Adaptive Parameter Research Articles

Confidence interval estimation for reliability of a two parameter Weibull distribution under complete and censored samples

Estimating the reliability function of the two parameter Weibull distribution is a critical task in various fields, providing essential insights into product longevity and optimizing maintenance strategies. Due to its adaptable parameters namely scale parameter α and shape parameter β, the Weibull distribution models diverse failure behaviours, from early-life defects to wear-out periods. By evaluating reliability at key time phases namely early life (t < 0.1α), useful life (0.1α ≤ t ≤ α), and end-of-life (t > α), this estimation process supports quality control, operational forecasting, and end-of-life planning. Such reliability assessments help minimize operational disruptions, improve cost management, and support strategic planning across industries where dependability and lifecycle management are essential, such as in manufacturing, healthcare, and technology sectors. Although it holds practical significance, the estimation of confidence interval (CI) for the reliability function of two parameter Weibull distribution has been relatively underexplored in the literature. This paper presents a new approach for constructing CI for the reliability function of the Weibull distribution using the generalized variable (GV) technique, applicable to both complete samples and type II singly right-censored samples. The empirical evaluation of this method indicates that it provides coverage probabilities that are closely aligned with the nominal level, even when dealing with small uncensored samples (as small as 5) and censored samples where the proportion of censored observations can reach 70%. In comparison, traditional methods for the Weibull distribution tend to yield less reliable or widely varied coverage probabilities for complete samples. The findings are demonstrated through practical examples. Keywords: Reliability, Confidence interval, Generalized Variable approach, Censoring.

Dynamical theory for adaptive systems

Abstract The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former’s dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

Adaptive Integral Sliding Mode Control with Chattering Elimination Considering the Actuator Faults and External Disturbances for Trajectory Tracking of 4Y Octocopter Aircraft

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

Forecasting techniques based on the use of time series models for forecasting expenditures on social protection and social security

In the context of a military conflict, it is growing the number of people who are in difficult life circumstances and need state assistance. Therefore, the issue of forecasting spending on social protection and social security is becoming increasingly relevant. The aim of the paper is to consider modern methods of analyzing and forecasting time series and propose their application to determine the need for spending on social protection and social security. The article provides a brief overview of regression models, their advantages and features of use for analyzing and predicting nonlinear and linear processes described by time series of statistical indicators used in the population social protection system. The paper offers a combination of predictions that can be made based on locally linear and nonlinear models, such as threshold autoregression (TAR) and exponential autoregression (ExAR), which have known quality characteristics. This approach primarily concerns time series that contain data on structural changes in the analyzed processes. Using combined predictions, it is possible to estimate the contribution of each component for both different and selected time points using variable weights. To build forecasts, depending on the scenario of distribution of social protection and social security expenditures, it is proposed to use Bayes networks. Their advantages are flexibility, high quality of results, the possibility of structural and parametric optimization and adaptation to new data and conditions, support for quality assessment by appropriate sets of statistical criteria, which makes it possible to analyze thoroughly intermediate and final results. It helps to increase the adequacy of models and the high quality of the final result of their application.

Adapting generalized suitability curves from Brown trout to Minnow using 1D and 2D aquatic habitat models

The article focuses on adapting generalized depth preference and flow velocity characteristics from Brown trout (Salmo trutta morpha fario) to the Minnow (Phoxinus phoxinus). The results obtained were used to model habitat suitability with 1D and 2D models. Since 1995, research on assessing aquatic habitat quality has been ongoing on 77 mountain streams in Slovakia. This assessment employs a System of Environmental Flow Analysis (SEFA), which is based on the Instream Flow Incremental Methodology (IFIM). In most sections, Brown trout occurred in representative numbers. Brown trout habitat preferences were derived and generalized, and represented by suitability curves. Minnows were present in sufficient numbers for the derivation of suitability curves only on some streams, making direct generalization from the measured data unrepresentative. The measurement results showed that Minnow has similar depth and flow rate preferences to Brown trout. Therefore, it can be assumed that it is possible to adapt the generalised suitability curves from Brown trout to Minnow. This expansion enabled us to broaden the assessment of habitat quality using the SEFA model to mountain streams that are dominated by Minnows and where there is insufficient Brown trout presence. Verification of parameter adaptation from Brown trout to Minnow was conducted in 11 sections of mountain streams. We discuss the performance of habitat quality modeling, concerning the fundamental hydraulic characteristics of streams, using both 1D and 2D models. The results of the 2D modeling are presented for a sub-mountain stream.

УРОВЕНЬ АДАПТИВНОСТИ ИСХОДНОГО МАТЕРИАЛА ДЛЯ СЕЛЕКЦИИ ЯРОВОЙ МЯГКОЙ ПШЕНИЦЫ В УСЛОВИЯХ РЯЗАНСКОЙ ОБЛАСТИ

The studies were conducted with the aim of a comprehensive assessment of spring soft wheat varieties of various maturity groups in terms of the level of disclosure of adaptive potential with the identification of valuable ones for use in breeding. The work was carried out in Ryazan region in 2018-2023. The material for the study was mid-early and mid-season foreign varieties Odeta, Sable, Kanyuk, Kalispero, KVS Torridon, KVS Aquilon, Ethos, CH Rubli, Etyud, as well as domestic varieties Zlata, Burlak, Agata, Gerakl, Omskaya 36, Margarita. Calculation of the parameters of adaptability, plasticity and stability was carried out according to the methods of A.A. Rossielle, J. Hemblin, L.A. Zhivotkov et al., E.D. Nettevich et al., V.A. Zykin, V.V. Khangildin. The soil of the plot is dark gray forest heavy loamy, of medium fertility. The weather conditions during the period were contrasting, typical of the Central Non-Black Earth Region. The yield of the mid-early group of varieties was within 3.57 ... 6.73 t/ha, mid-season - 3.33 ... 7.06 t/ha. High adaptive capacity was characterized by the mid-early variety Odeta, mid-season KVS Akvilon, Kanyuk, Kalispero, KVS Torridon. On average, over the years of research, their productivity was 5.51...5.95 t/ha, exceeding the average variety productivity in their maturity groups by 6.7...17.5%. At the same time, they had high indicators of compensatory capacity ((Y2+Y1)/2) - 5.62...5.95 t/ha, selection value (Sc) - 3.80...4.22 and variety stability level (VSL) - 103.2...156.2%. According to the comprehensive rating for the totality of all adaptability features, the best mid-early varieties were Zlata and Odeta with a rank sum of 21, mid-season varieties - Kanyuk, KVS Akvilon, KVS Torridon, Kalispero with a rank sum of 14...40.

Adaptive Free-Form Deformation Parameterization Based on Spring Analogy Method for Aerodynamic Shape Optimization

An adaptive Free-Form Deformation parameterization method based on a spring analogy is presented for aerodynamic shape optimization problems. The proposed method effectively incorporates the gradients of the objective and constraint functions, achieving automatic control point adjustment based on variances in design variable components. To evaluate the performance of the adaptive FFD parameterization method, two 2D airfoil optimization design problems are examined. The optimization of the RAE2822 airfoil with 12, 18 and 24 design variables demonstrates superior results for the adaptive method compared to uniform parameterization. The adaptive method requires fewer iterations and achieves lower objective function values. Additionally, the optimization design from NACA0012 to RAE2822 airfoil with 18 design variables shows that the adaptive parameterization method achieves a lower drag coefficient while satisfying the optimization objective. This validates the method’s capability to finely adjust airfoil shapes and capture more optimal design points by exerting stronger control over local shapes. The proposed adaptive FFD parameterization method proves highly effective for optimizing aerodynamic shapes, offering stability and efficiency in the early stages of optimization, even with a limited number of design variables.

Parameter-adaptive variational autoencoder for linear/nonlinear blind source separation

AbstractBlind source separation (BSS) serves as an important technique in the field of structural health monitoring (SHM), particularly for solving modal decomposition tasks. This study proposes a novel approach to both linear and nonlinear BSS problems in the Variational Autoencoder (VAE) framework, where the encoding and decoding processes of VAE are interpreted as procedures for inferring sources from observations and remixing these sources, respectively. In this way, the distribution of latent variables inferred by VAE is equivalent to the distribution of sources. We make improvements to the vanilla VAE to augment its effectiveness for BSS. First, we substitute standard normal distributions with trainable Gaussian processes (GP) as priors for latent variables and implement an exponential function as the activation function for adaptive parameters in the GP kernel functions. While the form of the priors is set as GP, the parameters of their kernel functions are not fixed but automatically converge to suitable values during the model training process. Additionally, a hyperparameter is introduced to balance the terms in the loss function. The proposed method is referred to as parameter-adaptive VAE (PAVAE). Then, upon different assumptions of the variances of sources, the proposed PAVAE is branched into two types: homoscedastic PAVAE (Ho-PAVAE) and heteroscedastic PAVAE (He-PAVAE). Through numerical and laboratory experiments, we demonstrate the effectiveness of the proposed method in solving BSS problems and their potential to underpin future research in SHM.

Voltage and Fuel Utilization Control Strategy for Solid Oxide Fuel Cell Based on Active Disturbance Rejection Control

ABSTRACTSolid oxide fuel cell (SOFC) systems have become a research focus because of their clean and high‐efficiency properties. Control of output voltage and fuel utilization is critical to the energy management for multi‐energy systems incorporating SOFC energy supply. However, maintaining precise control of the system's voltage in the presence of perturbations can be challenging. Moreover, the system's voltage control process can lead to fuel utilization fluctuations, which may affect the economy and safety. The design of the controller must meet both of these requirements. The stringent control requirements lead to poor parameter adaptability of existing controllers. This paper designs a nonlinear function and adopts a nonlinear/linear active disturbance rejection controller (ADRC) based on state switching to solve the output voltage tracking control problem of SOFC and maintain the fuel utilization rate in the ideal range. The simulation experimental results show that the proposed method has the advantages of strong and superior parameter adaptability with less control effort, which provides theoretical guidance for the design of the output voltage controller of the actual SOFC system.

Evaluating the Accuracy of the COMSOL-Based Finite-Element Method for Simulating Plasmon-Modified Fluorescence.

Accurately modeling plasmon-modified fluorescence is important for understanding and guiding the design of experimental nanostructures that reliably enhance fluorescence. They are of particular interest due to their potential to allow localized "hot spots" of high fluorescence enhancement in a reproducible manner. Given the increasingly prevalent use of the COMSOL Multiphysics software package for simulating these phenomena, we investigate its accuracy using an analytically tractable model consisting of a gold nanosphere interacting with either a plane wave or a radiating point dipole. COMSOL simulation results were compared with a formally exact analytical theory. It was found that simulation parameters commonly used for plane-wave scattering do not necessarily produce accurate results for the nanoparticle-plasmon-coupled dipole emission case. Instead, user-input adaptive meshing parameters were found to be helpful in achieving quantitative agreements between COMSOL and analytical theory results for plasmon-modified fluorescence. Our studies suggest convergence to analytically calculated values when a minimum of two additional user-input mesh elements separate the point-dipole position and the nanoparticle surface. This practical insight is expected to aid in the application of COMSOL simulations to planning and interpreting fluorescence modification experiments.

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms.

Assessment of metabolic indicators of the cardiovascular system in children with nephrotic syndrome

Objective: to evaluate the metabolic parameters of the cardiovascular system in nephrotic syndrome in children Material and methods: 110 children hospitalized with nephrotic syndrome in the nephrology department of the Samarkand Regional Children’s Multidisciplinary Medical Center were examined. Results: Renocardial syndrome with nephrotic syndrome develops in 64.8% of cases, the majority of patients are children 3-6 years of age (44.1%), with a predominance of boys (58.8%). In a cardiometric study in patients with nephrotic syndrome, renocardial syndrome is manifested by significant changes in metabolic and adaptation parameters: oxygen and lactate content - by 1.5 times, creatine phosphate - by 20%, tension index - by 4.8 times and vascular stiffness index - by 5.2 times. Conclusion: Deviation of cardiometry indicators in different age groups indicates a decrease in the intensity of blood circulation in organs and systems, indicating a significant load on the cardiovascular system in nephrotic syndrome.

An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism.

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model's superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies.

Integrating reinforcement learning and supervisory control theory for optimal directed control of discrete-event systems

Directed control is crucial for implementing controllers on programmable logic controllers. A directed controller is one that actively selects at most one controllable event (control command) to execute at any instant. This study investigates the numerical optimization problem of directed control for discrete-event systems, such as traffic systems and robotic systems. By integrating supervisory control theory and reinforcement learning, an algorithmic procedure is designed to synthesize an optimal directed controller, which minimizes the total cost associated with event execution while ensuring the safety and liveness of the controlled system. Two improved Q-learning algorithms incorporating dynamic parameter adaptation strategies are developed to enhance the global search ability of basic Q-learning. To demonstrate applicability and effectiveness, we apply the proposed method to control a guideway system and a multi-train traffic system, respectively. The experimental results indicate the superiority of the proposed method over the comparison methods.

An optimal parameterized Newton-type structure-preserving doubling algorithm for impact angle guidance-based 3D pursuer/target interception engagement

The proposed strategy, finite-time state-dependent Riccati equation (FT-SDRE)-based impact angle guidance, is generally employed to solve the 3D pursuer/target interception model with fixed lateral accelerations. This article expands its application to a general scenario where the lateral acceleration of a target may change. To achieve this, we approximate the accelerations of the azimuth and elevation angles of the target in the inertial frame via second-order finite difference schemes and develop a high-performance FT-SDRE algorithm with structure-preserving doubling algorithms (SDAs). As a result, the update frequency of the controller can be increased, and better guidance of the pursuer can be obtained to address the high maneuverability of the target during the entire interception procedure. At every state of the FT-SDRE, a modified Newton–Lyapunov method is employed to solve the continuous algebraic Riccati equation (CARE), and a new simplified SDA with adaptive optimal parameter selection is proposed for solving the associated Lyapunov equation. Our numerical results demonstrate that the FT-SDRE algorithm accelerated by our proposed methods is approximately three times faster than the FT-SDRE algorithm, in which the MATLAB functions icare and lyap are used to solve the CARE and the Lyapunov equation, respectively, throughout the entire interception procedure. In other words, the control frequency can be increased threefold. In our benchmark cases where the target maneuvers with nonlinear lateral acceleration, the target can be intercepted earlier via the proposed FT-SDRE algorithm.

An adaptive snow ablation-inspired particle swarm optimization with its application in geometric optimization

In response to the shortcomings of particle swarm optimization (PSO), such as low execution efficiency and difficulty in overcoming local optima, this paper proposes a multi-strategy PSO method incorporating snow ablation operation (SAO), known as SAO-MPSO. Firstly, Cubic initialization is performed on particles to obtain a good initial environment. Subsequently, SAO and PSO are combined in parallel, and a balanced search mechanism led by multiple sub-populations is devised, significantly improving the search efficiency of overall population. Finally, the degree day method of SAO is introduced, and particles are endowed with memory of environmental changes to prevent premature convergence of PSO, while balancing the exploration and exploitation (ENE) capabilities in later phases. All adaptive parameters are used throughout this method in place of fixed parameters to improve the robustness and adaptability. For a comprehensive analysis of SAO-MPSO, its good ENE ability is verified on CEC 2020 and CEC 2022 and this method is compared with existing improved PSO versions on both test sets. The results show that SAO-MPSO has certain advantages in the comparison of similar improved algorithms. In order to further validate the strength of SAO-MPSO in dealing with nonlinear optimization problems (OPs) with strong constraints, firstly, based on the ball Wang-Ball (BWB) curve, a combined BWB (CBWB) curve is constructed, and a construction method for CBWB curves that satisfy G1 and G2 continuity is derived. Then, with the energy minimization and scale parameters of the CBWB curve as the optimization objective and variables respectively, a shape optimization model that satisfies G2 continuity is established. Finally, three numerical optimization examples based on this model are solved using SAO-MPSO and compared with 10 other methods. The results show that the energy obtained by SAO-MPSO is the smallest, which verifies the effectiveness of this method applied to shape OPs of CBWB curve.

Adaptive MPC path-tracking controller based on reinforcement learning and preview-based PID controller

Path-tracking control is a crucial process for autonomous vehicles, ensuring that the vehicle drives safely along the reference path, and the suitable controller parameters ensure the accuracy and stability of this process. To enhance the adaptability of traditional path-tracking controller parameters, this paper proposes an adaptive model predictive control (MPC) controller based on a preview-based PID controller and deep deterministic policy gradient (DDPG) algorithm to achieve adaptive tuning of the controller parameters. Starting with the design of the dynamics tracking error model of the vehicle and the MPC controller. Based on the actor-critic reinforcement learning architecture, the DDPG agent is designed to tune the prediction horizon and weight matrix of the MPC controller. A preview-based PID controller is proposed to improve the efficiency and stability of reinforcement learning and compensate for the error in vehicle modeling. The improved algorithm performance is verified through the simulation scenarios of high-speed lane changing and accelerated overtaking scenarios constructed by MATLAB/Simulink. The results show that the improved algorithm significantly improves the adaptive ability of the traditional MPC controller to time-varying conditions and achieves higher tracking accuracy and stability.

Lower limb exoskeleton for gait rehabilitation with adaptive nonsingular sliding mode control

Abstract This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

Quantitative assessment of kidney split function and mean transit time in healthy patients using dynamic 18F-FDG PET/MRI studies with denoising and deconvolution methods making use of Legendre polynomials

PurposeOur objective was to assess a deconvolution and denoising technique based on Legendre polynomials compared to matrix deconvolution on dynamic 18F-FDG renography of healthy patients.MethodThe study was carried out and compared to the data of 24 healthy patients from a published study who underwent examinations with 99mTc-MAG3 planar scintigraphy and 18F-FDG PET/MRI. Due to corruption issues in some data used in the published article, post-publication measurements were provided. We have been warned that post-publication data were treated differently. The smoothing method switched from Bezier to Savitzky–Golay and the deconvolution from matrix-based (with Tikhonov Regularization) to Richardson–Lucy. A comparison of the split function and mean transit times of the published and post-publication data against our method based on Legendre polynomials was performed.ResultsFor split function, we only observed a good agreement between the processing methods for the 99mTc-MAG3 and the post-published data. No correlation was found between the split functions obtained on the 99mTc-MAG3 and the 18F-FDG, contrary to the published study. However, all calculated split function values for 18F-FDG and 99mTc-MAG3 were within the established normal range. For the mean transit time, the correlation was moderate with published data and very good with the post-publication measurements for both 99mTc-MAG3 and 18F-FDG. Bias of the Bland–Altman analysis of the mean transit times for 99mTc-MAG3 versus 18F-FDG was 1.1 min (SD 1.7 min) for the published data, − 0.11 min (SD 1.9 min) for the post-publication results and .05 min (SD 1.9 min) for our method.ConclusionsThe processing methods used in the original publication and in the post-publication work were quite complex and required adaptation of the fitting parameters for each individual and each type of examination. Our method did not require any specific adjustment; the same unmodified and fully automated algorithm was successfully applied to all data.

Filter‐Based Average Dwell‐Time Tuning Approach for Adaptive Prescribed‐Time Tracking of Uncertain Switched Nonlinear Systems

ABSTRACTThis paper addresses neural‐network‐based adaptive prescribed‐time (PT) tracking for uncertain switched systems with unmatched nonlinearities. A continuously switched adaptive tuning mechanism for neural network learning is developed by applying the average dwell time (ADT). First, a neural‐network‐based PT tracking control design strategy using the ADT‐based adaptive tuning mechanism is established for switched nonlinear systems in strict‐feedback form. A novel adaptive dynamic surface controller is designed recursively using a practical finite‐time scaling function and continuously switched tuning parameters. The switched adaptive tuning laws for neural networks are structured to reduce the conservatism associated with common adaptive laws. Then, a filter‐based tuning approach is employed to ensure the continuity of switched adaptive parameters with ADT in the designed controller. The practical PT stability of the closed‐loop system is demonstrated based on the boundedness of the adaptive parameters. Building upon this foundation, the proposed PT design approach is extended to control switched pure‐feedback nonlinear systems, even in cases where control directions are unspecified. The unknown sign problem encountered with switched virtual and actual control coefficient functions is resolved in the PT control framework. It is shown that the PT performance bound of the tracking error can be reduced by selecting the design parameter of the scaling function. Finally, simulation results illustrate the merits of the proposed theoretical approach.