arXiv subjects

Jhon Manuel Portella Delgado

Publications and source records attributed to Jhon Manuel Portella Delgado.

16 recordsLinked to original sources

arXiv2026

Admissibility-Preserving Constraint Lifting for Conditional Deadbeat Control of Discrete-Time Nonlinear Systems

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 pro- posed 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 Roup oscillator demonstrate the effectiveness of the proposed method in enforcing state constraints while tracking a reference command.

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arXiv2026

Constraint-Lifted Sector-Constrained Guidance for Rear-Approach Interception

This paper develops a sector-constrained guidance (SCG) law for interception of a maneuvering target while maintaining a prescribed approach geometry. The line of sight is required to remain within a specified angular sector behind the target throughout the engagement, resulting in a state-constrained guidance problem. To enforce this constraint, the directional angle between the target heading and the line of sight is mapped from the prescribed sector to an unconstrained coordinate using a sigmoid diffeomorphism. A guidance law is then designed in the lifted coordinate to generate a desired transverse velocity and corresponding pursuer lead angle. The desired lead angle is tracked through an inner-loop turn-rate controller, which is implemented through the normal-acceleration command of a nonlinear point-mass model. This construction embeds the sector constraint directly into the guidance law and does not require online constrained optimization. Numerical simulations against a maneuvering target show that SCG achieves interception while maintaining the prescribed rear-sector constraint, including when the pursuer normal-acceleration command is subject to actuator saturation. Comparisons with proportional guidance under the same engagement conditions demonstrate that proportional guidance may achieve interception but does not maintain the prescribed directional constraint.

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arXiv2026

A Numerical Investigation of Extremum-Seeking-Based Command Generation for Adaptively Controlled Systems

We develop an adaptive feedback control technique that combines an extremum-seeking-based command generator (ECG) with indirect adaptive control. In particular, ECG is used to generate commands that asymptotically optimize a cost function that is measured but whose functional form is unknown. For feedback control with command following and stabilization, the present paper combines ECG with predictive cost adaptive control (PCAC), which is an indirect adaptive control extension of model predictive control (MPC). PCAC extends generalized predictive control (GPC) by using quadratic programming to enforce output constraints and recursive least squares (RLS) with variable-rate forgetting (VRF) for system identification. The resulting ECG/PCAC framework combines command generation with closed-loop system identification and online optimization. The contribution of this paper is a numerical investigation of ECG/PCAC for adaptive stabilization, command following, and disturbance rejection

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arXiv2026

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.

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arXiv2026

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.

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arXiv2026

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.

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arXiv2026

Adaptive Fidelity-Based Density Tracking for Open Quantum Systems

This paper presents an online learning-based adaptive control framework for density-matrix tracking in a two-level Lindblad-Gorini-Kossakowski-Sudarshan (LGKS) quantum system, in which the feedback control law does not require prior knowledge of the system Hamiltonian or dissipative operators. The adaptive controller is based on a continuous-time formulation of retrospective cost adaptive control (RCAC). To preserve the geometric structure of the quantum-state evolution, an adaptive PID controller driven by Uhlmann's fidelity is employed. The proposed approach is validated in numerical simulations for both low-entropy and high-entropy density-tracking tasks, and robustness to measurement noise in the feedback path is investigated.

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arXiv2026

Sensor-Noise Mitigation in Extremum Seeking Control Using Adaptive Numerical Differentiation

Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is a crucial issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates the use of the recently developed adaptive input and state estimation (AISE) technique for numerical differentiation. In particular, this paper develops extremum-seeking control with adaptive input and state estimation (ESC/AISE), where AISE replaces the high-pass filter of ESC to improve performance under sensor noise. The effectiveness of ESC/AISE is illustrated via numerical examples.

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arXiv2025

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.

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arXiv2025

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.

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arXiv2025

Adaptive Control of Dual-Rotor Rotational System with Unknown Geometry and Unknown Inertia

This paper develops an input-output feedback linearization-based adaptive controller to stabilize and regulate a dual-rotor rotational system (DRRS), whose inertial properties as well as the geometric configuration of rotors are unknown. First, the equations of motion governing the dynamics of DRRS are derived using the Newton-Euler approach. Next, an input-output feedback linearization technique is used to linearize the dynamics from the rotor speeds to the angular position of the system. A finite-time convergent estimator, based on the portion of the DRRS dynamics, is used to update the required parameters in the controller. Finally, the proposed controller is validated in both step and harmonic command-following problems, and the robustness of the controller to the system's parameters is demonstrated.

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arXiv2024

Singularity-free Backstepping-based Adaptive Control of a Bicopter with Unknown Mass and Inertia

The paper develops a singularity-free backstepping-based adaptive control for stabilizing and tracking the trajectory of a bicopter system. In the bicopter system, the inertial parameters parameterize the input map. Since the classical adaptive backstepping technique requires the inversion of the input map, which contains the estimate of parameter estimates, the stability of the closed-loop system cannot be guaranteed due to the inversion of parameter estimates. This paper proposes a novel technique to circumvent the inversion of parameter estimates in the control law. The resulting controller requires only the sign of the unknown parameters. The proposed controller is validated in simulation for a smooth and nonsmooth trajectory-tracking problem.

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arXiv2024

Adaptive Nonlinear Control of a Bicopter with Unknown Dynamics

This paper presents an adaptive, model-based, nonlinear controller for the bicopter trajectory-tracking problem. The nonlinear controller is constructed by dynamically extending the bicopter model, stabilizing the extended dynamics using input-output linearization, augmenting the controller with a finite-time convergent parameter estimator, and designing a linear tracking controller. Unlike control systems based on the time separation principle to separate the translational and rotational dynamics, the proposed technique is applied to design a controller for the full nonlinear dynamics of the system to obtain the desired transient performance. The proposed controller is validated in simulation for a smooth and nonsmooth trajectory-tracking problem.

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arXiv2024

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

It is well known that zeros and poles of a single-input, single-output system in the transfer function form are the roots of the transfer function's numerator and the denominator polynomial, respectively. However, in the state-space form, where the poles are a subset of the eigenvalue of the dynamics matrix and thus can be computed by solving an eigenvalue problem, the computation of zeros is a non-trivial problem. This paper presents a realization of a linear system that allows the computation of invariant zeros by solving a simple eigenvalue problem. The result is valid for square multi-input, multi-output (MIMO) systems, is unaffected by lack of observability or controllability, and is easily extended to wide MIMO systems. Finally, the paper illuminates the connection between the zero-subspace form and the normal form to conclude that zeros are the poles of the system's zero dynamics

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arXiv2024

Adaptive Backstepping Control of a Bicopter in Pure Feedback Form with Dynamic Extension

This paper presents a model-based, adaptive, nonlinear controller for the bicopter stabilization and trajectory-tracking problem. The nonlinear controller is designed using the backstepping technique. Due to the non-invertibility of the input map, the bicopter system is first dynamically extended. However, the resulting dynamically extended system is in the pure feedback form with the uncertainty appearing in the input map. The adaptive backstepping technique is then extended and applied to design the controller. The proposed controller is validated in simulation for a smooth and nonsmooth trajectory-tracking problem.

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arXiv2023

Circumventing Unstable Zero Dynamics in Input-Output Linearization of Longitudinal Flight Dynamics

In this paper, we consider the problem of input-output linearization of the longitudinal flight dynamics. In longitudinal flight dynamics, inputs are typically thrust and elevator deflection whereas the outputs are the velocity and the flight path angle. An input-output linearization-based controller can be designed to render the multi-input, multi-output system linear; however, the resulting zero dynamics turns out to be unstable. In this work, we remove the zero dynamics from the closed-loop dynamics by considering an additional output. Although the additional output makes the system tall, which, in general, means that the input-to-output dynamics can not be linearized, we show that in the case of longitudinal flight dynamics, linearization is possible due to special geometric properties of the nonlinear terms.

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