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This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications. [ABSTRACT FROM AUTHOR]

RELEVANCE of the study lies in the development of an algorithm for controlling a DC voltage converter capable of providing a high-quality transient process with a wide change in the input parameters of the control object, affecting its nonlinear properties. purpose. THE PURPOSE. To consider methods for the development of continuous and discrete control systems for a step-up DC voltage converter and to obtain an analytical dependence of accounting for the nonlinear properties of the converter that affect the quality of regulation of the output voltage of the voltage stabilizer METHODS. The method of the average geometric root was chosen as a method for calculating the coefficients of the DC voltage converter control system, and the current circuit is adjusted using the technical optimum method, and the voltage circuit using the symmetric optimum method. The obtained analytical solutions of mathematical models and simulation models were compared in the SimInTech software environment. results. RESULTS. The simulation results show that the analytical solution of the mathematical model of the voltage stabilization system has a shorter transition time than the simulation results. This is due to the fact that in the mathematical model according to which the continuous control algorithm was synthesized, the presence of a power input filter, as well as the degree of their precharge, is not taken into account. Because of this, the transition time on simulation models is slower, applied 4 times than in the mathematical model. conclusion. CONCLUSION. When comparing the algorithms with each other, it can be seen that the algorithm obtained by converting a continuous algorithm by the Tusten method has the shortest transition time than all other algorithms. The continuous algorithm and the algorithm obtained by the inverse Euler transformation method have a speed close to each other, and the algorithm obtained by the direct Euler transformation turns out to be the slowest.

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A current challenge with adaptive controllers is to define efficient tuning methods of the controller parameters. Unlike linear systems, nonlinear systems may need parameters that are continuously tuned at different operating points to provide stability and desired behaviours. This study aims to develop a solution for tuning proportional–integral–derivative (PID) controller parameters as opposed to changing the operating points of a nonlinear system. Most tuning methods calculate parameters according to the system's step or frequency response. However, adaptive controllers have self-tuneable parameters or control rules. The proposed algorithm in this paper contains a controller, an estimator, and a reference model, and uses the system model. Unlike the model reference adaptive control method, the proposed controller has tuneable controller parameters estimated by the extended Kalman–Bucy filter. The filter estimates the controller parameters to make the system perform like the auxiliary ideal reference model to ensure minimum-time consumption. Hence, this study aims to develop an algorithm that will automatically calculate controller parameters for each operating point of the controlled chaotic or nonlinear system to minimize settling time at each operating point. The proposed algorithm is implemented in a unified chaotic system in which the estimator and controller of the system run together. Simulation results confirm the performance of the proposed algorithm. In addition, the simulation results provide strong evidence that the proposed algorithm can be an effective tool for controlling nonlinear or chaotic systems. [ABSTRACT FROM AUTHOR]

This study proposes a universal adaptive control algorithm for an unknown multi-input multi-output (MIMO) system using recursive least squares (RLS) and parameter self-tuning. The issue of adjusting the control and system parameters in response to changes in the platform was discussed. The development of a control algorithm that can consistently achieve reliable and robust control performance in various systems is important. This study aimed to develop a control algorithm that can track the reference value for any unknown MIMO system. For the controller design, an nth-order differential error dynamic model was designed, and an RLS with a scale factor was used to estimate the coefficients of the error dynamics. In the current scenario, the numbers of control inputs and error states in the error dynamics were assumed to be equal. It was designed such that the control input is derived based on the Lyapunov stability concept using the estimated coefficients. The scale factor in the RLS and injection term in the control input based on the sliding-mode approach were computed using a self-tuning methodology. The performance of the proposed universal adaptive control algorithm was evaluated using an actual DC motor and CarMaker (version 8.1.1) software tests under various scenarios. [ABSTRACT FROM AUTHOR]

When the operating conditions are changed suddenly, the model mismatch for the servo system using model-based methods occurs during the identification convergence, which will lead to the obvious speed spikes and damped oscillations, deteriorating the control performance of the servo system. To solve the problem, a parallel self-tuning scheme based on a generalized predictive control law is proposed in this paper. In the proposed scheme, the controlled model parameters in which an integral-proportional controller (IPC) is considered as the controller of speed loop are first online estimated by a recursive least squares method with a forgetting factor. Then, a model-mismatch compensator (MMC) is designed to obtain the corresponding compensation current, and in addition, the predicted speed is considered as the feedback speed to promote the establishment of an inner loop, which allows for a faster and more thorough model mismatch compensation based on both the excitation torque current and the adaptivity of the MMC. At the same time, through constructing two different quadratic performance indicators, the optimal control laws can be obtained based on a simplified decoupled derivation, thus supplying IPC and MMC with suitable control parameters simultaneously. Simulation and experimental results show that, compared with traditional methods, the proposed scheme can ensure faster convergence speed and better control performance.

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications.

This paper introduces a neural network adjustment method for a single gain of an integral proportional (IP) speed regulator, to improve the speed control of an induction motor. Thanks to its simplicity and strength, the integral proportional (IP) controller is widely used in the industry for speed control. Yet, in some cases, when the load or mechanical parameters change according to its working conditions, the integral proportional (IP) efficiency decreases, and the setup quality degrades. In this case, a neural IP-self-tuning seems to overcome these difficulties and ensure a good control performance. The results obtained through the implementation of the proposed control on a dSPACE system and an induction motor clearly demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

All-solid-state tunable lasers have been widely used in many fields including multi-photon microscopy, time-resolved photoluminescence, atomic physics, and so on owing to their broadband output spectrum range, good beam quality, and low noise. To cover the broad fluorescent line of the laser crystal as much as possible, a birefringent filter (BRF) is always the most popular candidate for acting as a tuning element. In this review, the tuning characteristics of BRF and the design rule as well as its progress in practical application are summarized. Especially, it is worth noting that laser crystal itself begins to act as the BRF for wavelength tuning except for its gain characteristic, which has paved a new way for developing a kind of novel tunable laser. We believe that this review will not only provide a valuable reference for the design of BRF but also lay the foundation for developing a new application of BRF. [ABSTRACT FROM AUTHOR]

Hardware performance counters (HPCs) are facilities offered by most off‐the‐shelf CPU architectures. They are a vital support to post‐mortem performance profiling and are exploited by standard tools such as Linux or Intel V‐Tune. Nevertheless, an increasing number of application domains (e.g., simulation, task‐based high‐performance computing, or cybersecurity) are exploiting them to perform different activities, such as self‐tuning, autonomic optimization, and/or system inspection. This repurposing of HPCs can be difficult, for example, because of the overhead for extracting relevant information. This overhead might render any online or self‐tuning activity ineffective. This article discusses various practical strategies to exploit HPCs beyond post‐mortem profiling, suitable for different application contexts. The presented strategies are accompanied by a general primer on HPCs usage on Linux. We also provide reference x86 (both Intel and AMD) implementations targeting the Linux kernel, upon which we present an experimental assessment of the viability of our proposals. [ABSTRACT FROM AUTHOR]

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems. [ABSTRACT FROM AUTHOR]

RELEVANCE. Servo drives operate with a law of change of the setting action unknown in advance and provide reproduction of this law by the output coordinate. Servo drives find application in robotic and mechatronic systems, machine tools, systems of automatic control and remote transmission of information, radar stations, guidance units, etc. The operation of servodrives often proceeds in conditions of instability parameters and characteristics elements of the electric drive. Corrective devices synthesized by classical methods of automatic control theory cannot cope with providing the specified accuracy of reproduction of the input signal and the required quality of transients. THE PURPOSE. In this regard, an important and urgent task is the synthesis of an active correction system with a non-stationary controller that provides the required quality and accuracy of the control process due to the coefficient self-tuning algorithm. METHODS. When solving this problem, methods for identifying parameters based on the gradient algorithm and numerical integration of the object of study dynamics equations, implemented by means of the MatLab software environment, were used. RESULTS. The paper solves the problem of synthesizing the self-tuning algorithm for the coefficients of the servo drive corrective device based on the identification approach. The parameters are identified by a searchless gradient algorithm while minimizing the discrepancy between the object of study and its inverse model, as well as restoring the coefficients of differential equations using integration and the corresponding computational procedures. An servo drive with negative position feedback is tuned to the modular optimum with a proportional controller whose coefficients are completely determined by the parameters to be identified. The self-tuning algorithm consists in calculating the correction factor of the non-stationary P-controller and forming a multiplicative channel of the active correction closed loop. CONCLUSION. The simulation of the electric drive in the MatLab software environment showed high accuracy and quickness of the process identifying parameters in a wide range of their change. When forming an active correction contour, a necessary requirement is to distinguish between the identification cycle and the self-tuning cycle. This makes it possible to avoid singular perturbations and reduce resonant facts during the operation of a parametrically invariant electric drive. The developed method of active correction with a priori known and unchanged structure of the object model of study makes it possible to maintain the required accuracy and quality of the operation of the electric drive under conditions of parametric disturbances with permissible deviations of accuracy and quality indicators. Implementation of the method does not require additional equipment, organization of special test signals, significant computational costs. The method of synthesizing a parametrically invariant electric drive can be used to develop robust control systems for non-stationary objects, including when the hypothesis of quasi-stationarity is not fulfilled.

This study proposes a universal adaptive control algorithm for an unknown multi-input multi-output (MIMO) system using recursive least squares (RLS) and parameter self-tuning. The issue of adjusting the control and system parameters in response to changes in the platform was discussed. The development of a control algorithm that can consistently achieve reliable and robust control performance in various systems is important. This study aimed to develop a control algorithm that can track the reference value for any unknown MIMO system. For the controller design, an nth-order differential error dynamic model was designed, and an RLS with a scale factor was used to estimate the coefficients of the error dynamics. In the current scenario, the numbers of control inputs and error states in the error dynamics were assumed to be equal. It was designed such that the control input is derived based on the Lyapunov stability concept using the estimated coefficients. The scale factor in the RLS and injection term in the control input based on the sliding-mode approach were computed using a self-tuning methodology. The performance of the proposed universal adaptive control algorithm was evaluated using an actual DC motor and CarMaker (version 8.1.1) software tests under various scenarios.

Flexible and stretchable radiofrequency coils for magnetic resonance imaging represent an emerging and rapidly growing field. The main advantage of such coil designs is their conformal nature, enabling a closer anatomical fit, patient comfort, and freedom of movement. Previously, we demonstrated a proof-of-concept single element stretchable coil design with a self-tuning smart geometry. In this work, we evaluate the feasibility of scaling this coil concept to a multi-element coil array and the associated engineering and manufacturing challenges. To this goal, we study a dual-channel coil array using full-wave simulations, bench testing, in vitro, and in vivo imaging in a 3 T scanner. We use three fabrication techniques to manufacture dual-channel receive coil arrays: (1) single-layer casting, (2) double-layer casting, and (3) direct-ink-writing. All fabricated arrays perform equally well on the bench and produce similar sensitivity maps. The direct-ink-writing method is found to be the most advantageous fabrication technique for fabrication speed, accuracy, repeatability, and total coil array thickness (0.6 mm). Bench tests show excellent frequency stability of 128 ± 0.6 MHz (0% to 30% stretch). Compared to a commercial knee coil array, the stretchable coil array is more conformal to anatomy and provides 50% improved signal-to-noise ratio in the region of interest. [ABSTRACT FROM AUTHOR]

Intermittent control combines open-loop trajectories with feedback at discrete time instances determined by events. Among other applications, it has recently been used to model quiet standing in humans where the system was assumed to be time-invariant. This article expands this work to the time-variant case by introducing an adaptive intermittent controller that exploits the well-known self-tuning architecture of adaptive control with a Kalman filter to perform online state and parameter estimation. Simulation and experimental results using a rotational inverted pendulum show advantages of the intermittent controllers compared to continuous feedback control since the former can provide persistent excitation due to their internal triggering mechanism, even when no external reference changes or disturbances are applied. Moreover, the results show that the event thresholds of intermittent control can be used to adjust the degree of responsiveness of the adaptation in the system, becoming a tool to balance the trade-off between steady-state performance and flexibility against parametric changes, addressing the stability–plasticity dilemma of adaptation and learning in control. [ABSTRACT FROM AUTHOR]

Tuning control parameters is an essential task for the controller in all practical applications. This paper presents a new Lyapunov-based off-line self-tuning control (OSTC). method. The OSTC is a nonlinear controller with applications to nonlinear systems. The OSTC uses the settling point and settling time to tune the control parameters. The presented algorithm tested 150,000 simulations to formulate the control parameters. The tuning task is based on the integral of the absolute error (IAE). and integral of time multiplied by absolute error (ITAE). performance criteria. Simulink/MATLAB was employed to test the OSTC method by defining two regulation and tracking control problems. For each control problem, six practices are considered, and the proposed algorithm is validated by comparing the formulated Equations of the control parameters. [ABSTRACT FROM AUTHOR]

Gas metal arc additive manufacturing (GMA-AM) is gaining great attention to produce large-scale parts owing to its merits of low device investments and high deposition rates. Despite this fact, a robust control system for deposition height in thin-walled parts is yet to be developed due to the lack of an appropriate controlling variable. This study attempts to propose an external wire-based GMA-AM technique to solve this issue. The influences of arc current, travel speed, and external wire feed speed (EWFS) on layer geometry were studied. It is indicated that the EWFS exhibits a greater influence on the layer height of thin walls than the arc current and the travel speed. The nozzle to the top surface distance (NTSD), monitored via a vision sensor, was extracted by Median filter, Sobel algorithm, Canny algorithm, and Hough transform. The dynamics between the NTSD and the EWFS were established via a nonlinear model. A parameter self-tuning fuzzy controller was designed to control the NTSD via changing the EWFS. System simulation tests and control tests were performed to validate the robustness of the parameter self-tuning fuzzy controller. [ABSTRACT FROM AUTHOR]

Introduction. The problem of determining the train coordinates on the approach section to the crossing is associated with the impact of destabilizing factors on the information primary detector ? the rail line with distributed parameters. This leads to an error in calculating train coordinates. The aim of the study is to develop and scientifically substantiate the principle of building a system for calculating train coordinates with self-tuning of the decision function under the influence of significant destabilizing factors on the information primary sensor. Materials and Methods. To solve the problem of reliable determination of train coordinates, we propose a two-phase principle for forming the decision function. At the first stage, by means of a training sample of images and using the learning principle, the decision function (model) of the system for calculating train coordinates is determined. When the train enters a fixed-length approach section, the mismatch is determined by comparing the calculated coordinate with the fixed one. The second stage is the self-tuning of the coefficients of the decision function until the required accuracy is achieved. Results. The article shows the stages of forming the decision function by two-dimensional images; there was developed and tested an algorithm for self-turning of the decision function under the influence of various destabilizing factors. Through using 6 attributes of components of current and voltage vectors at the rail line input, 6 solving functions were obtained. Various combinations of two-dimensional images were used as polynomial arguments. Discussion and Conclusion. The study results confirm the feasibility of forming decision function and its self-tuning. The maximum error in calculating coordinates for various combinations ranges from 9.97% (199.34 m) to 4.57% (91.49 m). The error of determination of 5% for two decisive functions satisfies the safety requirements, since in a 45-second time interval to activate an automatic crossing signal, a distance of 100 m is covered in 3 seconds, i.e. the elapsed time is only 3 seconds in a 45 second interval.

The cosmological constant and its phenomenology remain among the greatest puzzles in theoretical physics. We review how modifications of Einstein's general relativity could alleviate the different problems associated with it that result from the interplay of classical gravity and quantum field theory. We introduce a modern and concise language to describe the problems associated with its phenomenology, and inspect no-go theorems and their loopholes to motivate the approaches discussed here. Constrained gravity approaches exploit minimal departures from general relativity; massive gravity introduces mass to the graviton; Horndeski theories lead to the breaking of translational invariance of the vacuum; and models with extra dimensions change the symmetries of the vacuum. We also review screening mechanisms that have to be present in some of these theories if they aim to recover the success of general relativity on small scales as well. Finally, we summarize the statuses of these models in their attempts to solve the different cosmological constant problems while being able to account for current astrophysical and cosmological observations. [ABSTRACT FROM AUTHOR]

• An inlet-engine hybrid integrated modelling method with pressure dynamic self-tuning. • The proposed method combines the advantages of volume effect model and component level model. • A flight envelope verification on an actual controller proves its excellent stability and real-time performance. • The accuracy of the proposed method is verified by ground experimental data. With aircraft flying faster and higher, the interaction between the inlet and engine cannot be ignored. The inlet engine integration characteristics must be considered in engine design and performance evaluation. The total pressure between the inlet and compressor is a critical parameter that directly affects the accuracy of engine performance evaluation. The traditional solution to inlet-engine integrated modelling is to add an external balance equation to the component-level model (CLM), which may reduce real-time performance. In this study, an inlet-engine hybrid integrated modelling (IEHIM) method is proposed to solve this problem, which combines the advantages of the volume effect model and the CLM method. The proposed IEHIM method consists of an engine model, characteristics of the inlet component, and a pressure dynamic self-tuning (PDST) algorithm. The PDST algorithm coefficients were optimised to enhance the performance of the IEHIM method. Finally, the effectiveness of the IEHIM was verified through simulations and ground experiments. The method was also verified on an actual controller (operating frequency: 132 MHz), which exhibited a mass-flow matching error below 0.2 % and an average time consumption of 5.191 ms in each control cycle. The results were also compared with the actual experimental data, where the errors in the compressor entry total pressure and rotor speed were below 1.1520 % and 1.2195 %, respectively. [ABSTRACT FROM AUTHOR]

Computational intelligence (CI)-based methods offer a practical approach to overcoming the significant challenges posed by analytical and enumeration optimization methods when dealing with complex real-world problems. However, a notable drawback of these algorithms is the need for time-consuming and computationally demanding fine-tuning procedures to achieve optimal performance. This paper proposes a novel parameterless auto-tuning meta-heuristic architecture called the self-tuning multi-layer (STML). The fundamental concept behind this architecture involves a multi-layer structure where the inner layer optimizes the main problem. In contrast, the outer layer utilizes information obtained during the search to fine-tune the performance of the inner layer. This feature eliminates manual fine-tuning, as it can autonomously handle this task. A series of mathematical and benchmark problems were employed to demonstrate the computational prowess of the STML. The results indicate its superiority over other meta-heuristic algorithms. Additionally, the STML showcases robustness, as evidenced by the numerical proximity of results obtained from different independent runs on these benchmark problems. [Display omitted] • Self-tuning multi-layer (STML) autonomously carries out the optimization. • STML architecture eliminates manual parameter calibration. • STML removes the need for initial guesses on parameter impact for optimal results. • Preliminary results highlight STML's robustness and computational efficiency. [ABSTRACT FROM AUTHOR]

All-solid-state tunable lasers have been widely used in many fields including multi-photon microscopy, time-resolved photoluminescence, atomic physics, and so on owing to their broadband output spectrum range, good beam quality, and low noise. To cover the broad fluorescent line of the laser crystal as much as possible, a birefringent filter (BRF) is always the most popular candidate for acting as a tuning element. In this review, the tuning characteristics of BRF and the design rule as well as its progress in practical application are summarized. Especially, it is worth noting that laser crystal itself begins to act as the BRF for wavelength tuning except for its gain characteristic, which has paved a new way for developing a kind of novel tunable laser. We believe that this review will not only provide a valuable reference for the design of BRF but also lay the foundation for developing a new application of BRF.

Abstract Aiming at the problems of the poor adaptive ability in the current control methods for brushless DC motor, an adaptive fuzzy proportional integral derivative controller (AFPID) is proposed to realize the better control performance of speed for brushless DC motor. AFPID includes a conventional PID controller (C-PID) and PI + PD architectures with a configurable fuzzy logic controller (C-PID-FLC). The FLC in C-PID-FLC consists of two fuzzy inference engines, one is used to self-tune the parameters for PI control, the other is for the scaling factor self-tuning of PI control parameters. The PD structure in C-PID-FLC is mainly to reduce oscillation, overcome overshoot and speed up system response while effectively eliminating static errors. When the system reaches a certain stable state of rotation, AFPID adjusts the C-PID controller ground on the speed error, which saves control costs under the premise of ensuring control performance. AFPID adaptively realizes the respective advantages of C-PID and C-PID-FLC. Compared with other control methods, the merits of the proposed controller are highlighted. The results show that the AFPID controller has a better control performance regardless of changes in load disturbance and parameter variations. And through the change of mechanical parameters of brushless DC motor, the sensitivity of AFPID is analyzed.

Control of the family of systems that can be represented in the Euler Lagrange (EL) form is both challenging from a theoretical perspective and applicable to a broad spectrum of real systems. For this type of control problem, given that any parameter estimation error and disturbances are not directly addressed, the system performance deteriorates, and stability cannot be deduced in advance. Considering these issues, this work presents the design and the corresponding analysis of a saturation function based, model-free, continuous robust controller for mechanical systems represented in the EL form. In order to avoid chattering in controller input, which is a common problem in most robust and high-gain control designs, the proposed method makes use of continuously differentiable terms. The stability of the closed-loop system is ensured via rigorous Lyapunov-based arguments. To ease the tuning of the controller gain, an adaptive gain-tuning algorithm is proposed to be applied as an add-on. The effectiveness of the controller is demonstrated by a simulation study on a twin rotor multi-input-multi-output system (TRMS) model Furthermore, the feasibility of the proposed method is then tested on an in-house built, inherently unstable, and therefore extremely sensitive mobile robotic platform. In the experimental study, satisfactory performances are attained for both the controller and the gain-tuning algorithm where less than 0.5° error is obtained in roll and pitch angles and less than 1° error is achieved in the yaw direction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

Flexible and stretchable radiofrequency coils for magnetic resonance imaging represent an emerging and rapidly growing field. The main advantage of such coil designs is their conformal nature, enabling a closer anatomical fit, patient comfort, and freedom of movement. Previously, we demonstrated a proof-of-concept single element stretchable coil design with a self-tuning smart geometry. In this work, we evaluate the feasibility of scaling this coil concept to a multi-element coil array and the associated engineering and manufacturing challenges. To this goal, we study a dual-channel coil array using full-wave simulations, bench testing, in vitro, and in vivo imaging in a 3 T scanner. We use three fabrication techniques to manufacture dual-channel receive coil arrays: (1) single-layer casting, (2) double-layer casting, and (3) direct-ink-writing. All fabricated arrays perform equally well on the bench and produce similar sensitivity maps. The direct-ink-writing method is found to be the most advantageous fabrication technique for fabrication speed, accuracy, repeatability, and total coil array thickness (0.6 mm). Bench tests show excellent frequency stability of 128 ± 0.6 MHz (0% to 30% stretch). Compared to a commercial knee coil array, the stretchable coil array is more conformal to anatomy and provides 50% improved signal-to-noise ratio in the region of interest.

In the application of wireless power transfer system, parameter drift of resonant tank is a general situation which induces natural frequency of primary side or secondary side deviates from switching frequency. This frequency deviation phenomenon will deteriorate load-independent constant current characteristics and decrease output power capacity for an S/S compensation system. This article illustrates the problems caused by frequency deviation through output characteristic analysis and addresses them by a self-tuning method based on switched capacitors (SCs). The proposed self-tuning method requires no resonant parameters such as compensation capacitance and coil self-inductance in advance. Primary and secondary natural frequencies can be both tuned to switching frequency continuously by SCs. The proposed method is verified by a simulation and a 500 W S/S compensation experimental prototype. After completion of self-tuning process, load-independent constant current characteristic is recovered and the output power capacity is boosted, which are in consistent with the theoretical analysis of system output characteristics. The proposed method is also applicable to LCC/S and LCC/LCC topologies. [ABSTRACT FROM AUTHOR]

This paper formulates an enhanced Model-Reference-Adaptive-Controller (MRAC) that is augmented with a fuzzy-immune adaptive regulator to strengthen the disturbance-attenuation capability of closed-loop under-actuated systems. The proposed scheme employs the conventional state-space MRAC and augments it with a pre-configured fuzzy-immune mechanism that acts as a superior regulator to dynamically modulate the adaptation gains of the Lyapunov gain-adjustment law. The immunological computations increase the controller’s adaptability to flexibly manipulate the damping control effort under exogenous disturbances. The efficacy of the proposed Immune-MRAC law is comparatively analyzed under practical disturbance conditions by conducting real-time hardware experiments on the QNET rotary pendulum. The experimental outcomes validate the faster transient-recovery behavior and stronger damping effort of the proposed control law against the exogenous disturbances while preserving the system’s asymptotic stability and control energy efficiency. [ABSTRACT FROM AUTHOR]

The article is devoted to the selection of parameters and evaluation of the efficiency of an aviation engine based on a system approach, when the engine and power plant are considered as subsystems of a higher-level aircraft complex. To solve the problems of multiparameter optimization, complex mathematical models of the entire system, consisting of the aircraft and control systems, taking into account the properties of the used fuels, are developed. The integration of the aircraft engine and the aircraft is carried out on the basis of the conditions for ensuring mass balance, the volume layout of the starting thrust-mass ratio. In combined power plants, it is possible to consider engines of different types, for example, TPrE with parallel or sequential (tandem) arrangement of circuits, TRBEaf and ramjet and steam hydrogen rocket-turbine engines of several types, the parameters of the working process of which are optimized according to the conditions of a typical program flights The adaptation task can be solved by changing both control programs and parameters of intellectual regulators of individual subsystems, as well as the structure of individual subsystems and connections between them. The higher level determines which strategy and which adaptation algorithm to choose in this situation. The optimal behavior model of the system in the current situation is also determined here. At the next level (the level of the technological complex), a strategy for the integration of the control and planning systems of the gas station, technological equipment and information system is formed, depending on the modes of operation of the gas station and the fulfillment of the tasks set before it. The construction of the ACS GTE model in the VisSim modeling software package was completed, measures were taken to stabilize the system. The transfer functions of the main elements of the electronic automatic control system of the GTE were obtained: a speed sensor, a thermocouple, and a pressure sensor. sensor of the angular position, the actuator mechanism of the nozzle-valve, the movement of the aircraft by the pitch angle. [ABSTRACT FROM AUTHOR]

Clustering is a process widely studied in the field of pattern recognition. Despite the existence of numerous algorithms and continuous innovation, there are still unresolved issues. Clusters can exhibit diverse characteristics in terms of size, shape, and density, while noise and outliers pose challenges. One major hurdle is discovering a natural partition without relying on parameters or heuristics. In this paper, we propose a deep-learning framework capable of simulating clustering algorithms without the need for parameter tuning or embedding heuristics. Instead of developing a novel clustering algorithm, our framework can learn to cluster data in a natural and intuitive way without the need for manual intervention. The framework takes a database as input, provides a simple diagnosis of cluster presence, and identifies the discovered clusters without requiring any input from the investigator. Two phases are involved: a mapping phase that uses a deep-learning model trained by machine-learning experts to convert the original distance data into a more user-friendly representation and a decoding phase that generates the final partitions. Our preliminary version is currently limited to small databases and only simulates the behavior of the K-means algorithm. We conducted simulation experiments using both synthetic and real datasets to showcase the effectiveness and applicability of our idea. A demonstration will be available on the author's website 1 1 http://r-riad.net/.. [ABSTRACT FROM AUTHOR]

Linear actuators are widely used in all kinds of industrial applications due to being devices that convert the rotation motion of motors into linear or straight traction/thrust motion. These actuators are ideal for all types of applications where inclination, lifting, traction, or thrust is required under heavy loads, such as wheelchairs, medical beds, and lifting tables. Due to the remarkable ability to exert forces and good precision, they are used classic control systems and controls of high-order. Still, they present difficulties in changing their dynamics and are designed for a range of disturbances. Therefore, in this paper, we present the study of an electric linear actuator. We analyze the positioning in real-time and attack the sudden changes of loads and limitation range by the control. It uses a general-purpose control with self-tuning gains, which can deal with the essential uncertainties of the actuator and suppress disturbances, as they can change their weights to interact with changing systems. The neural network combined with PID control compensates the simplicity of this type of control with artificial intelligence, making it robust to drastic changes in its parameters. Unlike other similar works, this research proposes an online training network with an advantage over typical neural self-adjustment systems. All of this can also be dispensed with the engine model for its operation. The results obtained show a decrease of 42 % in the root mean square error (RMSE) during trajectory tracking and saving in energy consumption by 25 % . The results were obtained both in simulation and in real tests. [ABSTRACT FROM AUTHOR]

The trajectory tracking control for a class of wheeled mobile robots (WMRs) with kinematic parameter uncertainty is studied in this paper. Firstly, a new Lyapunov function is proposed, based on which, a new kinematic controller for WMRs without parameter uncertainty is designed to make the tracking error system asymptotically stable. Secondly, the parameter uncertainty of the kinematic model is taken into consideration and an modified adaptive law is designed to deal with the uncertainty. Then, in order to improve the tracking performance, a self-tuning algorithm of the parameters of the controllers is proposed based on neural networks. Finally, numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper presents a direct torque control method with possibilities of automatic algorithm modification in terms of torque ripple reduction. The algorithm is based on the conventional switching table with an arbitrary number of discretized voltage levels allowing higher space-voltage resolution and consequently lower torque ripple. The number of available voltage levels can be easily changed and subsequently torque ripple reduced without the need to modify the conventional switching table. Appropriate algorithm modifications leading to torque ripple reduction are automatized, making this kind of control method simple, effective, and suitable for upcoming smart drives in the rapidly growing industry 4.0. Selective back electromotive force compensation can also be part of automatization with the aim to improve the drive dynamics. Experimental validation presented in the paper confirms improvements in torque ripple reduction retaining the simple and time-effective control structure of the induction machine DTC drive. [ABSTRACT FROM AUTHOR]

A laser beam is a heat source with a high energy density; this technology has been rapidly developed and applied in the field of welding owing to its potential advantages, and supplements traditional welding techniques. An indepth analysis of its operating process could establish a good foundation for its application in China. It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process; it comprises a significant number of complex processes with random uncertain factors. Because of their nonlinear mapping and self-learning characteristics, artificial neural networks (ANNs) have certain advantages in comparison to traditional methods in the field of welding. Laser welding is a nonlinear dynamic process; these processes still pose a major challenge in the field of control. Therefore, establishing a stable model is a prerequisite for achieving accurate control. In this study, the identification and control of radial basis function neural networks in laser welding processes and self-tuning PID control methods are proposed to improve weld quality. Using a MATLAB simulation, it is shown that the proposed method can obtain a good description of the level of nonlinear dynamic control, and that the algorithm identification accuracy is high, practical, and effective. Using this method, the weld width quickly reaches the expected value and the system remains stable, with good robustness. Further, it ensures the stability and dynamic performance of the welding process and improves weld quality. [ABSTRACT FROM AUTHOR]

This paper describes the application of a self-tuning fuzzy sliding mode controller (STFSMC) to design the control signal of an electro-hydraulic actuator under standard road excitation based on ISO 8608. The proposed damper was designed to achieve effective vibration isolation with an actuator force. The proposed damper was incorporated into a quarter-car system through experiments for the subsequent dynamic analysis of the suspension system. A single-input single-output (SISO) fuzzy inference was strategically used to regulate the slope of a sliding surface. In addition, the fuzzy control methodology was applied to achieve self-tuning online output of the scale factor of the fuzzy sliding mode controller. The comfortable riding time and ride comfort level were also evaluated based on ISO 2631-1. Experimental results revealed that the proposed controller demonstrated evident improvements in vehicle vibration suppression and the ride quality in comparison with passive and fuzzy logic controller control systems. [ABSTRACT FROM AUTHOR]

To realize the tuning of three-phase dynamic wireless power receivers for rail vehicles, a non-real-time static self-tuning scheme based on the switched capacitor and the perturbation and observation (P&O) method is studied. First, the reactance regulation characteristics of the parallel and the series switched capacitor are discussed and compared. Then, to avoid the sophisticated mistuning detection, in the case of only observing the output voltage, the feasibility of using the P&O method to tune three phases of the receiver sequentially is analyzed. Furthermore, theoretical analysis shows that the switched capacitor can also be used to control the current phase difference between the three phases. Thereby, by introducing the current phase difference control, a fast three-phase simultaneous tuning strategy is proposed without additional hardware. Afterward, the self-tuning control scheme for the star-connected series-compensated receiver with the parallel and series switched capacitor is design in detail and be applied to a scaled-down prototype of a three-phase dynamic wireless power transfer system for verification. The result shows that the receiver can be tuned within the ±5% deviation of the main compensation capacitance, and the tuning speed of the proposed simultaneous tuning of three phases is about three times that of the sequence tuning.

In this work a self-tuning rate and queue based proportional and integral controller called SRQ-PI is proposed to efficiently control the queue length with small overshoot and faster settling time. SRQ-PI proposes a new control tracking function that maps level of congestion to the packet drop probability dynamically. In SRQ-PI, the incoming traffic rate is estimated and used with the proportional and integral controller. The SRQ-PI tunes itself and stabilizes the system with internal feedback without requiring any external feedback. Furthermore, the stability of the SRQ-PI is analyzed using control theory and presents systematic guidelines to select the control gain parameters. NS2 is used to carry out the simulation work. The simulation result demonstrates that SRQ-PI is stable and gets faster transient response due to lower average delay jitter and robust against dynamic network parameters. The SRQ-PI outperforms proportional integral (PI), Intelligent adaptive PI (IAPI) and Random exponential marking (REM) algorithm.

This paper formulates an original hierarchical self-tuning control procedure to enhance the disturbance-rejection capability of under-actuated rotary pendulum systems against exogenous disturbances. The conventional state-feedback controllers generally make a trade-off between the robustness and control effort in a closed-loop system. To combine the aforementioned characteristics into a single framework, this paper contributes to develop and augment the baseline Linear-Quadratic-Regulator (LQR) with a novel “adjustable degree-of-stability design” module. This augmentation dynamically relocates the system's closed-loop poles in the stable (left-half) region of the complex plane by dynamically adjusting a single hyper-parameter that modifies the constituents of LQR's performance index. The hyper-parameter is adaptively modulated online via a pre-calibrated hyperbolic-secant-function that is driven by state-error variables. The performance of the proposed adaptive controller is benchmarked against fixed-gain controllers via credible hardware experiments conducted on the standard QNET Rotary Pendulum setup. The experimental outcomes indicate that the proposed controller significantly enhances the system's robustness against exogenous disturbances and maintains its stability within a broad range of operating conditions, without inducing peak servo requirements.

Fuzzy Logic Controller (FLC) as speed controller is preferred in many AC machine drives, due to its ability to handle model non-linearity, speed variations and parameters change. Additionally, Self-Tuning FLC (ST-FLC) is a modified FLC controller to overcome the issues associated with a fixed parameter FLC and to avoid performance degradation of the machine drive. It can update the FLC parameters in accordance to any variation, changes or disturbances that may occur to the drive system. However, FLC system requires huge computation capacity which increases the computational burden of the overall machine drive system and may result in poor performance. This research proposed a simple ST-FLC mechanism to tune the main FLC speed controller. Three different rule-size of FLC (9, 25, and 49) rules are implemented with ST-FLC mechanism based Induction Motor (IM) drive. Performance comparison of the three different rule-size based ST-FLC is conducted based on simulation and experimental analysis. In addition, a computational effort is technically analyzed and compared for the three different rule-size. In the experiment, ST-FLC with less number of rules (9-rules) shows superior performance, lower sampling and lower computational efforts compared to ST-FLC with higher rule-size (25, 49) rules.

This paper presents a simulation and hardware implementation of a closed loop control of a separately excited D.C. motor using a self-tuning PID controller. The PID controller design is based on using the Field Programmable Analog Array (FPAA) technology. Parameters tuning of the PID controller is achieved by using the genetic algorithm (GA). The FPAA controller based technology gives the advantage of low power, no quantization noise, high bandwidth and high speed response. The practical results show that a self-tuning controller can outperform a hand-tuned solution and demonstrate adaptability to plant drift; also it gives very acceptable results in the reduction of overshoot, stability time and the steady-state transient response of the controlled plant.

This paper presents the self-tuned Automatic Generation Control for an interconnected power system with dominant wind energy penetration. The uncertain behavior of wind power plant has random fluctuations on the frequency of the power system and optimum performance cannot be achieved by the fixed structure controller. Thus, the self-tuned controller can handle the load disturbances of varying nature efficiently, by generating control signal based on least square method and minimum variance strategy. A two-area interconnected power system model consists of thermal, hydro and doubly fed induction generator-based wind power plants with dominant participation factor and random power output is considered for the simulation study. The self-tuned AGC efficiently regulates the variation in area control error caused by load disturbance and wind fluctuation, by adjusting its parameter automatically based on least square algorithm. Moreover, controller parameters also adjust its parameters qualitatively based on equipment parameter under various operating condition.

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