arXiv subjects

Krishanu Nath

Publications and source records attributed to Krishanu Nath.

3 recordsLinked to original sources

arXiv2026

Robust Fixed-Time Model Reference Adaptive Control

This article proposes a Model Reference Adaptive Control (MRAC) strategy to achieve fixed-time convergence of parameter estimation and tracking errors for unknown linear time-invariant systems, without relying on the persistence of excitation condition. Instead, it employs a less restrictive initial/interval excitation condition on the regressor matrix, enhancing practicality and ease of implementation in real-world scenarios. Our primary contribution is a novel parameter update law within the indirect MRAC framework, ensuring that parameter estimates converge within a fixed time, once the initial/interval excitation condition is met. This approach simplifies the practical requirements for adaptive control while guaranteeing robust performance against parameter uncertainty and external disturbances. Simulation results provide a comparison with the current literature to validate the effectiveness of this approach.

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arXiv2025

An Event-based State Estimation Approach for Positive Systems with Positive Observers

This article addresses the problem of state observer design for continuous-time linear positive networked systems. Considering the bandwidth constraint in the communication network, an event-measurement-based positive observer design is proposed. The physical interpretation of a positive observer differs from that of a general observer. Its primary goal is to ensure that all state estimates remain non-negative at all times. Using output measurements, a law with weighted sampling error is used to determine the sampling sequence between the system and the observer. The observer dynamics are designed using the standard Luenberger structure with the event-based sampled output information, which is updated only when an event occurs. Assuming observability and sufficient conditions for the positivity of the system, the asymptotic stability of the observer dynamics with sampled information is established. Sufficient conditions of stability and positivity are derived using linear matrix inequalities. Moreover, the design ensures that the event-based architecture is free from Zeno behavior, ensuring a positive minimum bound on the inter-execution time. In addition, numerical simulations on a three-tank system having variable cross-sections are used to demonstrate the efficacy of the proposed event-based positive observer.

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arXiv2025

Deep Neuro-Adaptive Sliding Mode Controller for Higher-Order Heterogeneous Nonlinear Multi-Agent Teams with Leader

This letter proposes a deep neural network (DNN)-based neuro-adaptive sliding mode control (SMC) strategy for leader-follower tracking in multi-agent systems with higher-order, heterogeneous, nonlinear, and unknown dynamics under external disturbances. The DNN is used to compensate the unknown nonlinear dynamics with higher accuracy than shallow neural networks (NNs) and SMC ensures robust tracking. This framework employs restricted potential functions within a set-theoretic paradigm to ensure system trajectories remain bounded within a compact set, improving robustness against approximation errors and external disturbances. The control scheme is grounded in non-smooth Lyapunov stability theory, with update laws derived for both inner and outer layer network weights of DNN. A numerical example is simulated that showcases the proposed controller's effectiveness, adaptability, and robustness.

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