arXiv subjects

Phanindra Tallapragada

Publications and source records attributed to Phanindra Tallapragada.

At least 19 recordsLinked to original sources

arXiv2026

Differentiable Reinforcement Learning for Path Tracking by an Agile Fish-Like Robot

Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.

cs.RO↗

arXiv2026

Flow primitives and infinitesimal generators of Perron-Frobenius and Koopman operators

The Koopman and the Perron-Frobenius operators are increasingly becoming popular in the control of complex nonlinear systems such as in a wide variety of robotics problems and flow control. This is in addition to the wide interest in the application of operator methods for better understanding of fluid flows. A particular problem of relevance to all such applications is, how does the Koopman or the Perron-Frobenius (PF) operator change if the underlying vector field of the dynamical system undergoes small changes or if two vector fields are added. The current numerical methods rely on significant computations and model or parameter changes to the dynamical system often require all the computations to be repeated. This paper reports on a novel approach to the computation of the approximate PF and Koopman operators in such cases. The approach makes use of the exponentials of the infinitesimal generators of these operators. It is shown that this approximation depends on the Lie bracket of the vector field and the perturbation vector field. Examples are described where the Koopman and PF operators are constructed from operators of primitive flows and for cases where the model parameters undergo perturbations.

nlin.CD↗

arXiv2026

Koopman Operator Framework for Modeling and Control of Off-Road Vehicle on Deformable Terrain

This work presents a hybrid physics-informed and data-driven modeling framework for predictive control of autonomous off-road vehicles operating on deformable terrain. Traditional high-fidelity terramechanics models are often too computationally demanding to be directly used in control design. Modern Koopman operator methods can be used to represent the complex terramechanics and vehicle dynamics in a linear form. We develop a framework whereby a Koopman linear system can be constructed using data from simulations of a vehicle moving on deformable terrain. For vehicle simulations, the deformable-terrain terramechanics are modeled using Bekker-Wong theory, and the vehicle is represented as a simplified five-degree-of-freedom (5-DOF) system. The Koopman operators are identified from large simulation datasets for sandy loam and clay using a recursive subspace identification method, where Grassmannian distance is used to prioritize informative data segments during training. The advantage of this approach is that the Koopman operator learned from simulations can be updated with data from the physical system in a seamless manner, making this a hybrid physics-informed and data-driven approach. Prediction results demonstrate stable short-horizon accuracy and robustness under mild terrain-height variations. When embedded in a constrained MPC, the learned predictor enables stable closed-loop tracking of aggressive maneuvers while satisfying steering and torque limits.

eess.SY↗

arXiv2024

Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learning

Stabilizing vertical dynamics for on-road and off-road vehicles is an important research area that has been looked at mostly from the point of view of ride comfort. The advent of autonomous vehicles now shifts the focus more towards developing stabilizing techniques from the point of view of onboard proprioceptive and exteroceptive sensors whose real-time measurements influence the performance of an autonomous vehicle. The current solutions to this problem of managing the vertical oscillations usually limit themselves to the realm of active suspension systems without much consideration to modulating the vehicle velocity, which plays an important role by the virtue of the fact that vertical and longitudinal dynamics of a ground vehicle are coupled. The task of stabilizing vertical oscillations for military ground vehicles becomes even more challenging due lack of structured environments, like city roads or highways, in off-road scenarios. Moreover, changes in structural parameters of the vehicle, such as mass (due to changes in vehicle loading), suspension stiffness and damping values can have significant effect on the controller's performance. This demands the need for developing deep learning based control policies, that can take into account an extremely large number of input features and approximate a near optimal control action. In this work, these problems are addressed by training a deep reinforcement learning agent to minimize the vertical acceleration of a scaled vehicle travelling over bumps by controlling its velocity.

cs.RO↗

arXiv2024

Controlled fluid transport by the collective motion of microrotors

Torque-driven microscale swimming robots, or microrotors, hold significant potential in biomedical applications such as targeted drug delivery, minimally invasive surgery, and micromanipulation. This paper addresses the challenge of controlling the transport of fluid volumes using the flow fields generated by interacting groups of microrotors. Our approach uses polynomial chaos expansions to model the time evolution of fluid particle distributions and formulate an optimal control problem, which we solve numerically. We implement this framework in simulation to achieve the controlled transport of an initial fluid particle distribution to a target destination while minimizing undesirable effects such as stretching and mixing. We consider the case where translational velocities of the rotors are directly controlled, as well as the case where only torques are controlled and the rotors move in response to the collective flow fields they generate. We analyze the solution of this optimal control problem by computing the Lagrangian coherent structures of the associated flow field, which reveal the formation of transport barriers that efficiently guide particles toward their target. This analysis provides insights into the underlying mechanisms of controlled transport.

math.OC↗

arXiv2024

Online learning of Koopman operator using streaming data from different dynamical regimes

The paper presents a framework for online learning of the Koopman operator using streaming data. Many complex systems for which data-driven modeling and control are sought provide streaming sensor data, the abundance of which can present computational challenges but cannot be ignored. Streaming data can intermittently sample dynamically different regimes or rare events which could be critical to model and control. Using ideas from subspace identification, we present a method where the Grassmannian distance between the subspace of an extended observability matrix and the streaming segment of data is used to assess the `novelty' of the data. If this distance is above a threshold, it is added to an archive and the Koopman operator is updated if not it is discarded. Therefore, our method identifies data from segments of trajectories of a dynamical system that are from different dynamical regimes, prioritizes minimizing the amount of data needed in updating the Koopman model and furthermore reduces the number of basis functions by learning them adaptively. Therefore, by dynamically adjusting the amount of data used and learning basis functions, our method optimizes the model's accuracy and the system order.

eess.SY↗

arXiv2024

Controlled transport of fluid particles by microrotors in a Stokes flow using linear transfer operators

The manipulation of a collection of fluid particles in a low Reynolds number environment has several important applications. As we demonstrate in this paper, this manipulation problem is related to the scientific question of how fluid flow structures direct Lagrangian transport. We investigate this problem of directing the transport by manipulating the flow, specifically in the Stokes flow context, by controlling the strengths of two rotors fixed in space. We demonstrate a novel dynamical systems approach for this problem and apply this method to several scenarios of Stokes flow in unbounded and bounded domains. Further, we show that the time-varying flow field produced by the optimal control can be understood in terms of dynamical structures such as coherent sets that define Lagrangian transport. We model the time evolution of the fluid particle density using finite dimensional approximations of the Liouville operators for the micro-rotor flow fields. Using these operators, the particle transport problem is framed as an optimal control problem, which we solve numerically. This framework is then applied to the problem of transporting a blob of fluid particles in domains with different boundary conditions: free space, near to a plane wall, in a circular confinement, and the transport of two distributions of particles to a common target. These examples demonstrate the effectiveness of the proposed framework and also shed light on the effects of boundaries on the ability to achieve a desired fluid transport using a rotor-driven flow.

physics.flu-dyn↗

arXiv2023

Feel the Force: From Local Surface Pressure Measurement to Flow Reconstruction in Fluid-Structure Interaction

Drawing inspiration from the lateral lines of fish, the inference of flow characteristics via surface-based data has drawn considerable attention. The current approaches often rely on analytical methods tailored exclusively for potential flows or utilize black-box machine learning algorithms to estimate a specific set of flow parameters. In contrast to a black box machine learning approach, we demonstrate that it is possible to identify certain modes of fluid flow and then reconstruct the entire flow field from these modes. We use Dynamic Mode Decomposition (DMD) to parametrize complex, dynamic features across the entire flow field. We then leverage deep neural networks to infer the DMD modes of the pressure and velocity fields within a large, unsteady flow domain, employing solely a time series of pressure measurements collected on the surface of an immersed obstacle. Our methodology is successfully demonstrated to diverse fluid-structure interaction scenarios, including cases with both free oscillations in the wake of a cylinder and forced oscillations of tandem cylinders, demonstrating its versatility and robustness.

physics.flu-dyn↗

arXiv2023

A Pendulum-Driven Legless Rolling Jumping Robot

In this paper, we present a novel rolling, jumping robot. The robot consists of a driven pendulum mounted to a wheel in a compact, lightweight, 3D printed design. We show that by driving the pendulum to shift the robot's weight distribution, the robot is able to obtain significant rolling speed, achieve jumps of up to 2.5 body lengths vertically, and clear horizontal distances of over 6 body lengths. The robot's dynamic model is derived and simulation results indicate that it is consistent with the rolling motion and jumping observed on the robot. The ability to both roll and jump effectively using a minimalistic design makes this robot unique and could inspire the use of similar mechanisms on robots intended for applications in which agile locomotion on unstructured terrain is necessary, such as disaster response or planetary exploration.

cs.RO↗

arXiv2023

Parametric roll oscillations of a hydrodynamic Chaplygin sleigh

Biomimetic underwater robots use lateral periodic oscillatory motion to propel forward, which is seen in most fishes known as body caudal fin (BCF) propulsion. The lateral oscillatory motion makes slender-bodied fish-like robots roll unstable. Unlike the case of human-engineered aquatic robots, many species of fish can stabilize their roll motion to perturbations arising from the periodic motions of propulsors. To first understand the origin of the roll instability, the objective of this paper is to analyze the parameters affecting the roll-angle stability of an autonomous fish-like underwater swimmer. Eschewing complex models of fluid-structure interaction, we instead consider the roll motion of a nonholonomic system inspired by the Chaplygin sleigh, whose center of mass is above the ground. In past work, the dynamics of a fish-like periodic swimmer have been shown to be similar to that of a Chaplygin sleigh. The Chaplygin sleigh is propelled by periodic torque in the yaw direction. The roll dynamics of the Chaplygin sleigh are linearized and around a nominal limit cycle solution of the planar hydrodynamic Chaplygin sleigh in the reduced velocity space. It is shown that the roll dynamics are then described as a nonhomogeneous Mathieu equation where the periodic yaw motion provides the parametric excitation. We study the added mass effects on the sleigh's linear dynamics and use the Floquet theory to investigate the roll stability due to parametric excitation. We show that fast motions of the model for swimming are frequently associated with roll instability. The paper thus sheds light on the fundamental mechanics that present trade-offs between speed, efficiency, and stability of motion of fish-like robots.

physics.flu-dyn↗

arXiv2023

Controlled density transport using Perron Frobenius generators

We consider the problem of the transport of a density of states from an initial state distribution to a desired final state distribution through a dynamical system with actuation. In particular, we consider the case where the control signal is a function of time, but not space; that is, the same actuation is applied at every point in the state space. This is motivated by several problems in fluid mechanics, such as mixing and manipulation of a collection of particles by a global control input such as a uniform magnetic field, as well as by more general control problems where a density function describes an uncertainty distribution or a distribution of agents in a multi-agent system. We formulate this problem using the generators of the Perron-Frobenius operator associated with the drift and control vector fields of the system. By considering finite-dimensional approximations of these operators, the density transport problem can be expressed as a control problem for a bilinear system in a high-dimensional, lifted state. With this system, we frame the density control problem as a problem of driving moments of the density function to the moments of a desired density function, where the moments of the density can be expressed as an output which is linear in the lifted state. This output tracking problem for the lifted bilinear system is then solved using differential dynamic programming, an iterative trajectory optimization scheme.

eess.SY↗

arXiv2022

Terrain parameter estimation from proprioceptive sensing of the suspension dynamics in offroad vehicles

Offroad vehicle movement has to contend with uneven and uncertain terrain which present challenges to path planning and motion control for both manned and unmanned ground vehicles. Knowledge of terrain properties can allow a vehicle to adapt its control and motion planning algorithms. Terrain properties, however, can change on time scales of days or even hours, necessitating their online estimation. The kinematics and, in particular the oscillations experienced by an offroad vehicle carry a signature of the terrain properties. These terrain properties can thus be estimated from proprioceptive sensing of the vehicle dynamics with an appropriate model and estimation algorithm. In this paper, we show that knowledge of the vertical dynamics of a vehicle due to its suspension can enable faster and more accurate estimation of terrain parameters. The paper considers a five degree of freedom model that combines the well known half-car and bicycle models. We show through simulation that the sinkage exponent, a parameter that can significantly influence the wheel forces from the terrain and thus greatly impact the vehicle trajectory, can be estimated from measurements of the vehicle's linear acceleration and rotational velocity, which can be readily obtained from an onboard IMU. We show that modelling the vertical vehicle dynamics can lead to significant improvement in both the estimation of terrain parameters and the prediction of the vehicle trajectory.

cs.RO↗

arXiv2019

Dynamics of groups of magnetically driven artificial microswimmers

Magnetically driven artificial microswimmers have the potential to revolutionize many biomedical technologies, such as minimally-invasive microsurgery, micro-particle manipulation, and localized drug delivery. However, many of these applications will require the controlled dynamics of teams of these micro-robots with minimal feedback. In this work, we study the motion and fluid dynamics produced by groups of artificial microswimmers driven by a torque induced through a uniform, rotating magnetic field. Through Stokesian dynamics simulations, we show that the swimmer motion produces a rotational velocity field in the plane orthogonal to the direction of the magnetic field's rotation, which causes two interacting swimmers to move in circular trajectories in this plane around a common center. The resulting over all motion is on a helical trajectory for the swimmers. We compare the highly rotational velocity field of the fluid to the velocity field generated by a rotlet, the point-torque singularity of Stokes flows, showing that this is a reasonable approximation on the time average. Finally, we study the motion of larger groups of swimmers and show that these groups tend to move coherently, especially when swimmer magnetizations are uniform. This coherence is achieved because the group center remains almost constant in the plane orthogonal to the net motion of the swimmers. The results in the paper will prove useful for controlling the ensemble dynamics of small collections of magnetic swimmers.

physics.flu-dyn↗

arXiv2019

Magnetically actuated artificial microswimmers as mobile microparticle manipulators

Micro-scale swimming robots have been envisaged for many medical applications such as targeted drug delivery, where the microrobot will be expected to navigate in a fluid through channels carrying a payload. Alternatively, in many cases, such a payload does not have to be physically bound to the swimmer, but may be instead manipulated and steered through the channel by the microrobot. We investigate this problem of contactless manipulation of a microparticle by mobile microswimmer in a fluid at low Reynolds number. We consider a model of a magnetically actuated artificial microswimmer, whose locomotion through a fluid induces a disturbance velocity field in the fluid, that then acts to propel a cargo particle in its vicinity. The problem investigated in this paper is therefore one of coupled locomotion-manipulation of two bodies in a fluid. The magnetic swimmer's motion is actuated by an externally applied magnetic field of constant strength but whose direction rotates at a constant rate in a plane. The swimmer propels itself in the direction perpendicular to this plane if the frequency associated with the periodic magnetic field is above a critical frequency. Below this critical frequency, the swimmer tumbles in place without net locomotion. The coupled fluid-swimmer-cargo particle dynamics are solved numerically using the method of Stokesian dynamics. The induced motion of the cargo particle is shown to be controllable. This is achieved by switching the planes of rotation of the magnetic field and switching frequency of the magnetic field above and below the critical frequency. While a swimmer with a specific geometry has been used in the model, the results of this paper are applicable to swimmers with other geometries and means of propulsion. The results of this paper show that microswimmers can be utilized as mobile manipulators of microparticles in a fluid.

cond-mat.soft↗

arXiv2019

Chaotic mixing using micro-rotors in a confined domain

In this work we study chaotic mixing induced by point micro-rotors in a bounded two dimensional Stokes flow. The dynamics of the pair of rotors, modeled as rotlets, are non Hamiltonian in the bounded domain and produce chaotic advection of fluid tracers in subsets of the domain. A complete parametric investigation of the fluid mixing as a function of the initial locations of the rotlets is performed based on pseudo phase portraits. The mixing of fluid tracers as a function of relative positions of micro-rotors is studied using finite time entropy and locational entropy. The finite time locational entropy is used to identify regions of the fluid that produce good vs poor mixing and this is visualized by the stretching and folding of blobs of tracer particles. Unlike the case of the classic blinking vortex dynamics, the velocity field of the flow modeled using rotlets inside a circular boundary is smooth in time and satisfies the no slip boundary condition. This makes the considered model a more realistic case for studies of mixing in microfluidic devices using magnetic actuated microspheres.

nlin.CD↗

arXiv2018

Chaotic Dynamics of the Chaplygin sleigh with a passive internal rotor

The Chaplygin sleigh is a classic example of a nonholonomically constrained mechanical system. The sleigh's motion always converges to a straight line whose slope is entirely determined by the initial configuration and velocity of the sleigh. We consider the motion of a modified Chaplygin sleigh that contains a passive internal rotor. We show that the presence of even a rotor with small inertia modifies motion of the sleigh dramatically. A generic trajectory of the sleigh in a reduced velocity space exhibits two distinct transient phases before converging to a chaotic attractor. We demonstrate this through numerics. In recent work the dynamics of the Chaplygin sleigh have also been shown to be similar to that of a fish like body in an inviscid fluid. The influence of a passive degree of freedom on the motion of the Chaplygin sleigh points to several possible applications in controlling the motion of the nonholonomically constrained terrestrial and aquatic robots.

nlin.CD↗

arXiv2017

A globally stable attractor that is locally unstable everywhere

We construct two examples of invariant manifolds that despite being locally unstable at every point in the transverse direction are globally stable. Using numerical simulations we show that these invariant manifolds temporarily repel nearby trajectories but act as global attractors. We formulate an explanation for such global stability in terms of the `rate of rotation' of the stable and unstable eigenvectors spanning the normal subspace associated with each point of the invariant manifold. We discuss the role of this rate of rotation on the transitions between the stable and unstable regimes.

nlin.CD↗

arXiv2011

Computation of Finite Time Lyapunov Exponents using the Perron-Frobenius operator

The problem of phase space transport which is of interest both theoretically and from the point of view of applications has been investigated extensively using geometric and probabilistic methods. Two of the important tools for this that emerged in the last decade are the Finite time Lyapunov exponents (FTLE) and the Perron-Frobenius operator. The relationship between these approaches has not been clearly understood so far. In this paper a methodology is presented to compute the FTLE from the Perron-Frobenius operator, thus providing a step towards combining both the methods into a common framework.

nlin.CD↗