This work proposes a novel combined model reference adaptive controller (MRAC) for unknown multi input output (MIMO) LTI systems with guaranteed parameter convergence. An online plant-parameter identification method is developed in conjunction direct control-parameter update law to ensure exponential convergence (after tunable finite time) of tracking error as well plant and estimation errors zero. Unlike the restrictive persistence excitation (PE) condition required classical MRAC...

This work proposes a new adaptive-robust control (ARC) architecture for class of uncertain Euler-Lagrange (EL) systems where the upper bound uncertainty satisfies linear in parameters (LIP) structure. Conventional ARC strategies either require structural knowledge system or presume that overall uncertainties its time derivative are norm bounded by constant. Due to unmodelled dynamics and modelling imperfection, true is not always available. Further, under consideration, prior assumption...

Underestimation and overestimation problems are commonly observed in conventional adaptive sliding mode control (ASMC). These refer to the fact that controller gain unnecessarily increases when states approaching surface (overestimation) or improperly decreases getting far from it (underestimation). In this paper, we propose a novel ASMC strategy overcomes such issues. contrast state of art, proposed is effective even an priori constant bound on uncertainty cannot be imposed. Comparative...

Abstract This work proposes a novel composite adaptive controller for uncertain Euler‐Lagrange (EL) systems. The law is strategically designed to be proportional the parameter estimation error in addition tracking error, leading convergence. Unlike conventional control laws which require regressor function persistently exciting (PE) convergence, proposed method guarantees convergence from milder initially (IE) condition on regressor. IE significantly less restrictive than PE, since it does...

This paper proposes an approximate/adaptive optimal control (AOC) design for completely unknown continuous-time linear time invariant systems, without requiring the restrictive persistence of excitation (PE) condition parameter convergence. The proposed AOC algorithm utilizes two layers filtering-the first layer filters strategically eliminate need state derivative information, while second provide suitable algebraic relations iteratively obtaining policy under a milder online-verifiable...

In this paper, we design and validate a kinematic controller for quadrotor tracking planar moving target using image-based visual servoing (IBVS). Most of the current literature on IBVS targets often consider restrictive assumptions dynamics that limits its generalizability any arbitrary motion. We propose model-free velocity estimator augmented based appropriately derived feature in virtual image plane. show how inner-loop mismatch affects performance through comprehensive theoretical...

This work proposes a novel PI-like composite adaptive control architecture for the uncertain Euler-Lagrange (EL) systems. The law is strategically designed to be proportional parameter estimation error in addition tracking error, leading convergence. Unlike conventional laws which require regressor function persistently exciting (PE) convergence, proposed method guarantees convergence from milder initially (IE) condition on regressor. IE significantly less restrictive than PE, since it does...

This brief presents a novel direct adaptive optimal controller design for uncertain continuous-time linear time-invariant systems. The gain parameter, obtained from the Riccati equation, is continuously estimated without using knowledge of system dynamics, rather information rich past, and current data along trajectory used parameter estimation. approach guarantees convergence to close neighborhood by relaxing restrictive persistence excitation condition, typically required achieving in...

This article addresses the problem of adaptive observer design for multi-input multioutput linear time-invariant plants, under effects bounded disturbances in system dynamics and output, without requiring persistence excitation (PE) condition parameter convergence. A switched robust is proposed, based on initial excitation, a milder as compared to PE terms requirement online verifiability. The unknown state strategically included an additional estimation framework, which, far authors are...

Abstract This work proposes a novel switched model reference adaptive control (MRAC) architecture, ensuring parameter convergence without requiring the assumption of persistence excitation (PE). Previous results which ensure with PE suffer from disadvantage that condition cannot be verified online as it relies on future behaviour signal. Further, requirement is often imposed by adding perturbation in reference/input signal, may deteriorate tracking performance, and thus renders objectives...

In this paper, a new Adaptive Sliding Mode Control (ASMC) framework is proposed for the tracking control of class uncertain nonlinear systems where system states are present explicitly in upper bound overall (or lumped) uncertainty. Conventional ASMC strategies presume that either uncertainty or its time derivative norm bounded by constant. However, such assumption restricts evaluation priori has been considered paper. The law does not constant on uncertainties and, rather, exploits unique...

This article proposes a novel closed-loop reference model (CRM) architecture for distributed adaptive control (DMRAC) algorithm, called CRM-DMRAC. The idea of CRM has been recently conceptualized in the literature single agent system where (leader) receives feedback from plant (follower) while enabling use high gain tuners without adding more transients parameter estimation. In this article, formulation is proposed leader subset follower agents (which are neighbors leader). external input to...

Summary This paper is a generalization of the recently developed techniques initial excitation (IE)–based adaptive control with an introduction to definition semi‐initial (semi‐IE), still more relaxed notion than IE. Classical controllers typically ensure Lyapunov stability extended error dynamics (tracking + parameter estimation error) and asymptotic tracking, while requiring stringent condition persistence (PE) for convergence. Of late, authors have proposed new architecture, which...

In this paper, a data-driven on-policy optimal control design is proposed for continuous-time linear time invariant (LTI) systems with completely unknown dynamics. An online system identifier and gain parameter estimator, which use past current data together standard gradient descent update laws, facilitate the of an adaptive controller that guarantees convergence without need persistence excitation (PE). Unlike classical approach enforcing restrictive PE condition on regressor, verifiable...

This work studies the robustness property of recently proposed initial excitation (IE) based adaptive controllers in presence unmodeled bounded disturbance dynamics. The IE-based have been shown to guarantee parameter convergence without requiring restrictive persistence (PE) condition, typically required classical for convergence. Unlike approaches, controller ensures exponential tracking and estimation errors zero once milder online verifiable IE condition is satisfied. Classical require...

This paper proposes a decentralized control barrier function (CBF) as solution for distributed global connectivity maintenance multi-agent system (MAS). Using combination of Fiedler value estimation (the second smallest eigenvalue the Laplacian) and functions, proposed method can ensure that agents will remain globally connected in fashion. The major advantage using CBF is it allows us to consider multiple objectives constraints at same time. Moreover, enables agent be increased desired...

This paper proposes a memory-efficient approximate/adaptive optimal control (AOC) design of completely unknown continuous-time (CT) linear time invariant (LTI) systems, without requiring the restrictive persistence excitation (PE) condition for parameter convergence. The AOC algorithm utilizes two layers filtering - first layer filters strategically eliminate need state derivative information, while second provide suitable algebraic relations iteratively obtaining policy under milder...

This letter confluences ideas from distributed model reference adaptive control (MRAC) architecture for multi-agent systems and closed-loop (CRM) based MRAC algorithm. The concept of CRM is recently proposed in literature single-agent problems, where the (leader) gets feedback plant (agent/follower) to facilitate improved transient performance. coins MRAC, it assumed that leader/reference connected only a subset followers incorporates them setting. Distributed parameter estimator controller...

Most of the contributions in adaptive control literature assume that system dynamics is linearly parametrizable, and a certainty equivalence principle exploited to guarantee global stability asymptotic convergence tracking error zero. Although linear-in-the-parameters (LIP) assumption reasonable for large class dynamics, there exists considerable number real world systems, involving complex where nonlinear parametrizations are inevitable. Previous research has shown classical gradient-based...

A singularity-free nonlinear hierarchical control framework is proposed in this paper for of a quad-rotorcraft unmanned aerial vehicle (UAV). saturation scheme with hyperbolic tangent function designed the position loop controller to ensure non-singular command attitude extraction and effect coupling between subsystem subsequently analyzed. The problem sign-ambiguity commonly appears reference overcome using arc by considering signs both arguments. To obviate singularity during tracking,...

Accurate trajectory tracking is essential in many applications involving autonomous vehicles. However, due to the coupled nonlinear dynamics of vehicle and external disturbances, this may not be case. This paper presents a Barrier Lyapunov Function (BLF) based controller design technique for vehicles with task-specific constrained state requirements. A BLF candidate introduced on constraints using direct method designed which guarantees that errors remain within defined bounds. The proposed...

Summary The article addresses the problem of a robust adaptive observer design for multi‐input multi‐output linear time‐invariant plants subjected to bounded disturbances in system dynamics and output relation. An is proposed based on finite excitation leading switched estimation strategy thus relaxing persistence (PE) condition. novel (f‐PE) condition introduced this work significantly milder than PE terms requirement online verifiability. estimator uses via input signal ensures uniformly...

This paper proposes a novel indirect adaptive optimal controller (AOC) for completely unknown continuous-time (CT) linear time invariant (LTI) systems using the policy iteration (PI) technique. The algorithm builds on Kleinman's method of iteratively solving algebraic Riccati equation (ARE). However, actual system and control matrices information, required by algorithm, is replaced their CT online estimates uniform sampling. A gradient-based identifier developed low pass filter, which...