Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback

In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems.

An event-triggered mechanism to observer-based sliding mode control of fractional-order uncertain switched systems

This paper is dealing with the problem of observer-based event-triggered sliding mode control for fractional-order uncertain switched systems with a positive order less than one. Firstly, a fractional-order state observer is designed, based on which a fractional-order integral sliding surface function is proposed. Then, utilizing the estimated observer error and sliding mode error vectors, an event-triggered condition is constructed to decide whether the current control signal should be updated or not. Besides, sufficient conditions are derived in the forms of linear matrix inequalities (LMIs) to ensure finite-time stability of the augmented closed-loop system by adopting an average dwell time approach. Thereafter, to avoid the occurrence of infinite triggers within finite time, this paper also discusses the Zeno behavior and refines the results in the previous literature. Finally, to illustrate the effectiveness and superiority of the proposed method, three numerical simulations are provided.

A Deterministic Annealing Neural Network Algorithm for the Minimum Concave Cost Transportation Problem

In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.

RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to Offshore Cranes

This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property. Third, based on the RBF neural network approach, an adaptive control law is designed to ensure the finite-time existence of sliding motion in the face of unknown nonlinearity. Fourth, feasible easy-checking linear matrix inequality conditions are developed to analyze passification performance of the resulting sliding motion. Finally, a simulation study is provided to confirm the validity of the proposed method.

Input-Output Finite-Time Generalized Dissipative Filter of Discrete Time-Varying Systems With Quantization and Adaptive Event-Triggered Mechanism

This article discusses the issue of input-output finite-time generalized dissipative filter design for a class of discrete time-varying systems. First, an adaptive event-triggered mechanism (AETM) with an adaptive law is proposed to adjust the threshold in the AETM according to the error between the system states and the filter states. Such an AETM determines whether the measurement output should be transmitted or not, which is more effective to economize the communication resources comparing with the traditional event-triggered mechanism. Second, in view of network-induced delays, the quantization and the AETM, a time-varying filter error system (TV-FES) is modeled. Then, a new augmented time-varying Lyapunov functional containing triple sum terms is provided. Based on the new finite-sum inequality and improved reciprocally convex combination lemma, delay-dependent conditions are obtained, which can ensure the TV-FES to be input-output finite-time stable and satisfy the given generalized dissipative performance. Moreover, the recursive linear matrix inequalities are presented to obtain the desired filter gains. Finally, numerical examples demonstrate the superiority and feasibility of the proposed method in this article.

Modeling and output tracking of transverse flux permanent magnet machines using high gain observer and RBF neural network

This paper deals with modeling and adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing high gain observer and radial basis function networks. The proposed model is developed based on computing the permeance between rotor and stator using quasiflux tubes. Based on this model, the techniques of feedback linearization and Hinfinity control are used to design an adaptive control law for compensating the unknown nonlinear parts, such as the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method in tracking both the angle and the angular velocity is shown in the simulation results.

A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks

Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.

Single-Input Pinning Controller Design for Reachability of Boolean Networks

This brief is concerned with the problem of a single-input pinning control design for reachability of Boolean networks (BNs). Specifically, the transition matrix of a BN is designed to steer the BN from an initial state to a desirable one. In addition, some nodes are selected as the pinning nodes by solving some logical matrix equations. Furthermore, a single-input pinning control algorithm is given. Eventually, a genetic regulatory network is provided to demonstrate the effectiveness and feasibility of the developed method.

Adaptive Optimal Control for Unknown Constrained Nonlinear Systems With a Novel Quasi-Model Network

A policy-iteration-based algorithm is presented in this article for optimal control of unknown continuous-time nonlinear systems subject to bounded inputs by utilizing the adaptive dynamic programming (ADP). Three neural networks (NNs), called critic network, actor network, and quasi-model network, are utilized in the proposed algorithm to give approximations of the control law, the cost function, and the function constituted by partial derivatives of value functions with respect to states and unknown input gain dynamics, respectively. At each iteration, based on the least sum of squares method, the parameters of critic and quasi-model networks will be tuned simultaneously, which eliminates the necessity of separately learning the system model in advance. Then, the control law is improved by satisfying the necessary optimality condition. Then, the proposed algorithm's optimality and convergence properties are exhibited. Finally, the simulation results demonstrate the availability of the proposed algorithm.

Output Multiformation Tracking of Networked Heterogeneous Robotic Systems via Finite-Time Hierarchical Control

This article investigates the finite-time output multiformation tracking (OMFT) problem of networked heterogeneous robotic systems (NHRSs), where each robot model involves external disturbances, parametric uncertainties, and possible kinematic redundancy. Besides, the interactions among robotic systems are described as a directed graph with an acyclic partition. Then, several novel practical finite-time hierarchical control (FTHC) algorithms are designed. The convergence analysis of the closed-loop dynamics is extremely difficult due to the lack of effective analysis methods. Based on the mathematics induction and reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.

Deep reinforcement learning and fuzzy logic controller codesign for energy management of hydrogen fuel cell powered electric vehicles

Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration. More specifically, this research paper establishes a power system model with three DC-DC converters, which includes a hierarchical energy management framework employed in a two-layer control strategy. Three loop control strategies for hybrid electric vehicles based on reinforcement learning are designed in the low-level layer control strategy. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) is used with a network. Three DRL controllers are designed using the hierarchical energy optimization control architecture. The comparative results between the two strategies, Deep Reinforcement Learning and Fuzzy logic supervisory control (DRL-F) and Super-Twisting algorithm and Fuzzy logic supervisory control (STW-F) under the EUDC driving cycle indicate that the proposed model DRL-F can ensure the Root Mean Square Error (RMSE) reduction for 21.05% compared to the STW-F and the Mean Error reduction for 8.31% compared to the STW-F method. The results demonstrate a more robust, accurate and precise system alongside uncertainties and disturbances in the Energy Management System (EMS) of FCHEV based on an advanced learning method.

On the fatigue life of dental implants: Numerical and experimental investigation on configuration effect

Dental implants have seen widespread and successful use in recent years. Given their long-term application and the critical role of geometry in determining fracture and fatigue characteristics, fatigue assessments are of utmost importance for implant systems. In this study, nine dental implant system samples were subjected to testing in accordance with ISO 14801 standards. The tests included static evaluations to assess ultimate loads and fatigue tests conducted under loads of 270 N and 230 N at a frequency of 15 Hz, aimed at identifying fatigue failure locations and fatigue life. Fatigue life predictions and related calculations were carried out using Fe-safe software. The initial model featured a 22° angle for both the fixture and abutment. Subsequently, variations in abutment angles at 21° and 23° were considered while keeping the fixture angle at 22°. In the next phase, the fixture and abutment angles were set as identical, at 21° and 23°. The results unveiled that when the angles of the abutment and fixture matched, stress values decreased, and fatigue life increased. Conversely, models featuring abutment angles of 21° and 23°, with a 22° angle for the fixture, led to a 49.1 % increase in stress and a 36.9 % decrease in fatigue life compared to the primary model. Notably, in the case of the implant with a 23° angle for both abutment and fixture, the fatigue life reached its highest value at 10 million cycles. Conversely, the worst-case scenario was observed in the implant with a 21° abutment angle and a 23° fixture angle, with a fatigue life of 5.49 million cycles.

ShadowGAN-Former: Reweighting self-attention based on mask for shadow removal

Shadow removal remains a challenging visual task aimed at restoring the original brightness of shadow regions in images. Many existing methods overlook the implicit clues within non-shadow regions, leading to inconsistencies in the color, texture, and illumination of the reconstructed shadow-free images. To address these issues, we propose an efficient hybrid model of Transformer and Generative Adversarial Network (GAN), named ShadowGAN-Former, which utilizes information from non-shadow regions to assist in shadow removal. We introduce the Multi-Head Transposed Attention (MHTA) and Gated Feed-Forward Network (Gated FFN), designed to enhance focus on key features while reducing computational costs. Furthermore, we propose the Shadow Attention Reweight Module (SARM) to reweight the self-attention maps based on the correlation between shadow and non-shadow regions, thereby emphasizing the contextual relevance between them. Experimental results on the ISTD and SRD datasets show that our method outperforms popular and state-of-the-art shadow removal algorithms, with the SARM module improving PSNR by 5.42% and reducing RMSE by 14.76%.

Stochastic dynamic output feedback stabilization of uncertain stochastic state-delayed systems

This paper addresses the problem of stochastic dynamic output feedback (SDOF) stabilization for a class of stochastic continuous-time state-delayed systems with norm-bounded nonlinear uncertainties. The aim is to design a linear, delayless, and SDOF control for all admissible uncertainties. The designed control ensures stochastically exponentially stability in the mean square, independent of the deterministic time delay. Using the Finsler's lemma, the necessary and sufficient conditions for the existence of such a control are proposed in terms of certain linear matrix inequalities. These results are illustrated with a simple example to demonstrate the applicability of the proposed design approach.

Effect of General Medical Degree Curricular Change on Mental Health of Medical Students: A Concurrent Controlled Educational Trial

Objective: General medical degree (GMD) curriculum usually causes significant psychological distress for medical students, especially in transition periods between preclinical, clerkship, and internship periods. This study was conducted to assess the effect of curricular change in GMD program on mental health of medical students in internship period. Method : This study evaluated mental health of 2 concurrent groups of medical students under reformed and non-reformed GMD curriculum. In this study, 120 out of 180 interns in the non-reform GMD program and 60 interns in the reformed GMD program were selected and their mental health status evaluated using Symptom Checklist-90-Revised (SCL-90-R) questionnaire. The cut-off point of 0.7 was used for Global Severity Index (GSI) score. SPSS software, version 14 (SPSS Inc, Chicago, Il, USA) was used for analysis. Chi-square, Fisher's exact test, t student, Mann-Whitney U, one-way ANOVA, and Kruskal-Wallis tests were used when appropriate. Logistic regression was used to estimate odds ratios for various determinants of students' mental health. Results: About half of the participants in the 2 groups were male (P = 0.63), and the mean age of the students in the reformed and non-reformed programs was 24.8 (1.97) and 24.7(1.80), respectively (P = 0.9). About 20% of participants in the non-reformed and less than 2% of those in the reformed program had GSI score of more than 0.7. Medical students in the reformed program had lower scores in total GSI and 9 its dimensions (P<0.001). The results obtained from the logistic regression analysis indicated that reformed curriculum and good economic status were significant independent variables contributing to decreased psychological distress (OR = 0.016 and 0.11, respectively). Conclusion: The results revealed that curricular changes which were based on World Federation of Medical Education recommendation, could be associated with improvement in mental health status of medical students.

In vitro propagation of some old Persian cypress accessions (Cupressus sempervirens L.) by embryo culture

In order to study in vitro propagation of some old Persian cypress genotypes (Cupressus sempervirens L.), embryos of twenty old cypress accessions were cultured on MS and SH media containing 100 and 200 mg L-1 myo-inositol. Germination percentage and growth parameters of produced plantlets and their hardening off were evaluated. Results showed that the highest germination percentage and germination rate was obtained with MS medium containing 100 mg L-1 myo-inositol, although no significant difference was observed with MS containing 200 mg L-1 myo-inositol and SH containing 100 mg L-1 myo-inositol. Furthermore, based on the results, the highest root length was gained with SH medium containing 200 mg L-1 myo-inositol. Germination percentage of isolated embryos and shoot length of produced plantlets were affected by genotype, so that the highest germination percentage and shoot length was obtained with KB and KT genotypes, respectively. The oldest genotype which was 4000 years old (Abarkuh cypress) showed no significant difference with other genotypes in terms of shoot and root length. In hardening-off step, first all genotypes were initially established but after 2 weeks, KJ2, KK, KSHN, KD1, KB genotypes died.

Fixed-time periodic stabilization of discontinuous reaction-diffusion Cohen-Grossberg neural networks

This paper aims to study the fixed-time stabilization of a class of delayed discontinuous reaction-diffusion Cohen-Grossberg neural networks. Firstly, by providing some relaxed conditions containing indefinite functions and based on inequality techniques, a new fixed-time stability lemma is given, which can improve the traditional ones. Secondly, based on state-dependent switching laws, the periodic wave solution of the formulated networks is transformed into the periodic solution of ordinary differential system. By utilizing differential inclusions theory and coincidence theorem, the existence of periodic solutions is obtained. Thirdly, based on the new fixed-time stability lemma, the periodic solutions are stabilized at zero in a fixed-time, which is a new topic on reaction-diffusion networks. Moreover, the established criteria are all delay-dependent, which are less conservative than the previous delay-independent ones for ensuring the stabilization of delayed reaction-diffusion networks. Finally, two examples give numerical explanations of the proposed results and highlight the influence of delays.