Warren E. Dixon
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View article: Development and validation of a kinematic hindlimb cycling model for rats
Development and validation of a kinematic hindlimb cycling model for rats Open
Functional electrical stimulation (FES) bicycle training is a physical rehabilitation technique used to promote muscle recovery and/or cardiorespiratory health in persons with lower extremity impairment due to neurologic injury. FES cyclin…
View article: LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller
LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller Open
Thermodynamic principles can be employed to design parameter update laws that address challenges such as the exploration vs. exploitation dilemma. In this paper, inspired by the Langevin equation, an update law is developed for a Lyapunov-…
View article: Collaborative Indirect Influencing and Control on Graphs using Graph Neural Networks
Collaborative Indirect Influencing and Control on Graphs using Graph Neural Networks Open
This paper presents a novel approach to solving the indirect influence problem in networked systems, in which cooperative nodes must regulate a target node with uncertain dynamics to follow a desired trajectory. We leverage the message-pas…
View article: Evaluation of a control paradigm allowing heart rate guided rehabilitative exercise for boys with Duchenne muscular dystrophy
Evaluation of a control paradigm allowing heart rate guided rehabilitative exercise for boys with Duchenne muscular dystrophy Open
Background: Aerobic cycle-training counters deconditioning and induces muscle and cardiorespiratory benefits in various neuromuscular disorders. However, its application to Duchenne muscular dystrophy (DMD) is limited due to lack of exerci…
View article: System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach Open
Deep Neural Networks (DNNs) are increasingly used in control applications due to their powerful function approximation capabilities. However, many existing formulations focus primarily on tracking error convergence, often neglecting the ch…
View article: Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters
Bounds on Deep Neural Network Partial Derivatives with Respect to Parameters Open
Deep neural networks (DNNs) have emerged as a powerful tool with a growing body of literature exploring Lyapunov-based approaches for real-time system identification and control. These methods depend on establishing bounds for the second p…
View article: Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems
Lyapunov-Based Graph Neural Networks for Adaptive Control of Multi-Agent Systems Open
Graph neural networks (GNNs) have a message-passing framework in which vector messages are exchanged between graph nodes and updated using feedforward layers. The inclusion of distributed message-passing in the GNN architecture makes them …
View article: Distributed RISE-based Control for Exponential Heterogeneous Multi-Agent Target Tracking of Second-Order Nonlinear Systems
Distributed RISE-based Control for Exponential Heterogeneous Multi-Agent Target Tracking of Second-Order Nonlinear Systems Open
A distributed implementation of a Robust Integral of the Sign of the Error (RISE) controller is developed for multi-agent target tracking problems with exponential convergence guarantees. Previous RISE-based approaches for multi-agent syst…
View article: Collaborative Spacecraft Servicing under Partial Feedback using Lyapunov-based Deep Neural Networks
Collaborative Spacecraft Servicing under Partial Feedback using Lyapunov-based Deep Neural Networks Open
Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A critical emerging application is collaborative spacecraft se…
View article: Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control Open
Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the adaptive control literature provides a Lyapunov-based approach to …
View article: Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic Nonlinear Systems
Lyapunov-Based Deep Neural Networks for Adaptive Control of Stochastic Nonlinear Systems Open
Controlling nonlinear stochastic dynamical systems involves substantial challenges when the dynamics contain unknown and unstructured nonlinear state-dependent terms. For such complex systems, deep neural networks can serve as powerful bla…
View article: Adaptive Deep Neural Network-Based Control Barrier Functions
Adaptive Deep Neural Network-Based Control Barrier Functions Open
Safety constraints of nonlinear control systems are commonly enforced through the use of control barrier functions (CBFs). Uncertainties in the dynamic model can disrupt forward invariance guarantees or cause the state to be restricted to …
View article: Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control
Lyapunov-Based Deep Residual Neural Network (ResNet) Adaptive Control Open
Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the adaptive control literature provides a Lyapunov-based approach to …
View article: Composite Adaptive Lyapunov-Based Deep Neural Network (Lb-DNN) Controller
Composite Adaptive Lyapunov-Based Deep Neural Network (Lb-DNN) Controller Open
Recent advancements in adaptive control have equipped deep neural network (DNN)-based controllers with Lyapunov-based adaptation laws that work across a range of DNN architectures to uniquely enable online learning. However, the adaptation…
View article: Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller
Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller Open
Deep neural network (DNN)-based adaptive controllers can be used to compensate for unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very susceptible to overfitting and co-adaptation. Dropout regularization is…
View article: Systems and methods for estimating the structure and motion of an object
Systems and methods for estimating the structure and motion of an object Open
In one embodiment, the structure and motion of a stationary object are determined using two images and a linear velocity and linear acceleration of a camera. In another embodiment, the structure and motion of a stationary or moving object …
View article: Systems and methods for maintaining multiple objects within a camera field-of-view
Systems and methods for maintaining multiple objects within a camera field-of-view Open
In one embodiment, a system and method for maintaining objects within a camera field of view include identifying constraints to be enforced, each constraint relating to an attribute of the viewed objects, identifying a priority rank for th…
View article: Hierarchical Reinforcement Learning-based Supervisory Control of Unknown Nonlinear Systems
Hierarchical Reinforcement Learning-based Supervisory Control of Unknown Nonlinear Systems Open
A supervisory control approach using hierarchical reinforcement learning (HRL) is developed to approximate the solution to optimal regulation problems for a control-affine, continuous-time nonlinear system with unknown drift dynamics. This…
View article: Controller Synthesis for Multi-Agent Systems With Intermittent Communication and Metric Temporal Logic Specifications
Controller Synthesis for Multi-Agent Systems With Intermittent Communication and Metric Temporal Logic Specifications Open
This paper investigates the controller synthesis problem for a multi-agent system (MAS) with intermittent communication. We adopt a relay-explorer scheme, where a mobile relay agent with absolute position sensors switches among a set of ex…
View article: Deep Recurrent Neural Network-Based Observer for Uncertain Nonlinear Systems
Deep Recurrent Neural Network-Based Observer for Uncertain Nonlinear Systems Open
Recurrent neural networks (RNNs) have gained popularity in various applications like handwriting recognition, time series prediction, and system identification due to their ability to retain state information for later use through temporal…
View article: Optimal Safety for Constrained Differential Inclusions using Nonsmooth Control Barrier Functions
Optimal Safety for Constrained Differential Inclusions using Nonsmooth Control Barrier Functions Open
For a broad class of nonlinear systems, we formulate the problem of guaranteeing safety with optimality under constraints. Specifically, we define controlled safety for differential inclusions with constraints on the states and the inputs.…
View article: Distributed State Estimation with Deep Neural Networks for Uncertain Nonlinear Systems under Event-Triggered Communication
Distributed State Estimation with Deep Neural Networks for Uncertain Nonlinear Systems under Event-Triggered Communication Open
Distributed state estimation is examined for a sensor network tasked with reconstructing a system's state through the use of a distributed and event-triggered observer. Each agent in the sensor network employs a deep neural network (DNN) t…
View article: Data-Based and Opportunistic Integral Concurrent Learning for Adaptive Trajectory Tracking During Switched FES-Induced Biceps Curls
Data-Based and Opportunistic Integral Concurrent Learning for Adaptive Trajectory Tracking During Switched FES-Induced Biceps Curls Open
Hybrid exoskeletons, which combine functional electrical stimulation (FES) with a motorized testbed, can potentially improve the rehabilitation of people with movement disorders. However, hybrid exoskeletons have inherently nonlinear and u…
View article: IEEE Transactions on Control Systems Technology information for authors
IEEE Transactions on Control Systems Technology information for authors Open
SYSTEMS TECHNOLOGY, the IEEE Control Systems Society publishes high-quality papers on technological advances in the design, realization, and operation of control systems.Submissions should emphasize novel contributions to the solution of c…
View article: Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking Open
In this paper, we develop safe reinforcement-learning-based controllers for\nsystems tasked with accomplishing complex missions that can be expressed as\nlinear temporal logic specifications, similar to those required by\nsearch-and-rescue…
View article: Encouraging Volitional Pedaling in Functional Electrical Stimulation-Assisted Cycling Using Barrier Functions
Encouraging Volitional Pedaling in Functional Electrical Stimulation-Assisted Cycling Using Barrier Functions Open
Stationary motorized cycling assisted by functional electrical stimulation (FES) is a popular therapy for people with movement impairments. Maximizing volitional contributions from the rider of the cycle can lead to long-term benefits like…
View article: IEEE Transactions on Control Systems Technology information for authors
IEEE Transactions on Control Systems Technology information for authors Open
SYSTEMS TECHNOLOGY, the IEEE Control Systems Society publishes high-quality papers on technological advances in the design, realization, and operation of control systems.Submissions should emphasize novel contributions to the solution of c…
View article: IEEE Transactions on Control Systems Technology information for authors
IEEE Transactions on Control Systems Technology information for authors Open
SYSTEMS TECHNOLOGY, the IEEE Control Systems Society publishes high-quality papers on technological advances in the design, realization, and operation of control systems.Submissions should emphasize novel contributions to the solution of c…
View article: IEEE Transactions on Control Systems Technology information for authors
IEEE Transactions on Control Systems Technology information for authors Open
SYSTEMS TECHNOLOGY, the IEEE Control Systems Society publishes high-quality papers on technological advances in the design, realization, and operation of control systems.Submissions should emphasize novel contributions to the solution of c…
View article: FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction
FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction Open
For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of dail…