6,141 research outputs found
Adaptive Differential Feedback in Time-Varying Multiuser MIMO Channels
In the context of a time-varying multiuser multiple-input-multiple-output
(MIMO) system, we design recursive least squares based adaptive predictors and
differential quantizers to minimize the sum mean squared error of the overall
system. Using the fact that the scalar entries of the left singular matrix of a
Gaussian MIMO channel becomes almost Gaussian distributed even for a small
number of transmit antennas, we perform adaptive differential quantization of
the relevant singular matrix entries. Compared to the algorithms in the
existing differential feedback literature, our proposed quantizer provides
three advantages: first, the controller parameters are flexible enough to adapt
themselves to different vehicle speeds; second, the model is backward adaptive
i.e., the base station and receiver can agree upon the predictor and variance
estimator coefficients without explicit exchange of the parameters; third, it
can accurately model the system even when the correlation between two
successive channel samples becomes as low as 0.05. Our simulation results show
that our proposed method can reduce the required feedback by several kilobits
per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h
(singular vector tracker). The proposed system also outperforms a fixed
quantizer, with same feedback overhead, in terms of bit error rate up to 30
km/h.Comment: IEEE 22nd International Conference on Personal, Indoor and Mobile
Radio Communications (2011
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
In this paper, we present a novel solution for real-time, Non-Linear Model
Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The
proposed controller formulates the Optimal Control Problem (OCP) in terms of
flat outputs over an adaptive lattice. In common approximated OCP solutions,
the number of discretization points composing the lattice represents a critical
upper bound for real-time applications. The proposed NMPC-based technique
refines the initially uniform time horizon by adding time steps with a sampling
criterion that aims to reduce the discretization error. This enables a higher
accuracy in the initial part of the receding horizon, which is more relevant to
NMPC, while keeping bounded the number of discretization points. By combining
this feature with an efficient Least Square formulation, our solver is also
extremely time-efficient, generating trajectories of multiple seconds within
only a few milliseconds. The performance of the proposed approach has been
validated in a high fidelity simulation environment, by using an UAV platform.
We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and
Programming for Autonomous Robots (SIMPAR 2018
Privately Estimating a Gaussian: Efficient, Robust and Optimal
In this work, we give efficient algorithms for privately estimating a
Gaussian distribution in both pure and approximate differential privacy (DP)
models with optimal dependence on the dimension in the sample complexity. In
the pure DP setting, we give an efficient algorithm that estimates an unknown
-dimensional Gaussian distribution up to an arbitrary tiny total variation
error using samples while tolerating a
constant fraction of adversarial outliers. Here, is the condition
number of the target covariance matrix. The sample bound matches best
non-private estimators in the dependence on the dimension (up to a
polylogarithmic factor). We prove a new lower bound on differentially private
covariance estimation to show that the dependence on the condition number
in the above sample bound is also tight. Prior to our work, only
identifiability results (yielding inefficient super-polynomial time algorithms)
were known for the problem. In the approximate DP setting, we give an efficient
algorithm to estimate an unknown Gaussian distribution up to an arbitrarily
tiny total variation error using samples while tolerating
a constant fraction of adversarial outliers. Prior to our work, all efficient
approximate DP algorithms incurred a super-quadratic sample cost or were not
outlier-robust. For the special case of mean estimation, our algorithm achieves
the optimal sample complexity of , improving on a bound from prior work. Our pure DP algorithm relies on a recursive
private preconditioning subroutine that utilizes the recent work on private
mean estimation [Hopkins et al., 2022]. Our approximate DP algorithms are based
on a substantial upgrade of the method of stabilizing convex relaxations
introduced in [Kothari et al., 2022]
Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction
Considering the competitive and strongly regulated pharmaceutical industry, mathematical
modeling and process systems engineering might be useful tools for implementing quality by
design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However,
a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification
of model candidates from a set of various model hypotheses. To identify the best experimental
design suitable for a reliable model selection and system identification is challenging for nonlinear
(bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness
for model selection problems under uncertainty, and thus translates the model selection problem
to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal
controls for improved model selection trajectories are expressed analytically with low computational
costs. We further demonstrate the impact of parameter uncertainties on the differential flatness-based
method and provide an effective robustification strategy with the point estimate method for
uncertainty quantification. In a simulation study, we consider a biocatalytic reaction step simulating
the carboligation of aldehydes, where we successfully derive optimal controls for improved model
selection trajectories under uncertainty
Data Detection and Channel Estimation of OFDM Systems Using Differential Modulation
Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation technique which is robust against multipath fading and very easy to implement in transmitters and receivers using the inverse fast Fourier transform and the fast Fourier transform. A guard interval using cyclic prefix is inserted in each OFDM symbol to avoid the inter-symbol interference. This guard interval should be at least equal to, or longer than the maximum delay spread of the channel to combat against inter-symbol interference properly.
In coherent detection, channel estimation is required for the data detection of OFDM systems to equalize the channel effects. One of the popular techniques is to insert pilot tones (reference signals) in OFDM symbols. In conventional method, pilot tones are inserted into every OFDM symbols. Channel capacity is wasted due to the transmission of a large number of pilot tones. To overcome this transmission loss, incoherent data detection is introduced in OFDM systems, where it is not needed to estimate the channel at first. We use differential modulation based incoherent detection in this thesis for the data detection of OFDM systems. Data can be encoded in the relative phase of consecutive OFDM symbols (inter-frame modulation) or in the relative phase of an OFDM symbol in adjacent subcarriers (in-frame modulation). We use higher order differential modulation for in-frame modulation to compare the improvement of bit error rate. It should be noted that the single differential modulation scheme uses only one pilot tone, whereas the double differential uses two pilot tones and so on. Thus overhead due to the extra pilot tones in conventional methods are minimized and the detection delay is reduced. It has been observed that the single differential scheme works better in low SNRs (Signal to Noise Ratios) with low channel taps and the double differential works better at higher SNRs. Simulation results show that higher order differential modulation schemes don¡¯t have any further advantages. For inter-frame modulation, we use single differential modulation where only one OFDM symbol is used as a reference symbol. Except the reference symbol, no other overhead is required. We also perform channel estimation using differential modulation. Channel estimation using differential modulation is very easy and channel coefficients can be estimated very accurately without increasing any computational complexity. Our simulation results show that the mean square channel estimation error is about ¡¼10¡½^(-2) at an SNR of 30 dB for double differential in-frame modulation scheme, whereas channel estimation error is about ¡¼10¡½^(-4) for single differential inter-frame modulation. Incoherent data detection using classical DPSK (Differential Phase Shift Keying) causes an SNR loss of approximately 3 dB compared to coherent detection. But in our method, differential detection can estimate the channel coefficients very accurately and our estimated channel can be used in simple coherent detection to improve the system performance and minimize the SNR loss that happens in conventional method
Inverse optimal control for differentially flat systems with application to lower-limb prosthetic devices
Powered prosthetic devices have shown to be capable of restoring natural gait to amputees.
However, the commercialization of these devices is faced by some challenges, in
particular in prosthetic controller design. A common control framework for these devices
is called impedance control. The challenge in the application of this framework is that it
requires the choice of many controller parameters, which are chosen by clinicians through
trial and error for each patient. In this thesis we automate the process of choosing these
parameters by learning from demonstration. To learn impedance controller parameters
for flat-ground, we adopt the method of learning from exemplar trajectories. Since we
do not at first have exemplar joint trajectories that are specific to each patient, we use
invariances in locomotion to produce them from pre-recorded observations of unimpaired
human walking and from measurements of the patient’s height, weight, thigh length, and
shank length. Experiments with two able-bodied human subjects wearing the Vanderbilt
prosthetic leg with an able-bodied adaptor show that our method recovers the same
level of performance that can be achieved by a clinician but reduces the amount of time
required to choose controller parameters from four hours to four minutes.
To extend this framework to learning controllers for stair ascent, we need a model
of locomotion that is capable of generating exemplar trajectories for any desired stair
height. Motivated by this challenge, we focus on a class of learning from demonstration
methods called inverse optimal control. Inverse optimal control is the problem of computing
a cost function with respect to which observed trajectories of a given dynamic
system are optimal. We first present a new formulation of this problem, based on minimizing
the extent to which first-order necessary conditions of optimality are violated.
This formulation leads to a computationally efficient solution as opposed to traditional
approaches. Furthermore, we develop the theory of inverse optimal control for the case
where the dynamic system is differentially flat. We demonstrate that the solution further
simplifies in this case, in fact reducing to finite-dimensional linear least-squares minimization.
We show how to make this solution robust to model perturbation, sampled
data, and measurement noise, as well as provide a recursive implementation for online
learning. Finally, we apply our new formulation of inverse optimal control to model
human locomotion during stair ascent. Given sparse observations of human walkers, our
model predicts joint angle trajectories for novel stair heights that compare well to motion
capture data. These exemplar trajectories are then used to learn prosthetic controllers
for one subject. We show the performance of the learned controllers in a stair ascent
experiment with the subject walking with the Vanderbilt prosthetic device
Exploiting Heterogeneity in Networks of Aerial and Ground Robotic Agents
By taking advantage of complementary communication technologies, distinct sensing functionalities and varied motion dynamics present in a heterogeneous multi-robotic network, it is possible to accomplish a main mission objective by assigning specialized sub-tasks to specific members of a robotic team. An adequate selection of the team members and an effective coordination are some of the challenges to fully exploit the unique capabilities that these types of systems can offer. Motivated by real world applications, we focus on a multi-robotic network consisting off aerial and ground agents which has the potential to provide critical support to humans in complex settings. For instance, aerial robotic relays are capable of transporting small ground mobile sensors to expand the communication range and the situational awareness of first responders in hazardous environments. In the first part of this dissertation, we extend work on manipulation of cable-suspended loads using aerial robots by solving the problem of lifting the cable-suspended load from the ground before proceeding to transport it. Since the suspended load-quadrotor system experiences switching conditions during this critical maneuver, we define a hybrid system and show that it is differentially-flat. This property facilitates the design of a nonlinear controller which tracks a waypoint-based trajectory associated with the discrete states of the hybrid system. In addition, we address the case of unknown payload mass by combining a least-squares estimation method with the designed controller. Second, we focus on the coordination of a heterogeneous team formed by a group of ground mobile sensors and a flying communication router which is deployed to sense areas of interest in a cluttered environment. Using potential field methods, we propose a controller for the coordinated mobility of the team to guarantee inter-robot and obstacle collision avoidance as well as connectivity maintenance among the ground agents while the main goal of sensing is carried out. For the case of the aerial communications relays, we combine antenna diversity with reinforcement learning to dynamically re-locate these relays so that the received signal strength is maintained above a desired threshold. Motivated by the recent interest of combining radio frequency and optical wireless communications, we envision the implementation of an optical link between micro-scale aerial and ground robots. This type of link requires maintaining a sufficient relative transmitter-receiver position for reliable communications. In the third part of this thesis, we tackle this problem. Based on the link model, we define a connectivity cone where a minimum transmission rate is guaranteed. For example, the aerial robot has to track the ground vehicle to stay inside this cone. The control must be robust to noisy measurements. Thus, we use particle filters to obtain a better estimation of the receiver position and we design a control algorithm for the flying robot to enhance the transmission rate. Also, we consider the problem of pairing a ground sensor with an aerial vehicle, both equipped with a hybrid radio-frequency/optical wireless communication system. A challenge is positioning the flying robot within optical range when the sensor location is unknown. Thus, we take advantage of the hybrid communication scheme by developing a control strategy that uses the radio signal to guide the aerial platform to the ground sensor. Once the optical-based signal strength has achieved a certain threshold, the robot hovers within optical range. Finally, we investigate the problem of building an alliance of agents with different skills in order to satisfy the requirements imposed by a given task. We find this alliance, known also as a coalition, by using a bipartite graph in which edges represent the relation between agent capabilities and required resources for task execution. Using this graph, we build a coalition whose total capability resources can satisfy the task resource requirements. Also, we study the heterogeneity of the formed coalition to analyze how it is affected for instance by the amount of capability resources present in the agents
- …