arXiv subjects

Chidre Shravista Kashyap

Publications and source records attributed to Chidre Shravista Kashyap.

4 recordsLinked to original sources

arXiv2026

Derivative-Free Generalized Multivariable Super-Twisting Control for Constrained Euler-Lagrange Systems

Robotic manipulators performing payload lift-and-transfer must track prescribed trajectories under abrupt load changes, with limited actuation and inaccurate plant knowledge. Such plants are inherently described by multivariable Euler--Lagrange~(EL) dynamics with cross-coupling established through inertial and Coriolis terms. The super-twisting algorithm~(STA) is the standard approach to regulating a first-order sliding mode~(FOSM) in such settings, yet existing designs are largely restricted to scalar sliding variables, and rest on a disturbance-derivative assumption that EL systems violate. In particular, the FOSM dynamics depend on the disturbance and become discontinuous under the step changes in load produced by payload engagement and release, rendering that derivative ill-defined. This study resolves both with a derivative-free generalized multivariable super-twisting framework. The discontinuous sign-function integral term is replaced with a continuous fractional-power term whose dynamics are independent of the disturbance derivative, while the known state-dependent scaling of the perturbation is incorporated into both the control law and the adaptive gain update. A time-shifting barrier function guarantees prescribed-time convergence to a user-specified bound independently of initial conditions, with the design relying only on mass-matrix norm bounds. Simulation and experimental results on a 7-DoF manipulator under actuator saturation are used to validate the proposed scheme.

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arXiv2026

Generalized Momenta-Based Koopman Formalism for Robust Control of Euler-Lagrangian Systems

This paper presents a novel Koopman operator formulation for Euler Lagrangian dynamics that employs an implicit generalized momentum-based state space representation, which decouples a known linear actuation channel from state dependent dynamics and makes the system more amenable to linear Koopman modeling. By leveraging this structural separation, the proposed formulation only requires to learn the unactuated dynamics rather than the complete actuation dependent system, thereby significantly reducing the number of learnable parameters, improving data efficiency, and lowering overall model complexity. In contrast, conventional explicit formulations inherently couple inputs with the state dependent terms in a nonlinear manner, making them more suitable for bilinear Koopman models, which are more computationally expensive to train and deploy. Notably, the proposed scheme enables the formulation of linear models that achieve superior prediction performance compared to conventional bilinear models while remaining substantially more efficient. To realize this framework, we present two neural network architectures that construct Koopman embeddings from actuated or unactuated data, enabling flexible and efficient modeling across different tasks. Robustness is ensured through the integration of a linear Generalized Extended State Observer (GESO), which explicitly estimates disturbances and compensates for them in real time. The combined momentum-based Koopman and GESO framework is validated through comprehensive trajectory tracking simulations and experiments on robotic manipulators, demonstrating superior accuracy, robustness, and learning efficiency relative to state of the art alternatives.

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arXiv2025

Periodic Event-Triggered Prescribed Time Control of Euler-Lagrange Systems under State and Input Constraints

This article proposes a periodic event-triggered adaptive barrier control policy for the trajectory tracking problem of perturbed Euler-Lagrangian systems with state, input, and temporal (SIT) constraints. In particular, an approximation-free adaptive-barrier control architecture is designed to ensure prescribed-time convergence of the tracking error to a prescribed bound while rejecting exogenous disturbances. In contrast to existing approaches that necessitate continuous real-time control action, the proposed controller generates event-based updates through periodic evaluation of the triggering condition. Additionally, we derive an upper bound on the monitoring period by analysing the performance degradation of the filtered tracking error to facilitate periodic evaluation of the event-triggered strategy. To this end, a time-varying threshold function is considered in the triggering mechanism to reduce the number of triggers during the transient phase of system behaviour. Notably, the proposed design avoids Zeno behaviour and precludes the need for continuous monitoring of the triggering condition. A simulation and experimental study is undertaken to demonstrate the efficacy of the proposed control scheme.

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arXiv2025

Tracking Control of Euler-Lagrangian Systems with Prescribed State, Input, and Temporal Constraints

The synthesis of a smooth tracking control for Euler-Lagrangian (EL) systems under stringent state, input, and temporal (SIT) constraints is challenging. In contrast to existing methods that utilize prior knowledge of EL model parameters and uncertainty bounds, this study proposes an approximation-free adaptive barrier function-based control policy to ensure local prescribed time convergence of tracking error under state and input constraints. The proposed approach uses smooth time-based generator functions embedded in the filtered tracking error, which is combined with a saturation function that limits control action and confines states within the prescribed limits by enforcing the time-varying bounds on the filtered tracking error. Importantly, corresponding feasibility conditions are derived pertaining to the minimum control authority, the maximum disturbance rejection capability of the control policy, and the viable set of initial conditions, illuminating the narrow operating domain of EL systems arising from the interplay of SIT constraints. Finally, the efficacy of the proposed approach is demonstrated using experimental and comparison studies.

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