any autonomous vehicle project, high-fidelity modelling of the vehicle platform is important for accurate simulations. For power-split hybrid electric vehicles, modelling the powertrain for autonomous applications is particularly difficult. The mapping from accelerator and brake pedal positions to torque at the wheels can be a function of many states. Due to this complex powertrain behavior, it is challenging to develop vehicle dynamics control algorithms for autonomous power-split hybrid vehicles.
The 2015 Lincoln MKZ Hybrid is the selected vehicle platform of Autonomoose, the University of Waterloo’s autonomous vehicle project. Autonomoose required high-fidelity models of the vehicle’s power-split powertrain and braking systems, and a new longitudinal dynamics vehicle controller. In this thesis, a grey-box approach to modelling the Lincoln MKZ’s powertrain and braking systems is proposed. The modelling approach utilizes a combination of shallow neural networks and analytical methods to generate a mapping from accelerator and brake pedal positions to the torque at each wheel. Extensive road testing of the vehicle was performed to identify parameters of the powertrain and braking models. Experimental data was measured using a vehicle measurement system and CAN bus diagnostic signals. Model parameters were identified using optimization algorithms. The powertrain and braking models were combined with a vehicle dynamics model to form a complete high-fidelity model of the vehicle that was validated by open-loop simulation.
The high-fidelity models of the powertrain and braking were simplified and combined with a longitudinal vehicle dynamics model to create a control-oriented model of the vehicle. The control-oriented model was used to design an instantaneously linearizing model predictive controller (MPC). The advantages of the MPC over a classical proportional-integral (PI) controller were proven in simulation, and a framework for implementing the MPC on the vehicle was developed. The MPC was implemented on the vehicle for track testing. Early track testing results of the MPC show superior performance to the existing PI that could improve with additional controller parameter tuning
Intelligent Control Strategies for an Autonomous Underwater Vehicle
The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control
problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics
are highly non-linear, and the relative similarity between the linear and angular velocities about
each degree of freedom means that control schemes employed within other flight vehicles are not
always applicable. In such instances, intelligent control strategies offer a more sophisticated
approach to the design of the control algorithm. Neurofuzzy control is one such technique, which
fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture.
Such an approach is highly suited to development of an autopilot for an AUV.
Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in
Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots.
However, the limitation of this technique is that it cannot be used for developing multivariable
fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and
employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control
of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is
extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design
that can accommodate changing vehicle pay loads and environmental disturbances.
Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system
design, the well known properties of radial basis function networks (RBFN) offer a more flexible
controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both
ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form.
This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the
hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector,
Defence Evaluation and Research Agency, Winfrit
A learning enhanced flight control system for high performance aircraft
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1992.Includes bibliographical references (leaves 99-101).by Noel F. Nistler.M.S
Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks
Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research.
Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case.
In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on
Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion.
A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV
Development of neural units with higher-order synaptic operations and their applications to logic circuits and control problems
Neural networks play an important role in the execution of goal-oriented paradigms. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. Development of higher-order neural units with higher-order synaptic operations will open a new window for some complex problems such as control of aerospace vehicles, pattern recognition, and image processing.
The neural models described in this thesis consider the behavior of a single neuron as the basic computing unit in neural information processing operations. Each computing unit in the network is based on the concept of an idealized neuron in the central nervous system (CNS). Most recent mathematical models and their architectures for neuro-control systems have generated many theoretical and industrial interests. Recent advances in static and dynamic neural networks have created a profound impact in the field of neuro-control.
Neural networks consisting of several layers of neurons, with linear synaptic operation, have been extensively used in different applications such as pattern recognition, system identification and control of complex systems such as flexible structures, and intelligent robotic systems. The conventional linear neural models are highly simplified models of the biological neuron. Using this model, many neural morphologies, usually referred to as multilayer feedforward neural networks (MFNNs), have been reported in the literature. The performance of the neurons is greatly affected when a layer of neurons are implemented for system identification, pattern recognition and control problems. Through simulation studies of the XOR logic it was concluded that the neurons with linear synaptic operation are limited to only linearly separable forms of pattern distribution. However, they perform a variety of complex mathematical operations when they are implemented in the form of a network structure. These networks suffer from various limitations such as computational efficiency and learning capabilities and moreover, these models ignore many salient features of the biological neurons such as time delays, cross and self correlations, and feedback paths which are otherwise very important in the neural activity.
In this thesis an effort is made to develop new mathematical models of neurons that belong to the class of higher-order neural units (HONUs) with higher-order synaptic operations such as quadratic and cubic synaptic operations. The advantage of using this type of neural unit is associated with performance of the neurons but the performance comes at the cost of exponential increase in parameters that hinders the speed of the training process.
In this context, a novel method of representation of weight parameters without sacrificing the neural performance has been introduced. A generalised representation of the higher-order synaptic operation for these neural structures was proposed. It was shown that many existing neural structures can be derived from this generalized representation of the higher-order synaptic operation. In the late 1960’s, McCulloch and Pitts modeled the stimulation-response of the primitive neuron using the threshold logic. Since then, it has become a practice to implement the logic circuits using neural structures. In this research, realization of the logic circuits such as OR, AND, and XOR were implemented using the proposed neural structures. These neural structures were also implemented as neuro-controllers for the control problems such as satellite attitude control and model reference adaptive control. A comparative study of the performance of these neural structures compared to that of the conventional linear controllers has been presented. The simulation results obtained in this research were applicable only for the simplified model presented in the simulation studies
NDI-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling.
The success of Unmanned Combat Aerial Vehicles (UCAVs) requires further
developments in the field of automated aerial refuelling (AAR) and control systems. AAR
aircraft models identified thus far do not take the centre of gravity (cg) position
movement into account during refuelling. A six-degree-of-freedom aircraft model was
combined with a moving cg model for refuelling. The equations of motion for the aircraft
in flight refuelling showed the aircraft dynamics to be coupled in the longitudinal and
lateral-directional planes when the cg had moved away from the reference point.
Applying assumptions specific to the flight conditions, simplified equations of motion
were derived. Modal analysis of four cases for the linearised aircraft model during aerial
refuelling was conducted. This revealed that the increase in mass was favourable to the
stability of the Dutch Roll mode, but the mode did become more oscillatory initially as
mass was increased, but as the cg moved forward, the mode became less oscillatory. The
opposite was observed with the Phugoid mode. The Short Period Oscillation (SPO)
decomposed into two first order modes during refuelling and these remained unchanged
during the refuelling process. Three radial basis function (RBF) neural networks
(RBFNN) were developed and trained to approximate the inverse plant dynamics and
predicted commanded deflections of the elevator, aileron and rudder. Training data
required for the network was randomly generated and the desired rates and commanded
control surface deflections were computed. The training error was the smallest in the
elevator deflection required during refuelling. A basic nonlinear dynamic inversion (NDI)
controller without a neural network (NN) was designed for the aircraft. The performance
of this controller was not satisfactory. The RBF was combined with the NDI to form a
RBFNN-based controller. The longitudinal NDI RBFNN-based controller was less
sensitive to modelling errors than the base NDI controller. The lateral NDI RBFNN-based
controller’s performance was worse than the longitudinal controller, but showed potential
as a technique for future consideration. Including the variation of aircraft inertia in the
model has been recommended as further work, as well as exploring other neural network
topologies in the NDI NN controller
Online parameter estimation of a miniature unmanned helicopter using neural network techniques
The online aerodynamic parameter estimation of a miniature unmanned helicopter using
Neural Network techniques has been presented. The simulation model for the miniature
helicopter was developed using the MATLAB/ SIMULINK software tool. Three trim conditions
were analyzed: hover flight, 10m/s forward flight and 20m/s forward flight. Radial
Basis Function (RBF) online learning was achieved using a moving window algorithm which
generated an input-output data set at each time step. RBF network online identification was
achieved with good robustness to noise for all flight conditions. However, the presence of
atmospheric turbulence and sensor noise had an adverse effect on network size and memory
usage. The Delta Method (DM) and the Modified Delta Method (MDM) was investigated
for the NN-based online estimation of aerodynamic parameters. An increasing number high
confidence estimated parameters could be extracted using the MDM as the helicopter transitioned
from hover to forward flight
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