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The editorial in the Journal of Intelligent Manufacturing discusses the increasing use of information technologies in manufacturing organizations, focusing on AI and data-driven decisions. The special issue highlights data-driven industrial decision-making techniques and theoretical frameworks for AI applications in manufacturing. The editorial also mentions the ASSISTANT project, funded by the European Commission, which introduced intelligent digital twins to enhance decision-making in manufacturing. The special issue covers four key themes: advanced AI-based production and logistics systems design, AI-based planning and scheduling, AI-based quality control and predictive maintenance, and AI-driven process optimization and intelligent control systems. [Extracted from the article]

ABSTRACT This article focuses on operationalizing a quality improvement framework to improve the quality and safety of patients in hospitals. Assessing the effectiveness of health care services, especially utilizing various patient health statuses, poses several difficulties. The artificial neural networks model assesses patient risk factors and enhances proper management. Our proposed approach extends the exponentially weighted moving average (EWMA) control chart to a risk‐adaptive EWMA chart. This chart is developed from residuals estimated from the artificial neural networks model, thus allowing an assessment of actual data from the patients undergoing cardiothoracic surgery. For patient evaluation, we employ artificial neural networks to establish the suggested control chart. The results indicate that this chart outperforms other methods, such as the risk‐adjusted EWMA chart for shift detection, which may present improvements in the healthcare system's patient care. [ABSTRACT FROM AUTHOR]

This study introduces a cutting-edge approach to regulating DC motors, featuring a unique combination of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. This innovative system capitalizes on the adaptive learning capabilities of ANNs to dynamically fine-tune the control parameters of DC motors. This adaptability ensures optimal motor performance across diverse operational conditions, addressing the challenges posed by fluctuating loads and varying speed requirements. The integration of LSTM networks into this framework adds a layer of predictive functionality, allowing the system to anticipate future motor states. Such foresight enables the regulator to make proactive adjustments, significantly enhancing its responsiveness to changes in operational demands. The dual application of ANN's adaptive control mechanisms and LSTM's predictive capabilities is particularly effective in overcoming the non-linearity and variability that are typical challenges in DC motor control. This synergy ensures that the motor operates efficiently, stably, and with a quick response time, even under varying and unpredictable conditions. The practical application of this advanced regulator in real-world scenarios has shown marked improvements in motor performance. These enhancements are evident in the increased efficiency, stability, and responsiveness of the motors, making them more suitable for a wide range of industrial applications. This study marks a notable progression in the field of DC motor control technology. By integrating advanced machine learning techniques, it offers a solution that is not only more efficient and reliable but also adaptable to the evolving demands of industrial environments. The innovative combination of ANN and LSTM networks in this regulator design paves the way for smarter, more responsive, and efficient motor control systems, potentially transforming how motors are managed in various industrial applications. [ABSTRACT FROM AUTHOR]

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. [ABSTRACT FROM AUTHOR]

A comparative analysis was conducted in current research on the control strategies utilized in three-phase 15-level Cascaded H-Bridge Multilevel inverter (CHBMLI)-fed different RL loads with a three-phase induction motor, generally applied in industries. The analysis of 15-level CHBMLI, with Sliding Mode Control (SMC), the sliding mode control with an artificial neural network (SMC-ANN), and sliding mode control with an Adaptive Neuro-Fuzzy Inference Strategy (SMC-ANFIS) controller are explained. The study compared the performance achieved by controllers at four different speeds of a 3ϕ inductor motor and parallel-connected RL load dynamic change under closed-loop operations. The comparative study results demonstrate that there should be a reduction in the methodology suggested in terms of both the quantity of power devices and Total Harmonic Distortion (THD). The researcher conducted the experimentation procedures to authenticate the proposed 15-level MLI topology's performance. The SMC-ANFIS control technique produces less voltage and lower current total harmonic distortion of 5.53% and 2.74% compared to the SMC-ANN control technique in both simulation and real-time implementations. [ABSTRACT FROM AUTHOR]

Summary: This research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others. [ABSTRACT FROM AUTHOR]

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments. [ABSTRACT FROM AUTHOR]

The cerebellum plays a central role in motor control and learning. Its neuronal network architecture, firing characteristics of component neurons, and learning rules at their synapses have been well understood in terms of anatomy and physiology. A realistic artificial cerebellum with mimetic network architecture and synaptic plasticity mechanisms may allow us to analyze cerebellar information processing in the real world by applying it to adaptive control of actual machines. Several artificial cerebellums have previously been constructed, but they require high-performance hardware to run in real-time for real-world machine control. Presently, we implemented an artificial cerebellum with the size of 104 spiking neuron models on a field-programmable gate array (FPGA) which is compact, lightweight, portable, and low-power-consumption. In the implementation three novel techniques are employed: (1) 16-bit fixed-point operation and randomized rounding, (2) fully connected spike information transmission, and (3) alternative memory that uses pseudo-random number generators. We demonstrate that the FPGA artificial cerebellum runs in real-time, and its component neuron models behave as those in the corresponding artificial cerebellum configured on a personal computer in Python. We applied the FPGA artificial cerebellum to the adaptive control of a machine in the real world and demonstrated that the artificial cerebellum is capable of adaptively reducing control error after sudden load changes. This is the first implementation and demonstration of a spiking artificial cerebellum on an FPGA applicable to real-world adaptive control. The FPGA artificial cerebellum may provide neuroscientific insights into cerebellar information processing in adaptive motor control and may be applied to various neuro-devices to augment and extend human motor control capabilities. [ABSTRACT FROM AUTHOR]

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems. [ABSTRACT FROM AUTHOR]

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system's fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%. [ABSTRACT FROM AUTHOR]

Memristors have several chaotic dynamic models and have been used successfully in various fields, including secure communication systems, information storage, and artificial neural networks. The memristor-based four-dimensional chaotic (FDMC) systems generate unpredictable and intricate time domain signals. Parameter fluctuations in the FDMC system may give birth to chaos, making it difficult to suppress. Stabilizing chaos in the FDMC system improves the circuit's performance. This paper synthesizes a novel time-efficient chatter-free nonlinear robust adaptive control (NLRAC) technique that stabilizes chaos in the FDMC system affected by time-varying unknown bounded exogenous disturbances and model uncertainties. The proposed NLRAC strategy decimates the time-varying unknown bounded exogenous disturbances and model uncertainties effects; it establishes a faster, smoother state-variable trajectories convergence to the zero vicinity. The theoretical analysis and mathematical proofs are based on the Lyapunov stability technique. Computer simulation results show that the proposed NLRAC technique effectively brings the FDMC system's state-variable trajectories to zero with reduced fluctuations for control input signals and state-variable trajectories. This feedback controller's attribute enhances closed-loop stability performance, improves precision, and reduces risk overshoot. The paper includes comparative computer simulation results to endorse the proposed controller performance. [ABSTRACT FROM AUTHOR]

This study aims to improve the control of robotic knee flexion during walking, with a particular emphasis on enhancing mobility and rehabilitation for patients with mobility problems. The objective is to develop a high-performance controller by integrating the Desired Optimal Controller (DOC)-based Multivariable Model Reference Adaptive Control (MRAC) algorithm with sophisticated optimization techniques. This study notably combines the Whale Optimization Algorithm (WOA) with a novel approach called Combined WOA-KHO to precisely optimize controller parameters. The technique provides a thorough explanation of the construction of the DOC-based MRAC algorithm, which employs a second-order transfer function for the reference model. This study emphasizes the inclusion of adaptive gains, the structural characteristics of the best controller, and the implementation of a deep neural network (DNN)-PID control system utilizing a Multi-Layer Feed-Forward Neural Network (MLFNN). In addition, this text elaborates on the optimization strategies, namely the employment of the Whale Optimization Algorithm (WOA) and the Combined WOA-KHO algorithm. The simulation results clearly demonstrate the gradual improvement of the system's performance, providing evidence for the effectiveness of the suggested DOC-based MRAC algorithm and the optimization approaches. An extensive examination of the system's response characteristics, such as settling time, rising time, and steady-state error, is performed using several simulations. A performance comparison is implemented between three optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and WOA. The study finds that using all three algorithms together significantly improved the gait control of a robotic knee system, outperforming the results obtained from traditional algorithm. [ABSTRACT FROM AUTHOR]

In this paper, by introducing memristive connections and inertial term into quaternion‐valued neural networks, the quaternion‐valued inertial memristive neural networks (QVIMNNs) is proposed. Based on the adaptive control strategy, the synchronization problem of the QVIMNNs is analyzed in this work. Two different assumption for activation functions are considered in this work; new criteria for global synchronization are then derived by designing novel adaptive controllers and update laws. Lastly, numerical examples are given to verify the results obtained in this work. [ABSTRACT FROM AUTHOR]

Electrical drive systems are commonly applied for the mechanisms of precise movement, where having a high-quality position and high-quality speed control is especially valuable. Very often, the mechanical part of these systems reveals resonant properties that are related to the limited stiffness of the interconnection between subsequent parts of the mechanism. In most cases, this sort of system may be described as a model of several linked masses. If only the structure of the mechanical part is known and the corresponding parameters are constant and identified, the demanded control quality may be obtained using a properly tuned ADRC or PID controller equipped with appropriate anti-resonance filtration. However, if the parameters of the mechanical part are variant, adaptive control may be considered as a solution. In this paper, artificial neural network (ANN) is considered to be a speed controller and its training method assures adaptation to the unknown mechanical parameters. The paper is particularly focused on a three-mass system, which possesses, due to its structure, two resonant frequencies. The unique property of the analyzed system is the application of drive units at both ends of the system, so that the controller has the ability to influence the resonant system from both sides. The coordination of the drive unit is performed by the aforementioned ANN, from which two outputs affect the drive units independently. The derivation of the mathematical model is followed by its implementation in a computer simulation and finally the evaluation in a dedicated laboratory setup, the construction of which is also presented in the paper. [ABSTRACT FROM AUTHOR]

Robotic systems have experienced exponential growth in their utilization for manufacturing applications over recent decades. Control systems responsible for executing desired robot motion planning face increasingly stringent performance requirements. These demands encompass high precision, efficiency, stability, robustness, ease of use, and simplicity of the user interface. Furthermore, diverse modern manufacturing applications primarily employ robotic systems within disturbed operating scenarios. This paper presents a novel neural motion-tracking control scheme for mobile manipulation robotic systems. Dynamic position output error feedback and B–Spline artificial neural networks are integrated in the design process of the introduced adaptive robust control strategy to perform efficient and robust tracking of motion-planning trajectories in robotic systems. Integration of artificial neural networks demonstrates performance improvements in the control scheme while effectively addressing common issues encountered in manufacturing environments. Parametric uncertainty, unmodeled dynamics, and unknown disturbance torque terms represent some adverse influences to be compensated for by the robust control scheme. Several case studies prove the robustness of the adaptive neural control scheme in highly coupled nonlinear six-degree-of-freedom mobile manipulation robotic systems. Case studies provide valuable insights and validate the efficacy of the proposed adaptive multivariable control scheme in manufacturing applications. [ABSTRACT FROM AUTHOR]

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical role of proprioception is representing the body state. We hypothesize that the proprioceptive system could also be critical for high-level tasks such as action recognition. To test this theory, we pursued a task-driven modeling approach, which allowed us to isolate the study of proprioception. We generated a large synthetic dataset of human arm trajectories tracing characters of the Latin alphabet in 3D space, together with muscle activities obtained from a musculoskeletal model and model-based muscle spindle activity. Next, we compared two classes of tasks: trajectory decoding and action recognition, which allowed us to train hierarchical models to decode either the position and velocity of the end-effector of one's posture or the character (action) identity from the spindle firing patterns. We found that artificial neural networks could robustly solve both tasks, and the networks' units show tuning properties similar to neurons in the primate somatosensory cortex and the brainstem. Remarkably, we found uniformly distributed directional selective units only with the action-recognition-trained models and not the trajectory-decoding-trained models. This suggests that proprioceptive encoding is additionally associated with higher-level functions such as action recognition and therefore provides new, experimentally testable hypotheses of how proprioception aids in adaptive motor control. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive neural control (ANC) method for the coupled nonlinear model of a novel type of embedded surface morphing aircraft which has a tiltable V-tail. A nonlinear model with sixdegrees-of-freedom is established. The first-order sliding mode differentiator (FSMD) is applied to the control scheme to avoid the problem of "differential explosion". Radial basis function neural networks are introduced to estimate the uncertainty and external disturbance of the model, and an ANC controller is proposed based on this design idea. The stability of the proposed ANC controller is proved using Lyapunov theory, and the tracking error of the closed-loop system is semi-globally uniformly bounded. The effectiveness and robustness of the proposed method are verified by numerical simulations and hardware-in-the-loop (HIL) simulations. [ABSTRACT FROM AUTHOR]

This research focuses on designing a min–max robust control based on a neural dynamic programming approach using a class of continuous differential neural networks (DNNs). The proposed controller solves the robust optimization of a proposed cost function that depends on the trajectories of a system with an uncertain mathematical model satisfying a class of non-linear perturbed systems. The dynamic programming min–max formulation enables robust control concerning bounded modelling uncertainties and disturbances. The Hamilton–Jacobi–Bellman (HJB) equation's value function, approximated by a DNN, permits to estimate the closed-loop formulation of the controller. The controller design is based on an estimated state trajectory with the worst possible uncertainties/perturbations that provide the degree of robustness using the proposed controller. The class of learning laws for the time-varying weights in the DNN is produced by studying the HJB partial differential equation. The controller uses the solution of the obtained learning laws and a time-varying Riccati equation. A recurrent algorithm based on the Kiefer–Wolfowitz method leads to adjusting the initial conditions for the weights to satisfy the final condition of the given cost function. The robust control suggested in this work is evaluated using a numerical example confirming the optimizing solution based on the DNN approximate for Bellman's value function. [ABSTRACT FROM AUTHOR]

Control systems that ensure robot operation during failures are necessary, particularly when manipulators are operating in hazardous or hard-to-reach environments. In such applications, fault-tolerant robot controllers should detect failures and, using fault-tolerant control methods, be able to continue operation without human intervention. Fault-tolerant control (FTC) is becoming increasingly important in all industries, including production lines in which modern robotic manipulators are used. The use of fault-tolerant systems in robotics can prevent the production line from being immobilized due to minor faults. In this paper, an overview of the current state-of-the-art methods of fault-tolerant control in robotic manipulators is provided. This review covers publications from 2003 to 2022. The article pays special attention to the use of artificial intelligence (AI), i.e., fuzzy logic and artificial neural networks, as well as sliding mode and other control methods, in the FTC of robotic manipulators. The cited and described publications were mostly found using Google Scholar. [ABSTRACT FROM AUTHOR]

This article explores the application of deep neural networks (DNNs) for optimized backstepping control design in a category of stochastic nonlinear strict-feedback systems with unknown dynamics, focusing on prescribed performance. DNNs, distinguished by their ability to handle high-complexity functions and enhance function approximation, are employed to estimate unknown nonlinear functions. The weight updating strategy for each layer of DNNs is determined through a Lyapunov function analysis. Tomitigate potential issues such as the tracking error exploding at uncertain moments, prescribed performance control (PPC) is introduced to constrain the tracking error within a predetermined range. Subsequently, a novel optimal tracking control scheme, integrating the backstepping method and Hamilton–Jacobi–Bellman (HJB) equation, is presented. The results indicate that all signals in the closed-loop system are bounded in probability, and the tracking error is maintained within a set of predefined arbitrarily small residuals. Simulation outcomes affirm the efficacy of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, a novel full feedback recurrent neural network (FFRNN) based integral sliding mode control (SMC) strategy is proposed for the altitude and attitude control problem of quad-rotor unmanned aerial vehicles (UAVs). The proposed method ensures fast convergence of the sliding surface as well as asymptotic convergence of the system state. Due to the high degree of the uncertainties of parameters for the quad-rotor UAV, it is very difficult to establish its accurate dynamic model. Therefore, the novel FFRNN structure is used to approximate the model uncertainty. The FFRNN has two hidden layers, and every hidden layer receives feedback signals from the next layer, which enhances its approximation power. Since this new structure combines the advantages of the inner/outer feedback neural network, more information can be saved inside the neural network, and the newly designed FFRNN has better approximation capacity, higher accuracy and faster network training speed than the neural networks without feedback loop and the single feedback loop neural network. Lyapunov stability theory is used to ensure the stability of the whole system, and then the real-time adaptive laws of FFRNN weights are derived. To validate the efficacy of the proposed approach, a comparative analysis is conducted between FFRNN and two other neural network models, namely radial basis function (RBF) neural network and double loop recurrent neural network (DLRNN), during the experiment. Simulation results indicate that the proposed integral sliding mode controller based on FFRNN has a better control performance, approximation performance and stability compared to sliding mode controller based on RBF and DLRNN. [ABSTRACT FROM AUTHOR]

Combustion engine speed control faces significant uncertainties in parameters like mass equivalent coefficient and efficiency. To address these challenges, this research develops an adaptive, self-tuning artificial neural network (ANN) control design that aims to optimize engine speed despite load torque and model uncertainties. We integrate three controllers—linear quadratic regulator (LQR), proportional-integral-derivative (PID), and sliding mode controller (SMC)—with machine learning techniques to evaluate their performance in hybrid electric vehicle engines. The ANN-based self-tuning controller identifies the most effective method for maintaining idle engine speed with minimal deviation. The results indicate that the ANN can accurately predict settling times and enhance controller performance, leading to reduced settling time errors, improved fuel economy, and better emissions performance. The study highlights LQR's high sensitivity in the lower settling time range and reduced sensitivity in the higher range, the PID controller's varied sensitivity, and SMC's consistent sensitivity across both ranges. A well-trained ANN is essential for optimizing control parameters and maintaining system stability. The ANN was trained and validated using the nntool MATLAB toolbox, with tests conducted across lower and higher settling time ranges. Results showed that the PID controller's performance was overfitted by insufficient training in the lower range, while the LQR and SMC demonstrated potential for more efficient control in both ranges. Overall, the study underscores the importance of a proficiently trained ANN for achieving precise control in hybrid electric vehicle engines. [ABSTRACT FROM AUTHOR]

The integration of autonomous vehicles (AVs) with hybrid electric vehicles (HEVs) aims to address the problem of optimizing energy economy while improving reliability and safety. By using intelligent driver support technologies and sophisticated control algorithms, this method aims to lower emissions and fuel consumption. Therefore, the HEVs develop an intelligent driver aid system that integrates an energy management system (EMS) with adaptive cruise control (ACC). To maximize energy use, the system integrates HEVs with autonomous vehicle technologies. In order to maintain safe distances from the lead vehicle, identify the desired acceleration, and switch between speed and distance control, the ACC uses a deep learning system in conjunction with a tilt integral derivative (TID) controller. This ensures stability. Based on ACC commands, the EMS controls energy usage. An enhanced manta ray foraging optimization (EMRFO) technique is used to significantly enhance speed control and energy economy. Simulink/MATLAB analysis of the suggested model shows notable gains in energy consumption control, particularly with the ACC with deep neural network (DNN) system, and considerable reductions in driving risks. In addition, the system precisely controls engine torque to maintain a 72.75 % State of Charge (SoC) and an average engine energy efficiency of 32.36 %. To validate the performance, the proposed approach is put into practice in real-time configuration. The configuration for the experiment produced an ideal efficiency of 31.99 % with minimal error using the proposed method. • This study aims to lower emissions and fuel consumption utilizing IDS. • HEVs developed which integrates EMS with adaptive cruise control (ACC). • Based on ACC commands, EMRFO is used to enhance speed control and energy economy. • Simulink/MATLAB analysis of the suggested model shows notable gains. • The experimental set-up produced an ideal efficiency of 31.99 % with minimal error. [ABSTRACT FROM AUTHOR]

Complex neural network structures may not be appropriate for adaptive control applications, as large number of variable adaptive parameters have to be updated in each time step, thus increasing the computational burden, which makes the real-time implementation of the controller difficult. In this study, the ability of the neural networks to approximate non-linear dynamic systems is used to derive a neuro-based adaptive control with minimalistic architecture. A linear neural identifier is devised, which emulates a local linear model of the system by online adjustment of its parameters. Stability and optimal rate of convergence is ensured through an adaptive learning rate, determined using Lyapunov stability theorems. The novelty of the control scheme lies in its minimalistic neural structure comprising of a single-linear neuron and, therefore, does not impose excessive computational burden on the system, making it feasible for real-time application. To assert our claims, benchmark examples from different domains are used to illustrate the effectiveness of the proposed controller. The neuro-controller is used in a water-lift plant to control the height of the water in a storage tank. Another example of a moving cart holding an inverted pendulum, an inherently unstable system that forms the basis of the robot-control mechanism, is also used. The controller is also tested on a complex non-linear higher-order power system to enhance stability by effectively damping the electromechanical oscillations. The superior performance of the controller is demonstrated by comparing with other recently reported controllers. Additional advantages of the proposed scheme include model-free control and requirement of only local measurements. The proposed method has potential applications in control problems that require adaptability, computational simplicity, and quick response. [ABSTRACT FROM AUTHOR]

In this article, a butterfly-like Prandtl–Ishlinskii (PI) hysteresis model and a novel neural network based adaptive implicit inverse control scheme to describe and control the butterfly-like hysteresis are proposed. The main contributions are: 1) a butterfly-like PI model is developed for the purpose of predicting the hysteresis effects and the model is feasible for controller design; 2) an implicit inverse control scheme especially for mitigating the butterfly-like hysteresis is implemented, which avoids the construction of the direct inverse of the butterfly-like PI model; 3) an adaptive implicit inverse control approach, which integrates the neural network and the implicit inverse technique into the output-feedback control is developed for eliminating the butterfly-like hysteresis and an arbitrarily small $L_{\infty }$ norm of tracking error is achieved. The proposed modeling and control methods are validated experimentally via the dielectric elastomer actuator based motion control platform. [ABSTRACT FROM AUTHOR]

This article presents an effective method to address the tracking control problem arising in uncertain switched high-order nonlinear system in strict-feedback form. The system under consideration contains unknown functions, which causally make the asymptotic tracking performance difficult to be achieved. By adopting the adding a power integrator approach in the framework of backstepping, a novel tracking controller is developed to guarantee an asymptotic tracking performance in the presence of the approximation error cased by neural networks (NNs) under arbitrary switching. The main contributions lie in: 1) the article for the first time embeds the backstepping technique in designing a kind of discontinuous controller with proportional integral (PI) compensation and 2) with the help of Filippov’s theory, a new defined system described by differential inclusions can be first obtained by taking some transformations, and then a novel nonsmooth Lyapunov function approach along with its upper right Dini derivative technique is applied to complete the construction of the discontinuous controller. Finally, two simulation examples are exhibited to verify the validity of the proposed design techniques. [ABSTRACT FROM AUTHOR]

This article presents an adaptive control framework for dynamically reconfigurable battery (DRB) systems based on the deep reinforcement learning method. The proposed adaptive control framework relies on deep Q-network to learn the DRB system operations. By utilizing its model-free nature, the proposed framework can significantly reduce the complexity of building experiences or expert models for DRB systems as well as improve battery operating time by ensuring cell balancing. Extensive simulation and experimental study has been carried out with data gathered from a real-world DRB testbed, and the results show the effectiveness and efficiency of the proposed control framework. [ABSTRACT FROM AUTHOR]

Compared with stochastic differential equations (SDEs) driven by white noise, random differential equations (RDEs) generated by colored noise are claimed to be more practical. This article considers reduced-order high-gain observer (ROHGO)-based neural tracking control on random nonlinear systems having output delay. In order to foster the design and analysis, the estimated states and the estimation errors are scaled by the high gain of the observer. Based on neural network (NN) approximation and state observation, an adaptive controller is designed for the overall system using the backstepping method. It is proved that all the closed-loop signals are bounded almost surely, letting alone the tracking error. By tuning the related design parameters, the asymptotic tracking error could be regulated arbitrarily small. Within the best of our knowledge, this article serves as the first attempt for NN-based control on RDE systems. Finally, the validity of main results is confirmed by a simulation example. [ABSTRACT FROM AUTHOR]

Artificial intelligence can bring convenience to human life. In the field of sports rehabilitation, the application of artificial intelligence is becoming more and more in-depth. This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. In the inner loop, a sliding mode iterative learning controller is designed to suppress periodic disturbance and abnormal jitter and achieve stable tracking of the target trajectory. Finally, the control method is simulated and verified by matlab/simulink, and a statistical experiment is done on the patient's recovery. The experimental results show that the use of artificial intelligence technology can effectively increase the sensitivity of the control system and improve the recovery rate of patients. Compared with the traditional sports rehabilitation control system, the sensitivity is increased by 22.7%, and the patient recovery rate is increased by 10.4%, which is of great significance in the field of sports rehabilitation. [ABSTRACT FROM AUTHOR]

This article considers the problem of adaptive prescribed performance tracking control via slow switching for a general class of uncertain switched nonlinear systems (SNSs) with unmodeled dynamics (UDs) and nonstrict-feedback structure. The UDs are not in their form of the input-to-state practical stability and their state information is unmeasurable. By reassigning the function variables, the coupling effects between UDs and the prescribed performance function are eliminated through the iterative process. In virtue of the neural networks (NNs) approximation capability, a novel adaptive backstepping procedure is proposed without adding extra first-order filters. By choosing an appropriate slow switching law, all the signals of the closed-loop system are bounded, and the system output tracks the reference signal with a prescribed performance level (PPL). [ABSTRACT FROM AUTHOR]

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, the novelty of this article lies in that it simultaneously considers delays (state and input delays) and input constraints (magnitude and rate constraints) for a class of uncertain nonlinear systems. In this article, the uncertain states and input delays are handled by integrating a constructed auxiliary system that functions as an observer with neural networks (NNs), with which the adverse effects caused by the uncertain states and input delays can be approximated and compensated. By involving smooth hyperbolic tangent functions in the designed auxiliary system, the problem of magnitude and rate constraints of the control input is fully addressed. Then, the backstepping technique runs through the entire control designing process, which allows the designed adaptive neural control strategy to handle the input constraints and delays at the same time. Furthermore, Nussbaum functions are employed to resolve the problem of unknown control directions. Due to the introduction of an input-driven filter, only the output of the system is required to be measured as the control feedback, which promotes the applicability of the designed controller. Under the proposed control scheme, semiglobal, uniform, and ultimate boundedness of all signals of the closed-loop system is realized with uncertain control directions, input and state delays, and guaranteed magnitude and rate constraints of control inputs. Finally, simulation results are illustrated to verify the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

In this article, an adaptive tracking control approach is developed for a class of strict-feedback nonlinear systems with time-varying full state constraints. As a breakthrough in this system, the special function constraints (whose constraint boundary is relevant to both state variables and time) are considered, which are rarely studied by research work. And there is no doubt that this method increases the complexity of designing this scheme. Furthermore, the time-varying integral barrier Lyapunov functions combining with backstepping technique is introduced to break the limitation of traditional methods as well as achieve the full state constraints. Meanwhile, fuzzy logic systems are selected to approximate unknown nonlinear functions. It is verified that all closed-loop signals are bounded and all states are forced in the time-varying boundness. In addition, the proposed control strategy has a good performance. The effectiveness of the theoretical analysis results is proved via a simulation example. [ABSTRACT FROM AUTHOR]

Teaching evaluation is a crucial measure to improve the quality of education. It is essential to formulate a scientific and reasonable evaluation system for teaching in colleges and universities. Teaching is a dynamic process, which makes it challenging to evaluate it comprehensively. However, as a new technology, the artificial neural network has the characteristics of nonlinear processing, adaptive learning, and high fault tolerance for various evaluation problems. This article first analyzes the current status of physical education and proposes solutions that comprehensively improve the quality of physical education in colleges and other institutions. It also suggests that a scientific evaluation of teaching in colleges provides a better feedback, incentives, and guidance, respectively. A model for the quality evaluation of physical education in colleges is established using artificial neural network. This model quantifies the concept of teaching evaluation index into actual data as its input, and teaching effect as output. The use of artificial neural network framework maps the input with output using an internally configured decision support system. Furthermore, it uses relevant software to conduct empirical research. The results show that this model enhances the quality of teaching evaluation, which not only overcomes the subjective factors of experts in the evaluation process but also produces satisfactory evaluation result. [ABSTRACT FROM AUTHOR]

Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, many systems have similar and well-understood dynamics, which suggests it is feasible to create a generalizable controller through meta-learning. In this work, we formulate a meta reinforcement learning (meta-RL) control strategy that can be used to tune proportional–integral controllers. Our meta-RL agent has a recurrent structure that accumulates "context" to learn a system's dynamics through a hidden state variable in closed-loop. This architecture enables the agent to automatically adapt to changes in the process dynamics. In tests reported here, the meta-RL agent was trained entirely offline on first order plus time delay systems, and produced excellent results on novel systems drawn from the same distribution of process dynamics used for training. A key design element is the ability to leverage model-based information offline during training in simulated environments while maintaining a model-free policy structure for interacting with novel processes where there is uncertainty regarding the true process dynamics. Meta-learning is a promising approach for constructing sample-efficient intelligent controllers. • An expert agent recognizes and controls any process in a large class. • Offline training leads to sample-efficient online operation and adaptation. • The online agent adapts automatically as plant characteristics drift. • The agent adjusts PI parameters instead of directly actuating the plant. • meta-RL design generalizes to various plant and controller structures. [ABSTRACT FROM AUTHOR]

In this brief, fixed-time synchronization problem for inertial memristive neural networks (IMNNs) with impulsive and adaptive control is investigated. Instead of modeling the memristor as a right-hand discontinuous system, memristor is regarded as an uncertain continuous time-varying parameter, memristive neural networks (MNNs) is modeled as a neural network (NNs) with polytopic uncertainty and time varying parameters. By establishing comparison system, the criteria are established for synchronization of IMNNs in a setting time with impulsive and adaptive control input. Based on convex combination method, the influence of different impulsive effects on synchronization behavior of the system is analyzed by dividing the impulsive interval. Finally, numerical examples are given for illustration. [ABSTRACT FROM AUTHOR]

Wheelchair-mounted robotic arms support people with upper extremity disabilities with various activities of daily living (ADL). However, the associated cost and the power consumption of responsive and adaptive assistive robotic arms contribute to the fact that such systems are in limited use. Neuromorphic spiking neural networks can be used for a real-time machine learningdriven control of robots, providing an energy efficient framework for adaptive control. In this work, we demonstrate a neuromorphic adaptive control of a wheelchair-mounted robotic arm deployed on Intel's Loihi chip. Our algorithm design uses neuromorphically represented and integrated velocity readings to derive the arm's current state. The proposed controller provides the robotic arm with adaptive signals, guiding its motion while accounting for kinematic changes in real-time. We pilot-tested the device with an able-bodied participant to evaluate its accuracy while performing ADLrelated trajectories. We further demonstrated the capacity of the controller to compensate for unexpected inertia-generating payloads using online learning. Videotaped recordings of ADL tasks performed by the robot were viewed by caregivers; data summarizing their feedback on the user experience and the potential benefit of the system is reported. [ABSTRACT FROM AUTHOR]

This paper presents an artificial neural network-based adaptive control approach for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The control objectives are: (1) extraction of maximum available power from the wind; (2) stator reactive power regulation according to the grid requirements. Artificial neural networks are used to estimate some nonlinear functions which represent the system uncertainties. The Lyapunov method is employed to prove the asymptotic stability of the closed-loop system. Numerical simulation results illustrate the effectiveness of the proposed control scheme in comparison with both vector control and sliding mode control techniques. • An artificial neural network based adaptive control approach for a DFIG based wind energy conversion system is presented. • The artificial neural networks are used to online estimate the system uncertainties. • A Lyapunov approach is used to prove the asymptotic stability of the closed-loop system. • A comparative study with field oriented vector control and sliding mode control is conducted. [ABSTRACT FROM AUTHOR]

In this article, the model‐based event‐triggered neural learning control problem is investigated for a class of discrete‐time strict‐feedback systems. First, with the case of sufficient network resources, an adaptive neural controller is designed with a new neural weight update law, which avoids the n$$ n $$‐step delay of traditional adaptive neural control methods. The proposed controller performs the tracking control task while ensuring that the radial basis function neural network can accurately identify the unknown system nonlinear dynamics and the estimated neural weights can converge to their ideal values. Second, with the case of limited network resources, the convergent neural weights can be reused to construct the neural network model, event‐trigger conditions, and the neural learning controller, which can improve the tracking control performance and save the communication resources. Especially, compared with the traditional event‐triggered adaptive neural controller, the neural learning controller can reduce the burden of online calculations since the constant neural weights are used to construct the neural network model and trigger conditions. For the proposed control scheme, simulation results are given to verify its effectiveness. [ABSTRACT FROM AUTHOR]

A recurrent wavelet first-order neural network (RWFONN) is proposed to select a desired attractor in a multistable erbium-doped fiber laser (EDFL). A filtered error algorithm is used to classify coexisting EDFL states and train RWFONN. The design of the intracavity laser power controller is developed according to the RWFONN states with the block control linearization technique and the super-twisting control algorithm. Closed-loop stability analysis is performed using the boundedness of synaptic weights. The efficiency of the control method is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

This article develops a self-triggered adaptive neural network (NN) tracking controller for a class of continuous-time nonlinear systems, that is, input constrained and with unknown drift and input dynamics. Since the drift and input dynamics are both unknown, an NN is built within a self-triggered update paradigm to approximate the unknown tracking control. The error derivative used in the weight update algorithm is derived using a robust exact differentiator technique. To address input constraints, an auxiliary compensator is designed for the unimplemented control effort. Through rigorous Lyapunov analyses, we can guarantee that all the tracking and weight errors are uniformly ultimately bounded. Finally, to show the effectiveness of the proposed control performance, simulation results of a two-link robot are provided and analyzed. [ABSTRACT FROM AUTHOR]

With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people's evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect. [ABSTRACT FROM AUTHOR]

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform the adaptive filtering techniques and algorithms normally used in adaptive control, especially when in nonlinear plants. The deeper the controller, the better the inverse function approximation, provided that the nonlinear plant has an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to nonlinear plant parameter changes in that the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. The settling and rise times of the step response are shown to improve in the DL-based AIC system. [ABSTRACT FROM AUTHOR]

Since the variable-order fractional systems show more complex characteristics and more degrees of freedom due to time-varying fractional derivatives, we introduce a variable-order fractional Hopfield-like neural network in this paper. First, the properties and dynamical behavior of the system are studied. The variable-order derivative's effects on the system's behavior are investigated through the Lyapunov exponents and bifurcation diagram; an emerging Feigenbaum tree of period-4 bubble is observed, which appears with the creation and annihilation of periodic orbits. A general basin of attraction for the fractional-order neural network is presented, demonstrating that its dynamical behaviors are extremely sensitive to initial conditions resulting in different periodic orbits and chaotic attractors' coexistence. After that, an adaptive control scheme is proposed for the variable-order fractional system. Through Lyapunov theorem and Barbalat's Lemma, the system's convergence and stability under the proposed control scheme are proven. The main advantages of the proposed controller are its guaranteed stability, robustness against uncertainties, and simplicity. Finally, the synchronization results are presented. Numerical simulations show the excellent performance of the proposed controller for the variable-order fractional Hopfield-like neural network. [ABSTRACT FROM AUTHOR]

This article investigates optimal control problem for a class of discrete-time (DT) nonlinear systems with unknown dynamics. With the help of a broad learning system (BLS), a novel online adaptive dynamic programming (ADP) controller is presented. First, to approximate the unknown system dynamics, an approximator based on BLS is presented. The connection weights are calculated by the data of the system by using the ridge regression algorithm. Then, two BLSs are adopted to approximate the optimal cost function and optimal control law, respectively. The connection weights of these two BLSs are updated using the given weights tuning law at each sampling instant. The proposed optimal controller is proved to ensure that all the system states and estimation errors are uniform ultimate bounded. Finally, simulation examples are carried out to further demonstrate the effectiveness of the proposed BLS-based approximator and optimal controller. [ABSTRACT FROM AUTHOR]

Summary: This article focuses on the adaptive asymptotic learning tracking control problem of nonlinear systems with full state constraints. First, an adaptive neural network tracking algorithm is proposed which combines a time‐varying feedback element and robust compensation feedback element in the form of smooth function to guarantee that output signal tracks the reference signal asymptotically. Apart from this, a time‐varying integral barrier Lyapunov function is utilized to ensure that the system states are always kept in the constraint region. Furthermore, the event‐triggered mechanism is designed to efficiently reduce unnecessary transmissions. Compared with the other literatures, the original state constraint problem is directly solved by the proposed adaptive control algorithm. Finally, simulation is included to validate the built theoretical results. [ABSTRACT FROM AUTHOR]

This manuscript suggests an advanced high-step-up-gain Luo converter-fed brushless DC motor (BLDCM) drive for enhancing the power quality by using artificial neural network (ANN)-based model reference adaptive control algorithm. To overcome these issues, designing a competent intelligent controller by using an effective design approach is essential to utilize the brushless DC motor (DCM) to its maximum potential. The model reference adaptive controller system has a parameter modification tool added with the regular feedback loop which accord better results when there are modifications in process parameters. The ANN is significant tool for reckoning and controlling system, with the key factors of intrinsic parallelism, fault tolerance and learning fitness. To address the issue of adapting the nonlinearity, parameters and loads in BLDC motor drive, an integration of the MRAS with ANN features is employed. The proposed adaptive system-based speed control for BLDC motor drive system effectiveness is compared with existing PID-based speed control strategy by utilizing the MATLAB Simulink platform. However, the obtained result highlights the proposed model reference adaptive control (MRAC)-based model in optimizing BLDC motor drives for applications requiring high precision and adaptability. [ABSTRACT FROM AUTHOR]