56 research outputs found
Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation into the control input with an additional exponential decaying term. An MPC based control method is then designed for the robot in the presence of external disturbances. The MPC optimization has been formulated as a convex nonlinear minimization problem and a primal-dual neural network (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been significantly improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach, which can be applied for a large range of wheeled mobile robots
Safety Index Synthesis via Sum-of-Squares Programming
Control systems often need to satisfy strict safety requirements. Safety
index provides a handy way to evaluate the safety level of the system and
derive the resulting safe control policies. However, designing safety index
functions under control limits is difficult and requires a great amount of
expert knowledge. This paper proposes a framework for synthesizing the safety
index for general control systems using sum-of-squares programming. Our
approach is to show that ensuring the non-emptiness of safe control on the safe
set boundary is equivalent to a local manifold positiveness problem. We then
prove that this problem is equivalent to sum-of-squares programming via the
Positivstellensatz of algebraic geometry. We validate the proposed method on
robot arms with different degrees of freedom and ground vehicles. The results
show that the synthesized safety index guarantees safety and our method is
effective even in high-dimensional robot systems
Collective Intelligence for Object Manipulation with Mobile Robots
While natural systems often present collective intelligence that allows them
to self-organize and adapt to changes, the equivalent is missing in most
artificial systems. We explore the possibility of such a system in the context
of cooperative object manipulation using mobile robots. Although conventional
works demonstrate potential solutions for the problem in restricted settings,
they have computational and learning difficulties. More importantly, these
systems do not possess the ability to adapt when facing environmental changes.
In this work, we show that by distilling a planner derived from a
gradient-based soft-body physics simulator into an attention-based neural
network, our multi-robot manipulation system can achieve better performance
than baselines. In addition, our system also generalizes to unseen
configurations during training and is able to adapt toward task completions
when external turbulence and environmental changes are applied
Model-Based Reinforcement Learning for Stochastic Hybrid Systems
Optimal control of general nonlinear systems is a central challenge in
automation. Enabled by powerful function approximators, data-driven approaches
to control have recently successfully tackled challenging robotic applications.
However, such methods often obscure the structure of dynamics and control
behind black-box over-parameterized representations, thus limiting our ability
to understand closed-loop behavior. This paper adopts a hybrid-system view of
nonlinear modeling and control that lends an explicit hierarchical structure to
the problem and breaks down complex dynamics into simpler localized units. We
consider a sequence modeling paradigm that captures the temporal structure of
the data and derive an expectation-maximization (EM) algorithm that
automatically decomposes nonlinear dynamics into stochastic piecewise affine
dynamical systems with nonlinear boundaries. Furthermore, we show that these
time-series models naturally admit a closed-loop extension that we use to
extract local polynomial feedback controllers from nonlinear experts via
behavioral cloning. Finally, we introduce a novel hybrid relative entropy
policy search (Hb-REPS) technique that incorporates the hierarchical nature of
hybrid systems and optimizes a set of time-invariant local feedback controllers
derived from a local polynomial approximation of a global state-value function
A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks
In this paper, we propose a deep state-action-reward-state-action (SARSA) λ learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency communication (URLLC). To reduce the mean decoding error probability in time-varying network environments, this work designs a reliable learning algorithm for providing a long-term resource allocation, where the reward feedback is based on the instantaneous network performance. With the aid of the proposed algorithm, this paper addresses three main challenges of the reliable resource sharing in NOMA-URLLC networks: 1) user clustering; 2) Instantaneous feedback system; and 3) Optimal resource allocation. All of these designs interact with the considered communication environment. Lastly, we compare the performance of the proposed algorithm with conventional Q-learning and SARSA Q-learning algorithms. The simulation outcomes show that: 1) Compared with the traditional Q learning algorithms, the proposed solution is able to converges within 200 episodes for providing as low as 10-2 long-term mean error; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of decoding error probabilities; and 3) The proposed feedback system is efficient for the long-term learning process
Optimal filter design for power converters regulated by FCS-MPC in the MEA
For the DC electrical power distribution system onboard more electric aircraft, the voltage quality of DC bus is of a great concern since there could be significant harmonics distortions when feeding different power electronics loads. This problem can be potentially addressed by introducing a dc filter to the point-of-load converters regulated by the finite control set model predictive control (FCS-MPC). To optimize this filter, Genetic Algorithm (GA) is utilized for searching the optimal design which guarantees a low mass and low power losses. Different from the conventional filter design methods, the proposed method treats LC as design variables which need to be optimised while ensuring the output power quality. First, relations among variables, operation conditions and constraints are built based on commercial data and circuit simulations. Then, the design and optimization are developed with these relations and a Pareto-front is finally given by GA. After that, the best design is obtained by an index integrating two objectives. Lastly, the design approach is verified by experiment where an FCS-MPC regulated converter was used as a particular example fed by three different LC filters
Robust recovery of missing data in electricity distribution systems
The advanced operation of future electricity distribution systems is likely to require significant observability of the different parameters of interest (e.g., demand, voltages, currents, etc.). Ensuring completeness of data is, therefore, paramount. In this context, an algorithm for recovering missing state variable observations in electricity distribution systems is presented. The proposed method exploits the low rank structure of the state variables via a matrix completion approach while incorporating prior knowledge in the form of second order statistics. Specifically, the recovery method combines nuclear norm minimization with Bayesian estimation. The performance of the new algorithm is compared to the information-theoretic limits and tested trough simulations using real data of an urban low voltage distribution system. The impact of the prior knowledge is analyzed when a mismatched covariance is used and for a Markovian sampling that introduces structure in the observation pattern. Numerical results demonstrate that the proposed algorithm is robust and outperforms existing state of the art algorithms
Fabrication and characterization of shape memory polymers at small scales
The objective of this research is to thoroughly investigate the shape memory effect
in polymers, characterize, and optimize these polymers for applications in information storage systems.
Previous research effort in this field concentrated on shape memory metals for
biomedical applications such as stents. Minimal work has been done on shape memory poly-
mers; and the available work on shape memory polymers has not characterized the behaviors
of this category of polymers fully. Copolymer shape memory materials based on diethylene
glycol dimethacrylate (DEGDMA) crosslinker, and tert butyl acrylate (tBA) monomer are
designed. The design encompasses a careful control of the backbone chemistry of the materials.
Characterization methods such as dynamic mechanical analysis (DMA), differential
scanning calorimetry (DSC); and novel nanoscale techniques such as atomic force microscopy
(AFM), and nanoindentation are applied to this system of materials. Designed experiments
are conducted on the materials to optimize spin coating conditions for thin films. Furthermore,
the recovery, a key for the use of these polymeric materials for information storage, is
examined in detail with respect to temperature. In sum, the overarching objectives of the
proposed research are to: (i) design shape memory polymers based on polyethylene glycol
dimethacrylate (PEGDMA) and diethylene glycol dimethacrylate (DEGDMA) crosslinkers,
2-hydroxyethyl methacrylate (HEMA) and tert-butyl acrylate monomer (tBA). (ii) utilize
dynamic mechanical analysis (DMA) to comprehend the thermomechanical properties of
shape memory polymers based on DEGDMA and tBA. (iii) utilize nanoindentation and
atomic force microscopy (AFM) to understand the nanoscale behavior of these SMPs, and
explore the strain storage and recovery of the polymers from a deformed state. (iv) study
spin coating conditions on thin film quality with designed experiments. (iv) apply neural
networks and genetic algorithms to optimize these systems.Ph.D.Committee Chair: Gall, Ken; Committee Chair: May, Gary S; Committee Member: Brand, Oliver; Committee Member: Degertekin, F Levent; Committee Member: Milor, Linda
A Dynamic Distributed Scheduler for Computing on the Edge
Edge computing has become a promising computing paradigm for building IoT
(Internet of Things) applications, particularly for applications with specific
constraints such as latency or privacy requirements. Due to resource
constraints at the edge, it is important to efficiently utilize all available
computing resources to satisfy these constraints. A key challenge in utilizing
these computing resources is the scheduling of different computing tasks in a
dynamically varying, highly hybrid computing environment. This paper described
the design, implementation, and evaluation of a distributed scheduler for the
edge that constantly monitors the current state of the computing infrastructure
and dynamically schedules various computing tasks to ensure that all
application constraints are met. This scheduler has been extensively evaluated
with real-world AI applications under different scenarios and demonstrates that
it outperforms current scheduling approaches in satisfying various application
constraints.Comment: 11 pages,14 figure
On a Uniform Causality Model for Industrial Automation
The increasing complexity of Cyber-Physical Systems (CPS) makes industrial
automation challenging. Large amounts of data recorded by sensors need to be
processed to adequately perform tasks such as diagnosis in case of fault. A
promising approach to deal with this complexity is the concept of causality.
However, most research on causality has focused on inferring causal relations
between parts of an unknown system. Engineering uses causality in a
fundamentally different way: complex systems are constructed by combining
components with known, controllable behavior. As CPS are constructed by the
second approach, most data-based causality models are not suited for industrial
automation. To bridge this gap, a Uniform Causality Model for various
application areas of industrial automation is proposed, which will allow better
communication and better data usage across disciplines. The resulting model
describes the behavior of CPS mathematically and, as the model is evaluated on
the unique requirements of the application areas, it is shown that the Uniform
Causality Model can work as a basis for the application of new approaches in
industrial automation that focus on machine learning
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