47 citations · 172 across the 92 of their papers we have counts for
19 papers · 1 filter
Event-Driven Safe Control of a Black-Box Balancing Wheel with Friction-Gated Actuation
Kanishka Harwani, Neha Navarkar, Aliasghar Arab +1
Commercial balancing wheels are closed systems: an internal controller keeps the board upright, and a rider steers by shifting weight and twisting the hips. We augment a one-wheel…
Learning Robust Control Lyapunov Functions through Lipschitz Neural Networks
Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
This work presents a novel framework for learning robust control Lyapunov functions and stabilizing controllers for nonlinear dynamical systems subject to additive disturbances upp…
Sandbox-Enabled Digital Twin for Cyber-Physical Systems
Meet Udeshi, Md Raz, Prashanth Krishnamurthy +2
Firmware/software in cyber-physical system (CPS) embedded devices/controllers can have vulnerabilities stemming from multiple sources such as weak security practices, outdated libr…
A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
Ali Umut Kaypak, Prashanth Krishnamurthy, Farshad Khorrami
Ensuring safety under unknown and stochastic dynamics remains a significant challenge in reinforcement learning (RL). In this paper, we propose a model predictive control (MPC)-bas…
A Consensus Algorithm for Second-Order Systems Evolving on Lie Groups
Akhil B Krishna, Farshad Khorrami, Anthony Tzes
In this paper, a consensus algorithm is proposed for interacting multi-agents, which can be modeled as simple Mechanical Control Systems (MCS) evolving on a general Lie group. The…
Data-Efficient System Identification via Lipschitz Neural Networks
Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
Extracting dynamic models from data is of enormous importance in understanding the properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of neur…
Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions
Shiqing Wei, Rooholla Khorrambakht, Prashanth Krishnamurthy +2
Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method…
Combining Switching Mechanism with Re-Initialization and Anomaly Detection for Resiliency of Cyber-Physical Systems
Hao Fu, Prashanth Krishnamurthy, Farshad Khorrami
Cyber-physical systems (CPS) play a pivotal role in numerous critical real-world applications that have stringent requirements for safety. To enhance the CPS resiliency against att…
Tracking Real-time Anomalies in Cyber-Physical Systems Through Dynamic Behavioral Analysis
Prashanth Krishnamurthy, Ali Rasteh, Ramesh Karri +1
Increased connectivity and remote reprogrammability/reconfigurability features of embedded devices in current-day power systems (including interconnections between information tech…
Confidence-Aware Safe and Stable Control of Control-Affine Systems
Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measuremen…
Learning a Better Control Barrier Function Under Uncertain Dynamics
Bolun Dai, Prashanth Krishnamurthy, Farshad Khorrami
Using control barrier functions (CBFs) as safety filters provides a computationally inexpensive yet effective method for constructing controllers in safety-critical applications. H…
High-Dimensional Controller Tuning through Latent Representations
Alireza Sarmadi, Prashanth Krishnamurthy, Farshad Khorrami
In this paper, we propose a method to automatically and efficiently tune high-dimensional vectors of controller parameters. The proposed method first learns a mapping from the high…
Using Circulation to Mitigate Spurious Equilibria in Control Barrier Function -- Extended Version
Vinicius Mariano Goncalves, Prashanth Krishnamurthy, Anthony Tzes +1
Control Barrier Functions and Quadratic Programming are increasingly used for designing controllers that consider critical safety constraints. However, like Artificial Potential Fi…
State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE
Bolun Dai, Prashanth Krishnamurthy, Andrew Papanicolaou +1
We develop a computationally efficient learning-based forward-backward stochastic differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) systems su…
Neural Lyapunov Control for Nonlinear Systems with Unstructured Uncertainties
Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
Stabilizing controller design and region of attraction (RoA) estimation are essential in nonlinear control. Moreover, it is challenging to implement a control Lyapunov function (CL…
Data-Efficient Control Barrier Function Refinement
Bolun Dai, Heming Huang, Prashanth Krishnamurthy +1
Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computat…
Data-Driven Deep Learning Based Feedback Linearization of Systems with Unknown Dynamics
Raktim Gautam Goswami, Prashanth Krishnamurthy, Farshad Khorrami
A methodology is developed to learn a feedback linearization (i.e., nonlinear change of coordinates and input transformation) using a data-driven approach for a single input contro…
Learning a Better Control Barrier Function
Bolun Dai, Prashanth Krishnamurthy, Farshad Khorrami
Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constrai…
State Constrained Stochastic Optimal Control Using LSTMs
Bolun Dai, Prashanth Krishnamurthy, Andrew Papanicolaou +1
In this paper, we propose a new methodology for state constrained stochastic optimal control (SOC) problems. The solution is based on past work in solving SOC problems using forwar…