arXiv subjects

Soutrik Bandyopadhyay

Publications and source records attributed to Soutrik Bandyopadhyay.

6 recordsLinked to original sources

arXiv2026

Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric

Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on real-time search progress, maintaining bounded heuristic behavior while maintaining superior clearance. To enable rigorous evaluation, we introduce the OptiSafe index, a normalized metric that quantifies the trade-off between safety and optimality. Extensive evaluation across 10 environments shows that UPP achieves a 0.94 OptiSafe score in cluttered environments, compared with 0.22-0.85 for existing methods, with only 0.5-1% path-length overhead in simulation and a 100% success rate. Hardware validation on TurtleBot confirms practical advantages despite sim-to-real gaps.

cs.RO↗

arXiv2025

Initial Excitation-based Adaptive Observers for Discrete-Time LTI Systems

In practical applications, the efficacy of a control algorithm relies critically on the accurate knowledge of the parameters and states of the underlying system. However, obtaining these quantities in practice is often challenging. Adaptive observers address this issue by performing simultaneous state and parameter estimation using only input-output measurements. While many adaptive observer designs exist for continuous-time systems, their discrete-time counterparts remain relatively unexplored. This paper proposes an initial excitation (IE)-based adaptive observer for discrete-time linear time-invariant systems. In contrast to conventional designs that rely on the persistence of excitation condition, which requires continuous excitation and infinite control effort, the proposed method does not require excitation for infinite time, thus making it more practical for stabilization tasks. We employ a two-layer filtering structure and a normalized gradient descent-based update law for learning the unknown parameters. We also propose modifying the regressors to enhance information extraction, leading to faster convergence. Rigorous theoretical analysis guarantees bounded and exponentially converging estimates of both states and parameters under the IE condition, and simulation results validate the efficacy of the proposed design.

eess.SY↗

arXiv2025

Behavioral Inequalities

We introduce behavioral inequalities as a way to model dynamical systems defined by inequalities among their variables of interest. We claim that such a formulation enables the representation of safety-aware dynamical systems, systems with bounds on disturbances, practical design limits and operational boundaries, etc. We develop a necessary and sufficient condition for the existence of solutions to such behavioral inequalities and provide a parametrization of solutions when they exist. Finally, we show the efficacy of the proposed method in two practical examples.

eess.SY↗

arXiv2024

Lagrangian-based online safe reinforcement learning for state-constrained systems

This paper proposes a safe reinforcement learning (RL) algorithm that approximately solves the state-constrained optimal control problem for continuous-time uncertain nonlinear systems. We formulate the safe RL problem as the minimization of a Lagrangian that includes the cost functional and a user-defined barrier Lyapunov function (BLF) encoding the state constraints. We show that the analytical solution obtained by the application of Karush-Kuhn-Tucker (KKT) conditions contains a state-dependent expression for the Lagrange multiplier, which is a function of uncertain terms in the system dynamics. We argue that a naive estimation of the Lagrange multiplier may lead to safety constraint violations. To obviate this challenge, we propose an Actor-Critic-Identifier-Lagrangian (ACIL) algorithm that learns optimal control policies from online data without compromising safety. We provide safety and boundedness guarantees with the proposed algorithm and compare its performance with existing offline/online RL methods via a simulation study.

eess.SY↗

arXiv2023

Safe Q-learning for continuous-time linear systems

Q-learning is a promising method for solving optimal control problems for uncertain systems without the explicit need for system identification. However, approaches for continuous-time Q-learning have limited provable safety guarantees, which restrict their applicability to real-time safety-critical systems. This paper proposes a safe Q-learning algorithm for partially unknown linear time-invariant systems to solve the linear quadratic regulator problem with user-defined state constraints. We frame the safe Q-learning problem as a constrained optimal control problem using reciprocal control barrier functions and show that such an extension provides a safety-assured control policy. To the best of our knowledge, Q-learning for continuous-time systems with state constraints has not yet been reported in the literature.

eess.SY↗

arXiv2022

Safe reinforcement learning control for continuous-time nonlinear systems without a backup controller

This paper proposes an on-policy reinforcement learning (RL) control algorithm that solves the optimal regulation problem for a class of uncertain continuous-time nonlinear systems under user-defined state constraints. We formulate the safe RL problem as the minimization of the Hamiltonian subject to a constraint on the time-derivative of a barrier Lyapunov function (BLF). We subsequently use the analytical solution of the optimization problem to modify the Actor-Critic-Identifier architecture to learn the optimal control policy safely. The proposed method does not require the presence of external backup controllers, and the RL policy ensures safety for the entire duration. The efficacy of the proposed controller is demonstrated on a class of Euler-Lagrange systems.

eess.SY↗