SearcharxivSearch

arXiv subjects

Ankit Goel

Publications and source records attributed to Ankit Goel.

At least 19 recordsLinked to original sources

Performance-based Adaptation Termination for Preventing Parameter Drift in Adaptive Vibration Suppression

Parameter drift remains a practical limitation of adaptive vibration control systems, particularly when persistence of excitation diminishes after disturbance attenuation or when measurement disturbances render the adaptation problem ill-conditioned. This issue is especially relevant in flexible structures, where adaptive controllers may continue updating parameters even after satisfactory vibration suppression has been achieved. This paper proposes a computationally efficient root-mean-square (RMS)-based stopping criterion that mitigates parameter drift by freezing adaptation once satisfactory vibration attenuation has been sustained over a prescribed interval. The criterion is implemented by recursively computing an exponentially weighted moving RMS of the performance variable and requires negligible additional computational effort. The proposed mechanism is integrated with retrospective cost adaptive control (RCAC) and validated through both numerical simulations and closed-loop experiments on a cantilever-beam vibration-suppression platform with a noncollocated actuator configuration. Without the proposed stopping criterion, continued adaptation after disturbance rejection leads to gradual parameter drift and degradation of vibration suppression performance. When the RMS-based threshold is enabled, controller parameters remain bounded, and the achieved vibration attenuation is preserved. The results demonstrate that a simple performance-based monitoring mechanism can effectively prevent parameter drift in adaptive vibration control, retain the transient benefits of adaptation, and incur minimal computational overhead.

eess.SY

Exactness Certificates for Closed-Form CBF Safety-Filter Projections

For control-affine systems, standard and high-order control barrier function conditions are affine in the control input and are commonly enforced through quadratic-program-based safety filters. Although convex, these optimization problems may be undesirable in embedded, high-rate, or resource-limited implementations. This letter characterizes when the corresponding Euclidean projection can be recovered from the affine inequalities violated by a nominal control input. Given a nominal input, we form the violated set and compute the minimum-norm correction that enforces the violated inequalities with equality. This violated-set correction is closed form, but it need not equal the exact Euclidean projection onto the full feasible set. The main result gives a necessary and sufficient exactness certificate based on primal and dual feasibility, followed by structural sufficient conditions involving interactions among affine-inequality normals. An online certification algorithm is then presented to determine when the closed-form update is exact. When the certificate fails, a finite active-set search can be used to recover the exact projection. Numerical simulations illustrate that the violated-set correction can remain feasible while failing to be the exact projection due to dual infeasibility, and demonstrate computational speedup relative to a standard CBF-QP solver.

eess.SY

Feedback Linearization-Based Guidance with Zero-Dynamics Correction for Guaranteed Interception

This paper develops a guidance law for nonlinear interception using input-output feedback linearization (IOL). The engagement between a pursuer and an evader is modeled using point-mass dynamics, and a baseline IOL-based guidance law is constructed by regulating the angular rates of the line-of-sight (LOS) vector. While this approach yields stable input-output behavior, it does not constrain the internal (zero) dynamics of the system, which can result in non-intercepting trajectories despite successful regulation of the LOS rates. To address this limitation, a modified IOL-based guidance law is proposed that incorporates a correction mechanism to enforce convergence of the range. The resulting formulation ensures that LOS alignment corresponds to a closing trajectory, thereby enabling convergence of the pursuer to the evader for a broad class of initial engagement geometries. The proposed method retains the computational simplicity and real-time implementability of feedback linearization while improving closed-loop performance relative to classical guidance laws. Extensive Monte Carlo simulations over a wide range of initial conditions are conducted to evaluate the proposed method. The results demonstrate improved reliability, reduced miss distance, and consistent convergence compared to the baseline IOL and classical proportional navigation.

eess.SY

Model-Free Dynamic Mode Adaptive Control for Data-Driven Control Synthesis

This paper presents a model-free, data-driven control synthesis method called dynamic mode adaptive control (DMAC) for systems whose mathematical models are unavailable or unsuitable for classical control design. The proposed approach combines data-driven dynamics approximation with adaptive control synthesis to enable online controller design using measured system data. DMAC comprises two main components: a dynamics-approximation module and a controller-synthesis module. The dynamics approximation module estimates a local linear representation of the system dynamics directly from measurements using a matrix recursive least-squares algorithm with a forgetting factor. The estimated dynamics are then used to compute an online stabilizing controller with full-state feedback and integral action. Theoretical analysis establishes convergence properties of the recursive dynamics approximation and boundedness of the closed-loop system under the DMAC controller. The performance of the proposed method is demonstrated through numerical examples involving representative dynamical systems, including an unstable linear system, the Van der Pol oscillator, and the Burgers' equation. Sensitivity studies further demonstrate the robustness of DMAC with respect to both algorithm hyperparameters and variations in system parameters.

eess.SY

Adaptive Constraint-Lifting Control with Stability and Invariance Guarantees

This paper develops an adaptive tracking controller for a class of nonlinear systems with parametric uncertainty subject to state constraints. The system is characterized by a strict-feedback structure with unknown parameters entering both the drift and input channels. The objective is to design a control law, without knowledge of the unknown parameters, that guarantees closed-loop stability, achieves desired tracking performance, and ensures forward invariance of a prescribed safe set. An adaptive constraint-lifting framework is developed that transforms the constrained control problem into an equivalent unconstrained representation, enabling recursive controller synthesis in lifted coordinates. The proposed design integrates parameter estimation with constraint enforcement without requiring online optimization. A Lyapunov-based stability analysis, combined with the Barbashin-Krasovskii-LaSalle invariance principle, establishes boundedness of all closed-loop signals, asymptotic convergence of the system states to the desired equilibrium, and forward invariance of the safe set under uncertainty. In particular, the analysis characterizes the largest invariant set of the closed-loop system and guarantees convergence despite unknown parameters. The effectiveness of the proposed approach is demonstrated on a DC motor system with uncertain parameters, illustrating accurate tracking performance and safe operation.

math.OC

State-Constrained Control of Discrete-Time Nonlinear Systems via Constraint Lifting

This paper presents a constraint-enforcing control framework for a class of discrete-time strict-feedback nonlinear systems. The objective is to guarantee closed-loop stability while ensuring forward invariance of a prescribed safe set defined by state constraints. The proposed approach transforms the constrained control problem into an equivalent unconstrained one through smooth constraint-lifting mappings constructed using strictly increasing sigmoid functions. Controller synthesis is then performed in the lifted coordinates, enabling recursive backstepping design while preserving the admissibility of the constrained states. Conditions on the controller gains are derived to guarantee both asymptotic stability of the closed-loop system and forward invariance of the admissible domain of the lifting functions. The analysis also establishes a conditional deadbeat property for the second backstepping step once the system trajectory enters a region in which the lifting-domain admissibility conditions are satisfied. Numerical simulations of a constrained double-integrator system demonstrate the effectiveness of the proposed method in enforcing state constraints while tracking a reference command.

math.OC

A Constraint-Lifting Framework for Safe and Stable Nonlinear Control

This paper presents a constraint-lifting control framework for designing stabilizing controllers that guarantee the forward invariance of a prescribed safe set. State-of-the-art safety-enforcing methods, such as control barrier functions (CBFs) and model predictive control (MPC), typically rely on solving constrained optimization problems in real time and therefore may not yield an explicit control law that guarantees constraint satisfaction under all conditions. In contrast, the proposed approach develops an explicit control law for a class of nonlinear systems that ensures both asymptotic stabilization of a desired equilibrium and safety preservation of a user-defined set. The central idea is to lift the constrained state space into an unbounded domain using a sigmoid-based diffeomorphic mapping, synthesize the controller in the transformed coordinates, and then map it back to the original coordinates. To address numerical conditioning near constraint boundaries, a special class of Lyapunov candidate functions, called sigmoid integral functions, is introduced. A rigorous stability analysis, based on the Barbashi-Krasovskii-LaSalle invariance principle, establishes asymptotic convergence and safety guarantees. The efficacy of the proposed controller is demonstrated through a safe attitude-control problem.

math.OC

Adaptive Thrust Regulation in Solid-fuel Ramjet with Variable Geometry Inlet

This paper presents the application of a novel data-driven adaptive control technique, dynamic mode adaptive control (DMAC), to regulate thrust in a solid-fuel ramjet (SFRJ). A quasi-static one-dimensional model of SFRJ with a variable geometry inlet is developed to compute thrust. An adaptive tracking controller is then designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller designed around the identified model. Simulation results demonstrate that DMAC achieves accurate thrust regulation across a range of commanded profiles and operating conditions, without requiring an analytical model of the SFRJ. These findings indicate that DMAC provides a reliable and effective approach for model-free thrust regulation in an SFRJ with variable-geometry inlets as the control input.

math.OC

Thrust Regulation in a Solid Fuel Ramjet using Dynamic Mode Adaptive Control

This paper presents the application of a novel data-driven adaptive control technique, called dynamic mode adaptive control (DMAC), for regulating thrust in a solid fuel ramjet (SFRJ). A high-fidelity computational model incorporating compressible flow theory and equilibrium chemistry is used to simulate the combustion dynamics. An adaptive tracking controller is designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller synthesized around the identified model. Simulation results demonstrate that DMAC provides an effective and reliable approach for thrust regulation in SFRJs. In addition, a systematic hyperparameter sensitivity study is conducted by varying the tuning parameters over several orders of magnitude. The resulting responses show that the closed-loop performance and tracking error remain stable across wide parameter variations, indicating that DMAC exhibits strong robustness to hyper parameter tuning.

math.OC

Data-driven Pressure Recovery in Diffusers

This paper investigates the application of a data-driven technique based on retrospective cost optimization to optimize the frequency of mass injection into an S-shaped diffuser, with the objective of maximizing the pressure recovery. Experimental data indicated that there is an optimal injection frequency between 100 Hz and 300 Hz with a mass flow rate of 1 percent of the free stream. High-fidelity numerical simulations using compressible unsteady Reynolds-Averaged Navier-Stokes (URANS) are conducted to investigate the mean and temporal features resulting from mass injection into an S-shaped diffuser with differing injection speeds and pulse frequencies. The results are compared with experiments to confirm the accuracy of the numerical solution. Overall, 2-D simulations are relatively in good agreement with the experiment, with 3-D simulations currently under investigation to benchmark the effect of spanwise instabilities. Simulation results with the proposed data-driven technique show improvements upon a baseline case by increasing pressure recovery and reducing the region of flow recirculation within the diffuser.

physics.flu-dyn

Low-Order $\mathcal{H}_2 / \mathcal{H}_\infty$ Controller Design for Aeroelastic Vibration Suppression

This paper presents an $\mathcal{H}_2 / \mathcal{H}_\infty$ minimization-based output-feedback controller for active aeroelastic vibration suppression in a cantilevered beam. First, a nonlinear structural model incorporating moderate deflection and aerodynamic loading is derived and discretized using the finite element method (FEM). Then, a low-order linear model is identified from random gaussian input response data from the FEM model to synthesize an output-feedback controller using the $\mathcal{H}_2 / \mathcal{H}_\infty$ framework. A frequency-weighted dynamic filter is introduced to emphasize disturbance frequencies of interest, enabling the controller to target dominant vibration modes. Simulation results demonstrate the effectiveness of the proposed technique for vibration suppression and study its robustness to system parameter variations, including actuator placement.

eess.SY

Model-free Adaptive Output Feedback Vibration Suppression in a Cantilever Beam

This paper presents a model-free adaptive control approach to suppress vibrations in a cantilevered beam excited by an unknown disturbance. The cantilevered beam under harmonic excitation is modeled using a lumped parameter approach. Based on retrospective cost optimization, a sampled-data adaptive controller is developed to suppress vibrations caused by external disturbances. Both displacement and acceleration measurements are considered for feedback. Since acceleration measurements are more sensitive to spillover, which excites higher frequency modes, a filter is developed to extract key displacement information from the acceleration data and enhance suppression performance. The vibration suppression performance is compared using both displacement and acceleration measurements.

eess.SY

Computational Modeling and Learning-Based Adaptive Control of Solid-Fuel Ramjets

Solid-fuel ramjets offer a compact, energy-dense propulsion option for long-range, high-speed flight but pose significant challenges for thrust regulation due to strong nonlinearities, limited actuation authority, and complex multi-physics coupling between fuel regression, combustion, and compressible flow. This paper presents a computational and control framework that combines a computational fluid dynamics model of an SFRJ with a learning-based adaptive control approach. A CFD model incorporating heat addition was developed to characterize thrust response, establish the operational envelope, and identify the onset of inlet unstart. An adaptive proportional-integral controller, updated online using the retrospective cost adaptive control (RCAC) algorithm, was then applied to regulate thrust. Closed-loop simulations demonstrate that the RCAC-based controller achieves accurate thrust regulation under both static and dynamic operating conditions, while remaining robust to variations in commands, hyperparameters, and inlet states. The results highlight the suitability of RCAC for SFRJ control, where accurate reduced-order models are challenging to obtain, and underscore the potential of learning-based adaptive control to enable robust and reliable operation of SFRJs in future air-breathing propulsion applications.

math.OC

Computing Safe Control Inputs using Discrete-Time Matrix Control Barrier Functions via Convex Optimization

Control barrier functions (CBFs) have seen widespread success in providing forward invariance and safety guarantees for dynamical control systems. A crucial limitation of discrete-time formulations is that CBFs that are nonconcave in their argument require the solution of nonconvex optimization problems to compute safety-preserving control inputs, which inhibits real-time computation of control inputs guaranteeing forward invariance. This paper presents a novel method for computing safety-preserving control inputs for discrete-time systems with nonconvex safety sets, utilizing convex optimization and the recently developed class of matrix control barrier function techniques. The efficacy of our methods is demonstrated through numerical simulations on a bicopter system.

eess.SY

Stability Preserving Safe Control of a Bicopter

This paper presents a control law for stabilization and trajectory tracking of a multicopter subject to safety constraints. The proposed approach guarantees forward invariance of a prescribed safety set while ensuring smooth tracking performance. Unlike conventional control barrier function methods, the constrained control problem is transformed into an unconstrained one using state-dependent mappings together with carefully constructed Lyapunov functions. This approach enables explicit synthesis of the control law, instead of requiring a solution of constrained optimization at each step. The transformation also enables the controller to enforce safety without sacrificing stability or performance. Simulation results for a polytopic reference trajectory confined within a designated safe region demonstrate the effectiveness of the proposed method.

eess.SY

Predictive Control Barrier Functions for Discrete-Time Linear Systems with Unmodeled Delays

This paper introduces a predictive control barrier function (PCBF) framework for enforcing state constraints in discrete-time systems with unknown relative degree, which can be caused by input delays or unmodeled input dynamics. Existing discrete-time CBF formulations typically require the construction of auxiliary barrier functions when the relative degree is greater than one, which complicates implementation and may yield conservative safe sets. The proposed PCBF framework addresses this challenge by extending the prediction horizon to construct a CBF for an associated system with relative degree one. As a result, the superlevel set of the PCBF coincides with the safe set, simplifying constraint enforcement and eliminating the need for auxiliary functions. The effectiveness of the proposed method is demonstrated on a discrete-time double integrator with input delay and a bicopter system with position constraints.

eess.SY

Computing Invariant Zeros of a MIMO Linear System Using State-Space Realization

Poles of a multi-input multi-output (MIMO) linear system can be computed by solving an eigenvalue problem; however, the problem of computing its invariant zeros is equivalent to a generalized eigenvalue problem. This paper revisits the problem of computing the invariant zeros by solving an eigenvalue problem. We introduce a realization called the invariant zero form in which the system's invariant zeros are isolated in a partition of the transformed dynamics matrix. It is shown that the invariant zeros are then the eigenvalues of a partition of the transformed dynamics matrix. Although the paper's main result is proved only for square MIMO systems, the technique can be heuristically extended to nonsquare MIMO systems, as shown in the numerical examples.

math.OC

Swarm-optimized Adaptive Augmentation of Missile Autopilot

This paper considers the problem of optimizing a missile autopilot. In particular, the paper investigates the application of an online learning technique to learn and optimize the gains of a three-loop topology autopilot for a planar missile modeled with nonlinear dynamics and nonlinear aerodynamics forces and moments. The classical autopilot for a missile is based on a three-loop topology, where each loop consists of tunable proportional gains. An adaptive three-loop autopilot is constructed by augmenting the classical autopilot's fixed-gain controllers with a learning-based controller, which is recursively optimized using retrospective cost optimization. Numerical simulations show that online learning improves the tracking performance of the classical autopilot in both nominal and off-nominal interception scenarios.

eess.SY