12 citations · 20 across the 23 of their papers we have counts for
24 papers
Temporal-Logical Stochastic Processes
Venkatraman Renganathan
We develop a measure-theoretic framework for Temporal-Logical Stochastic Processes (TLSP), in which temporal-logic specifications induce time-indexed stochastic observables capturi…
HJ-Gauss: A Monte-Carlo HJ Reachability Scheme
Lekan Molu, Venkatraman Renganathan, Namhoon Cho
Backward reachable sets or tubes (BRS/Ts), evaluated with grid-based level-set methods over viscous Hamilton-Jacobi (HJ) equations, furnish principled reachability certificates for…
Cooperative missile guidance design using Distributed Nonlinear Dynamic Inversion
Sabyasachi Mondal, Bhaskar Biswas, Venkatraman Renganathan
This paper presents a new cooperative guidance algorithm based on Distributed Nonlinear Dynamic Inversion (DNDI) to demonstrate a coordinated missile attack.
Distance Between Stochastic Linear Systems
Venkatraman Renganathan, Sei Zhen Khong
While the existing stochastic control theory is well equipped to handle dynamical systems with stochastic uncertainties, a paradigm shift using distance measure based decision maki…
Probabilistic Robustness in the Gap Metric
Venkatraman Renganathan
Uncertainties influencing the dynamical systems pose a significant challenge in estimating the achievable performance of a controller aiming to control such uncertain systems. When…
Predictive & Trust-based Multi-Agent Coordination
Venkatraman Renganathan, Sabyasachi Mondal, Antonios Tsourdos
This paper presents a trust-based predictive multi-agent consensus protocol that analyses neighbours' anticipation data and makes coordination decisions. Agents in the network shar…
Stereographic Projection of Probabilistic Frequency-Domain Uncertainty
Anton Nystrom, Venkatraman Renganathan, Michael Cantoni
This paper investigates the stereographic projection of points along the Nyquist plots of single input single output (SISO) linear time invariant (LTI) systems subject to probabili…
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
Adrien Banse, Venkatraman Renganathan, Raphaël M. Jungers
We extend the notion of Cantor-Kantorovich distance between Markov chains introduced by (Banse et al., 2023) in the context of Markov Decision Processes (MDPs). The proposed metric…
Distributed Adaptive Control for Uncertain Networks
Venkatraman Renganathan, Anders Rantzer, Olle Kjellqvist
Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed f…
Regret and Conservatism of Distributionally Robust Constrained Stochastic Model Predictive Control
Maik Pfefferkorn, Venkatraman Renganathan, Rolf Findeisen
We analyse the conservatism and regret of distributionally robust (DR) stochastic model predictive control (SMPC) when using moment-based ambiguity sets for modeling unknown uncert…
An Online Learning Analysis of Minimax Adaptive Control
Venkatraman Renganathan, Andrea Iannelli, Anders Rantzer
We present an online learning analysis of minimax adaptive control for the case where the uncertainty includes a finite set of linear dynamical systems. Precisely, for each system…
Regret Analysis for Risk-aware Linear Quadratic Control
Venkatraman Renganathan, Dongjun Wu
This paper investigates the regret associated with the Distributionally Robust Control (DRC) strategies used to address multistage optimization problems where the involved probabil…
Distributionally Robust Covariance Steering with Optimal Risk Allocation
Venkatraman Renganathan, Joshua Pilipovsky, Panagiotis Tsiotras
This article extends the optimal covariance steering (CS) problem for discrete time linear stochastic systems modeled using moment-based ambiguity sets. To hedge against the uncert…
Path Planning Using Wassertein Distributionally Robust Deep Q-learning
Cem Alpturk, Venkatraman Renganathan
We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation i…
Distributed Implementation of Minimax Adaptive Controller For Finite Set of Linear Systems
Venkatraman Renganathan, Anders Rantzer, Olle Kjellqvist
This paper deals with a distributed implementation of minimax adaptive control algorithm for networked dynamical systems modeled by a finite set of linear models. To hedge against…
Distributionally Robust RRT with Risk Allocation
Kajsa Ekenberg, Venkatraman Renganathan, Björn Olofsson
An integration of distributionally robust risk allocation into sampling-based motion planning algorithms for robots operating in uncertain environments is proposed. We perform non-…
Leveraging second-order information for tuning of inverse optimal controllers
Taouba Jouini, Zhiyong Sun, Venkatraman Renganathan
We leverage second-order information for tuning of inverse optimal controllers for a class of discrete-time nonlinear input-affine systems. For this, we select the input penalty ma…
History Data Driven Distributed Consensus in Networks
Venkatraman Renganathan, Angela Fontan, Karthik Ganapathy
The association of weights in a distributed consensus protocol quantify the trust that an agent has on its neighbors in a network. An important problem in such networked systems is…
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Venkatraman Renganathan, Sleiman Safaoui, Aadi Kothari +3
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and predict…
Anomaly Detection Under Multiplicative Noise Model Uncertainty
Venkatraman Renganathan, Benjamin J. Gravell, Justin Ruths +1
State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are susceptible to model risk as…
Risk-Averse RRT* Planning with Nonlinear Steering and Tracking Controllers for Nonlinear Robotic Systems Under Uncertainty
Sleiman Safaoui, Benjamin J. Gravell, Venkatraman Renganathan +1
We propose a two-phase risk-averse architecture for controlling stochastic nonlinear robotic systems. We present Risk-Averse Nonlinear Steering RRT* (RANS-RRT*) as an RRT* variant…
Higher-Order Moment-Based Anomaly Detection
Venkatraman Renganathan, Navid Hashemi, Justin Ruths +1
The identification of anomalies is a critical component of operating complex, and possibly large-scale and geo-graphically distributed cyber-physical systems. While designing anoma…
Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints
Venkatraman Renganathan, Iman Shames, Tyler H. Summers
Safely deploying robots in uncertain and dynamic environments requires a systematic accounting of various risks, both within and across layers in an autonomy stack from perception…
Distributionally Robust Tuning of Anomaly Detectors in Cyber-Physical Systems with Stealthy Attacks
Venkatraman Renganathan, Navid Hashemi, Justin Ruths +1
Designing resilient control strategies for mitigating stealthy attacks is a crucial task in emerging cyber-physical systems. In the design of anomaly detectors, it is common to ass…