In this paper, we introduce a model-free, real-time, dynamic optimization and control method for a class of rigid body dynamics. Our method is based on a recent extremum seeking control for vibrational stabilization (ESC-VS) approach that is applicable to a class of second-order mechanical systems. The new ESC-VS method is able to stabilize a rigid body dynamic system about the optimal state of an objective function that can be unknown expression-wise, but assessable through measurements; the ESC-VS is operable by using only one perturbation/vibrational signal. We demonstrate the effectiveness and the applicability of our ESC-VS approach via three rigid-body systems: (1) satellite attitude dynamics, (2) quadcopter attitude dynamics, and (3) acceleration-controlled unicycle dynamics. The results, including simulations with and without measurement delays/noise, illustrate the ability of our ESC-VS to operate successfully as a new methodology of optimization and control for rigid body dynamics. (c) 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This paper focuses on the further development of the Lie bracket approximation approach for optimization and control via extremum seeking systems. Classical results in this area provide algorithms with exponential convergence rates for quadratic-like cost functions, and polynomial decay rates for cost functions of higher degrees. This paper proposes a novel design that ensures the motion of the extremum seeking system along directions associated with higher-order Lie brackets, thereby achieving exponential convergence for cost functions that are "flat-bottomed", i.e., polynomial-like but of degree greater than two and unlike literature assumptions, we do not require Hessian information or strictly non zero Hessian at the minimum. Numerical simulations are presented to demonstrate the effectiveness of the proposed designs and their exponential convergence on fourth-, sixth-, and even eighth-degree cost functions. We include a comparison that shows our design outperforming a Newton-based method.

This paper provides a rigorous derivation for what is known in the literature as the Lie bracket approximation of control-affine systems in a more general and sequential framework for higher-orders. In fact, by using chronological calculus, we show that said Lie bracket approximations can be derived, and considered, as higher-order averaging terms. Hence, the theory provided in this paper unifies both averaging and approximation theories of control-affine systems. In particular, the Lie bracket approximation of order (n) turns out to be a higher-order averaging of order (n+1). The derivation and formulation provided in this paper can be directly reduced to the first and second-order Lie bracket approximations available in the literature. However, we do not need to make many of the unproven assumptions provided in the literature and show that they are in fact natural corollaries from our work. Moreover, we use our results to show that important and useful information about control-affine extremum seeking systems can be obtained and used for significant performance improvement, including a faster convergence rate influenced by higher-order derivatives. We provide multiple numerical simulations to demonstrate both the conceptual elements of this work as well as the significance of our results on extremum seeking with comparison against the literature.

In this letter, we reveal a universal, very simple extremum seeking natural feedback law and mechanism that governs, adapts, and generates in real-time, optimized lift variations for successful energy gain flight in presence of wind shear. The introduced law/mechanism, which is computationally minimal and needs only sensory information of the wind or local energy rate (i.e., model-free and data-driven) is able to characterize and replicate dynamic soaring optimized flight physics of windward climb in real-time for a variety of soaring birds species, namely wandering albatross, black-browed albatross and grey-headed albatross. We confirm the effectiveness of this new simple, real-time law by successful comparisons with sophisticated non-real-time optimal control solver and reported biological data. Our results establish the proposed mechanism as a new paradigm in soaring flight physics. That is, our results substantially advance the computational physics/biology aspects of the problem while providing a biologically plausible theory for avian soaring behavior.

In this paper, we provide a novel framework that enables a sensitivity-based observability test and state estimation algorithm for wind turbine power systems (WTPSs). The provided framework is the first of its kind in the literature, as it is able to deal with state-of-the-art WTPS models that are nonreduced, highly nonlinear differential-algebraic equation systems. Moreover, the framework includes nonsmoothness in both the dynamics and output functions to unify the operational conditions over different wind speed regions. We demonstrate the effectiveness of the proposed framework (thanks to the underlying tools from generalized derivatives theory) on different wind speed profiles, including real-world wind data. We also illustrate how the proposed framework, by the utilization of robust observability analysis during nonsmooth transitions, enables accurate state estimation for cases when the conventional Extended Kalman Filter approach fails. (c) 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This paper introduces a novel model-free, real-time unicycle-based source seeking design. This design autonomously steers the unicycle dynamic system towards the extremum point of an objective function or physical/scalar signal that is unknown expression-wise, but accessible via measurements. A key contribution of this paper is that the introduced design converges exponentially to the extremum point of objective functions (or scalar signals) that behave locally like a higher-degree power function (e.g., fourth-degree polynomial function) as opposed to locally quadratic objective functions, the usual case in literature. We provide theoretical results and design characterization, supported by a variety of simulation results that demonstrate the robustness of the proposed design, including cases with different initial conditions and measurement delays/noise. Also, for the first time in the literature, we provide experimental robotic results that demonstrate the effectiveness of the proposed design and its exponential convergence ability. These experimental results confirm that the proposed exponentially convergent extremum seeking design can be practically realized on a physical robotic platform under real-world sensing and actuation constraints. (c) 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

In this paper, we introduce a higher-order averaging theory and method for a wide range of nonsmooth systems that are generally characterized by the classical averaging canonical form. Utilizing tools from generalized derivatives theory, we provide a nonsmooth near-identity transformation analogous to the one in smooth averaging theory. Additionally, we exploit sharp calculus rules from lexicographic differentiation theory to provide a closed formula for nonsmooth first-order averaging, and for the first time in the literature, nonsmooth second-order averaging. In fact, our approach recovers the smooth averaging results, without needing to check, if the system under consideration is smooth. Equipped with a nonsmooth second-order averaging theory, we generalize literature results and introduce a class of control-affine extremum seeking systems that tolerate nonsmoothness in the vector fields and/or the objective function by analyzing its stability based on a closed formula analogous to first-order Lie bracket approximations available in the smooth literature. We provide numerical simulation results involving complicated nonsmooth functions to demonstrate the effectiveness of our approach.

Optimal control theory extending from the calculus of variations has not been used to study the wind turbine power system (WTPS) control problem, which aims at achieving two targets: (i) maximizing power generation in lower wind speed conditions; and (ii) maintaining the output power at the rated level in high wind speed conditions. A lack of an optimal control framework for the WTPS (i.e., no access to actual optimal control trajectories) reduces optimal control design potential and prevents competing control methods of WTPSs to have a reference control solution for comparison. In fact, the WTPS control literature often relies on reduced and linearized models of WTPSs, and avoids the nonsmoothness present in the system during transitions between different conditions of operation. In this paper, we introduce a novel optimal control framework for the WTPS control problem. We use in our formulation a recent accurate, nonlinear differential-algebraic equation (DAE) model of WTPSs, which we then generalize over all wind speed ranges using non-smooth functions. We also use developments in nonsmooth optimal control theory to take into account nonsmoothness present in the system. We implement this new WTPS optimal control approach to solve the problem numerically, including (i) different wind speed profiles for testing the system response; (ii) real-world wind data; and (iii) a comparison with smoothing and naive approaches. Results show the effectiveness of the proposed approach.

In this work, we extend the sensitivity-based rank condition (SERC) test for local observability to another class of systems, namely smooth and nonsmooth differential-algebraic equation (DAE) systems of index-1. The newly introduced test for DAEs, which we call the lexicographic SERC (L-SERC) observability test, utilizes the theory of lexicographic differentiation to compute sensitivity information. Moreover, the newly introduced L-SERC observability test can judge which states are observable and which are not. Additionally, we introduce a novel sensitivity based extended Kalman filter (S-EKF) algorithm for state estimation, applicable to both smooth and nonsmooth DAE systems. Finally, we apply the newly developed S-EKF to estimate the states of a wind turbine power system model.

This paper presents a novel extremum seeking control (ESC) approach for the vibrational stabilization of a class of mechanical systems (e.g., systems characterized by equations of motion resulting from Newton second law or Euler-Lagrange mechanics). Inspired by flapping insects mechanics, the proposed ESC approach is operable by only one perturbation signal and can admit generalized forces that are quadratic in velocities. We test our ESC, and compare it against approaches from literature, on some classical mechanical systems (e.g., mass-spring and an inverted pendulum systems). We also provide a novel, first-of-its-kind, application of the introduced ESC by achieving a 1D model-free source-seeking of a flapping system.

New sensitivity-based methods are developed for determining identifiability and observability of nonsmooth input-output systems. More specifically, lexicographic derivatives are used to construct nonsmooth sensitivity rank condition (SERC) tests, which we call lexicographic SERC (L-SERC) tests. The introduced L-SERC tests are practically implementable, accurate, and analogous to (and indeed recover) their smooth counterparts. To accomplish this, a novel first-order Taylor-like approximation theory is developed to directly treat nonsmooth (i.e., continuous but nondifferentiable) functions. An L-SERC algorithm is proposed that determines partial structural identifiability or observability, which are useful characterizations in the nonsmooth setting. Lastly, the theory is illustrated through an application in climate modeling.

In this paper, we take a step toward characterizing the physics of the hovering phenomenon in flapping insects and hummingbirds as a class of extremum seeking (ES) feedback systems. By characterizing hovering flight in insects and hummingbirds as a natural hovering ES system, we achieve (1) very simple, (2) stable, (3) model-free, and (4) real-time hovering. More importantly, our hovering ES characterization only needs the natural oscillations of the wing as the ES input. That is, unlike other control techniques in the literature, the natural hovering ES system only needs the natural flapping action built in the system and feedback of local sensations (measurements) related to the altitude where the insect seeks to stabilize itself. Said ES characterization can become an important initial step in starting a new line of research that may succeed in resolving the long-standing gap between model-based control theory and the biologically observed mechanisms that stabilize hovering flight. We provide simulation trials, including comparisons with some approaches from literature, to demonstrate the effectiveness and robustness of our results. We used literature data for hawkmoth, cranefly, bumblebee, dragonfly, hoverfly, and a hummingbird.

The theory of chronological calculus, using sophisticated tools from differential geometry, has enabled higher-order averaging methods that are computable based on Lie-algebraic formulations. However, there seems to be some ambiguity in the literature regarding the class of systems on which higher-order averaging via chronological calculus can be applied to (x = is an element of f(x, t) or x = is an element of f(x, t; is an element of)). In this paper, we clarify the aforementioned ambiguity and clarify the conditions by which higher-order averaging methods using chronological calculus is applicable to systems in the form x = is an element of f(x, t; is an element of). Additionally, we clarify the transfer of stability properties between an averaged system of finite order (e.g., first/second/third order-averaging) and the original nonlinear time-varying (periodic) system when the averaged system is asymptotically stable. We provide examples to illustrate the concepts discussed in this paper.

This paper focuses on the further development of the Lie bracket approximation approach for extremum seeking systems. Classical results in this area provide extremum seeking algorithms with exponential convergence rates for quadratic-like cost functions, and polynomial decay rates for cost functions of higher degrees. This paper proposes a novel control design approach that ensures the motion of the extremum seeking system along directions associated with higher-order Lie brackets, thereby ensuring exponential convergence for cost functions that are polynomial-like but with degree greater than two.

The problem of hovering in flapping insects/hummingbirds, and potential biomimicry by micro aerial vehicles (MAVs), have been studied for decades by scientists and engineers. Said communities often study hovering in flapping systems as either an open-loop or closed-loop system to analyze stability and/or propose control designs. Recently, a fundamentally novel result has been achieved in the literature of the hovering problem. That is, hovering in flapping insects/hummingbirds can be characterized/mimicked as a stable, model-free, real-time extremum seeking control (ESC) feedback system. In this paper we aim at two contributions: (i) provide a novel open-loop, optimal control characterization of hovering; and (ii) compare the performance of closed-loop, real-time ESC in hovering vs. the provided open-loop, non-real-time optimal control in hovering. Copyright (c) 2025 The Authors.

Many autonomous robots aimed at source-seeking are studied, and their controls designed, using unicycle modeling and formulation. This is true not only for model-based controllers, but also for model-free, real-time control methods such as extremum seeking control (ESC). In this paper, we propose a unicycle-based ESC design applicable to differential wheeled robots that: (1) is very simple design, based on one simple control-affine law, and without state integrators; (2) attenuates oscillations known to persist in ESC designs (i.e., fully stop at the source); and (3) operates in a model-free, real-time setting, tolerating environmental/sensor noise. We provide simulation and real-world robotic experimental results for fixed and moving light source seeking by a differential wheeled robot using our proposed design. Results indicate clear advantages of our proposed design when compared to the literature, including attenuation of undesired oscillations, improved convergence speed, and better handling of noise.

This article focuses on sensitivity and control theory for linear complementarity systems (LCSs), a type of dynamical system that experiences hybrid continuous/discrete behavior and is therefore nonsmooth. In particular, a sensitivity theory is given that characterizes generalized derivative information of solutions of LCSs with respect to parametric perturbations. With this theory in hand, a computationally-relevant open-loop optimal control theory is provided using a direct method (i.e., the control is parametrically discretized and generalized gradients of the objective function are described). The approach here is based on lexicographic directional differentiation theory, a relatively new tool in nonsmooth analysis, being applied to nonlinear complementarity systems (NCSs). The optimal control theory is illustrated with an example. As a byproduct of the sensitivity theory, well-posedness results for a new class of hybrid dynamical system, called the lexicographic linear complementarity system (LexLCS), are also established.

In this letter, we report the first experimental demonstration of the recently emerged new paradigm in hovering and flapping flight physics called (Natural Hovering Extremum Seeking (NH-ES)) [doi.org/10.1103/4dm4-kc4g], which theorized that stable hovering flight physics observed in nature by flapping insects and hummingbirds can be generated via a model-free, real-time, computationally-basic, sensory-based feedback mechanism that only needs the built-in natural oscillations of the flapping wing as both the control and the propulsive input. We run experiments of moth-like, light source-seeking, on a flapping-wing body in a total model-free setting that is agnostic to morphological parameters and body/aerodynamic models. We show that the flapping body using NH-ES gains altitude and stabilizes autonomously the servos responsible for flapping, including with pitching dynamics (believed in literature to be a main reason of instability in open-loop hovering). The flapping body effectively/stably hovers about the light source, needing only feedback of local measurements of light intensity. Our results were also achieved under delay/noise effects, supporting earlier observations that NH-ES is robust against potential processing delays and noisy-sensations.

This letter presents a novel extremum seeking control (ESC) approach for the vibrational stabilization of a class of mechanical systems (e.g., systems characterized by equations of motion resulting from Newton's second law or Euler-Lagrange mechanics). Inspired by flapping insects mechanics, the proposed ESC approach is operable by only one perturbation signal and can admit generalized forces that are quadratic in velocities. We test our ESC, and compare it against approaches from literature, on some classical mechanical systems (e.g., mass-spring and an inverted pendulum systems). We also provide a novel, first-of-its-kind, application of the introduced ESC by achieving a 1D model-free source-seeking of a flapping system.

For centuries, soaring birds -- such as albatrosses and eagles -- have been mysterious and intriguing for biologists, physicists, aeronautical/control engineers, and applied mathematicians. These fascinating biological organisms have the ability to fly for long-duration while spending little to no energy. This flight technique/maneuver is called dynamic soaring (DS). For biologists and physicists, the DS phenomenon is nothing but a wonder of the very elegant ability of the bird's interaction with nature and using its physical ether in an optimal way for better survival and energy efficiency. For the engineering community, it is a source of inspiration and an unequivocal promising chance for bio-mimicking. In literature, significant work has been done on modeling and constructing control systems that allow the DS maneuver to be mimicked. However, mathematical characterization of the DS phenomenon in literature has been limited to optimal control configurations that utilized developments in numerical optimization algorithms along with control methods to identify the optimal DS trajectory taken (or to be taken) by the bird/mimicking system. In this paper, we provide a novel two-layered mathematical approach to characterize, model, mimic, and control DS in a simple and real-time implementation. The first layer will be a differential geometric control formulation and analysis of the DS problem. The second layer will be a linkage between the DS philosophy and a class of dynamical control systems known as extremum seeking systems. We believe our framework captures more of the biological behavior of soaring birds and opens the door for geometric control theory and extremum seeking systems to be utilized in systems biology and natural phenomena. Simulation results are provided along with comparisons with powerful optimal control solvers to illustrate the advantages of the introduced method.