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This article presents a comprehensive survey of the integration of machine learning techniques into robotic grasping, with a special emphasis on the challenges and advancements for space applications. The incorporation of artificial intelligence, particularly through deep learning, reinforcement learning, transfer learning, convolutional neural networks and recurrent neural networks, has significantly revolutionized robotic grasping. These advancements facilitate autonomous, efficient, and sophisticated manipulation in the challenging environment of outer space, transitioning from traditional mechanical grippers to sophisticated systems powered by advanced algorithms. This transition highlights the critical integration of sensory perception, grasp planning, and execution mechanisms, enhancing robots' capabilities to perceive, interact with, and manipulate objects with unprecedented precision and adaptability. The article meticulously outlines significant advancements achieved through the deployment of convolutional neural networks for visual information processing, RNNs for sequential decision-making, RL for autonomous strategy refinement, and transfer learning for leveraging pre-learned knowledge in novel tasks. These technologies address the unique challenges of space environments, such as varied textures, occlusions, microgravity conditions, and the sim-to-real gap, by enhancing sample efficiency, improving sim-to-real transfer capabilities, and integrating multimodal data for better object localization and pose estimation. Furthermore, the review explores the specific challenges faced in space robotic grasping, including handling varied textures and occlusions, adapting to unpredictable conditions, achieving real-time processing, and ensuring safety and reliability. It proposes future research directions focused on overcoming these hurdles, such as enhanced generalization through multimodal learning, robust sim-to-real transfer techniques, and the development of collaborative robotics and swarm intelligence. Critical to the development of ML models for robotic grasping are the roles of specialized datasets and simulation environments. Datasets like the Cornell Grasping Dataset and the Yale-CMU-Berkeley Object, along with simulation platforms such as Gazebo and PyBullet, provide essential resources for training, testing, and refining ML models. These tools enable researchers to simulate complex robotic systems and interactions within realistic environments, fostering rapid iterations on design and control strategies. In summary, this article offers in-depth insights into the progress, current challenges, and future prospects of machine learning techniques in robotic grasping for space exploration. It showcases significant strides made in the field and charts a path forward, emphasizing the need for innovative solutions to navigate the complexities of robotic manipulation in outer space. Through the strategic integration of advanced ML techniques, the development of adaptable and efficient robotic systems for space applications continues to advance, promising to unlock new possibilities in space exploration and beyond. • Surveys machine learning's application in space robotic grasping, noting key advances. • Highlights CNNs and RNNs' roles in robotic perception and action. • Discusses deep learning's evolution in autonomous space robotics. • Outlines future directions, emphasizing sim-to-real and multimodal learning. [ABSTRACT FROM AUTHOR]

Advancing cutting-edge techniques to accurately classify electromyography (EMG) signals are of paramount importance given their extensive implications and uses. While recent studies in the literature present promising findings, a significant potential still exists for substantial enhancement. Motivated by this need, our current paper introduces a novel ensemble neural network approach for time series classification, specifically focusing on the classification of upper limb EMG signals. Our proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, leveraging their capabilities to achieve accurate classification. We provide a thorough explanation of the architecture and methodology, considering the unique characteristics and challenges posed by EMG signals. Furthermore, we outline the preprocessing steps employed to transform raw EMG signals into a suitable format for classification. To evaluate the effectiveness of our proposed technique, we compare its performance with a baseline LSTM classifier. The obtained numerical results demonstrate the superiority of our method. Remarkably, the method we propose attains an average accuracy of 91.5%, with all motion classifications surpassing the 90% threshold. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM's exceptional sequential data learning capability and CNN's intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare. [ABSTRACT FROM AUTHOR]

The control of rehabilitation robots presents a formidable challenge owing to the myriad of uncharted disturbances encountered in real-world applications. Despite the existence of several techniques proposed for controlling and identifying such systems, many cutting-edge approaches have yet to be implemented in the context of rehabilitation robots. This highlights the necessity for further investigation and exploration in this field. In light of this motivation, we introduce a pioneering algorithm that employs a finite estimator and Gaussian process to identify and forecast the uncharted dynamics of a 2-DoF knee rehabilitation robot. The proposed algorithm harnesses the probabilistic nature of Gaussian processes, while also guaranteeing finite-time convergence through the utilization of the Lyapunov theorem. This dual advantage allows for the effective exploitation of the Gaussian process's probabilistic capabilities while ensuring reliable and timely convergence of the algorithm. The algorithm is delineated and the finite time convergence is proven. Subsequently, its performance is investigated through numerical simulations for estimating complex unknown and time-varying dynamics. The results obtained from the proposed algorithm are then employed for controlling the rehabilitation robot, highlighting its remarkable capability to provide precise estimates while effectively handling uncertainty. [ABSTRACT FROM AUTHOR]

It is well known that the permanent magnet synchronous motor (PMSM) exhibits chaotic characteristics when its parameters fall within a certain range, which can lead to system instability. This article proposes an adaptive control strategy for achieving the fixed-time chaotic stabilization of PMSM, even in the presence of unknown parameters and perturbations. The developed controller is synthesized by combining a parametric adaptive mechanism with a fixed-time control technique. The stability analysis demonstrates that the system states under the developed controller can converge to small neighborhoods around the equilibrium point within a fixed time. Thanks to the adoption of the parametric adaptive mechanism, the developed controller is not only insensitive to unknown parameters but also robust against perturbations. Finally, simulated studies are conducted to verify and emphasize the effectiveness of the developed control strategy. [ABSTRACT FROM AUTHOR]

This article presents an indirect neural-based finite-time integral sliding mode control algorithm for the reference trajectory tracking guidance of Mars entry vehicle under uncertainties. The proposed controller is developed as a combination of finite-time integral sliding mode controller and indirect neural identification. The finite-time integral sliding mode controller is designed by constructing a new type of finite-time integral sliding mode surface to prevent the singularity problem. Moreover, the neural network (NN) is combined with the finite-time integral sliding mode controller to identify the lumped uncertainty and attenuate the chattering phenomenon. Particularly, the concept of indirect neural identification is adopted and only a single adaptive parameter is required to be learned online. In this way, the proposed controller is not only strongly robust against aerodynamic and density uncertainties, but also computationally simple for onboard implementations. Stability argument indicates that the proposed controller can ensure the radial distance tracking error and its time differentiation regulate to the small residual sets around zero in finite time. Lastly, the effectiveness and excellent guidance performance of the proposed control algorithm are demonstrated through simulation studies on a Mars Science Laboratory-type (MSL-type) entry vehicle. [ABSTRACT FROM AUTHOR]

In this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the help of neural network (NN) and tan-type barrier Lyapunov function (BLF). The NN is introduced to identify the unknown part in the dynamic model of the space manipulator. Benefiting from the neural identification, the proposed controller is model-free and insensitive to external perturbations. Moreover, the BLF is adopted to guarantee the position tracking errors never exceed the predefined output constraints. Different from log-type BLF, the tan-type BLF is employed for the control design, which makes the proposed controller universal for the cases with and without considering the output constraints. The semiglobal uniform ultimate boundedness of the resulting closed-loop system is strictly obtained through stability argument. All error variables in the closed-loop system can eventually stabilize to the small residual sets about zero under the proposed controller. Lastly, simulations and comparisons are given to demonstrate the effectiveness and excellent tracking performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are subject to complex aerodynamic or hydrodynamic forces and various uncertainties, making their dynamic modeling difficult. Hence, in these systems, it is preferred to apply control techniques whose closed-loop systems can predict the system's uncertain dynamics and provide acceptable performance. To this end, this paper exploits a new neural network-based optimal mixed H 2 / H ∞ control for a modified unmanned aerial vehicle to accomplish trajectory tracking missions. Firstly, the dynamic model of the modified unmanned aerial vehicles is presented. Then, the design procedure of the new controller is delineated. In this approach, H ∞ attenuates the effect of uncertainties and through H 2 the consumed control energy is minimized. Similar to real-world applications, it is supposed that there are control input constraints and external disturbances. The neural networks are also employed to estimate all uncertainties. The numerical simulations for two different desired paths are demonstrated. Simulation results illustrate the efficient performance of the proposed control technique in the presence of external disturbances, dynamic uncertainties, and control input constraints. [ABSTRACT FROM AUTHOR]

Modern requirements dictate the need for sustainable transportation systems, given the substantial growth in transportation activities over recent years that is predicted to persist. This surge in transportation has brought about environmental concerns such as air pollution and noise. To deal with this crisis, municipal administrations are investing in sustainable, reliable, economical, and environmentally friendly transportation systems. This review examines the latest developments in fuzzy decision systems for sustainable transport supplements. By reviewing the literature, we assess the serviceability of the entire supply chain to maintain transport quality, remove degradation, and meet customer demands. The link between fuzzy decision systems and supply chain serviceability may not be immediately obvious, but there are many reasons why putting them together can be a valuable focus for companies. By leveraging the capabilities of fuzzy decision systems to optimize supply chain processes and improve service levels, companies can gain a competitive advantage and better meet customer demand. [ABSTRACT FROM AUTHOR]

The challenging problem of attitude tracking control for unmanned space vehicles (USVs) subject to actuator faults and output constraints is addressed in this study. A novel adaptive neural fault-tolerant controller is proposed by integrating the neural networks (NNs) and barrier Lyapunov function (BLF) with the backstepping technique. Two NNs are adopted to approximate the uncertain nonlinear terms caused by unknown attitude dynamics and actuator faults, respectively. Moreover, the BLF is introduced to tackle the output constraints. It is strictly proved that all the closed-loop error signals are uniformly ultimately bounded under the proposed controller. Totally, the proposed adaptive neural fault-tolerant controller has the following two distinctive features. (1) The proposed controller is model-free and can still be applicable even when the USV attitude dynamic model is completely unknown in advance. (2) The proposed controller can guarantee the attitude tracking error always within the predefined output constraints even in the presence of actuator faults and thus ensuring safety. Finally, the excellent tracking performance of the proposed controller is verified through numerical simulations and comparisons. [ABSTRACT FROM AUTHOR]

This article addresses trajectory tracking control of unmanned surface vessels (USVs) subject to position and velocity measurement biases. Unlike model uncertainties and external disturbances, measurement biases can lead to mismatched disturbances in system kinematics, rendering great difficulty to the USV control system design. To overcome this problem, a disturbance attenuation controller was recursively synthesized by incorporating two disturbance observers into the backstepping control design. The stability argument shows that all error signals in the closed-loop system can regulate to the small neighborhoods about the origin. The proposed controller has two remarkable features: (1) By adopting two disturbance observers to estimate the mismatched and matched lumped disturbances, the proposed controller is robust against model uncertainties and external disturbances and insensitive to measurement biases. (2) Meanwhile, the proposed controller is structurally simple and user friendly. Lastly, comparative simulations were conducted to validate the obtained results. [ABSTRACT FROM AUTHOR]

The theory of critical curves determines the main characteristics of a discrete dynamical system in two dimensions. One important property that has garnered recent attention is the problem of chaos synchronization, along with the location of its chaotic attractors, basin boundaries, and bifurcation mechanisms. Varying the parameters of the maps reveals the instrumental role that these curves play, where the bifurcation leads to complex topological structures of the basins occurs by contact with the basin boundaries, resulting in the appearance or disappearance of some components of the basin. This study focuses on the properties of a discrete dynamical system consisting of two symmetrically coupled non-invertible maps, specifically those with an invariant one-dimensional submanifold (or one-dimensional maps). These maps exhibit a complex structure of basins with the coexistence of symmetric chaotic attractors, riddled basins, blow-out, on-off intermittency, and, most significantly, the appearance of chaotic synchronization with a correlation between all the characteristics. The numerical method of critical curves can be used to demonstrate a wide range of dynamic scenarios and explain the bifurcations that lead to their occurrence. These curves play a crucial role in a system of two symmetrically coupled maps, and their significance will be discussed. [ABSTRACT FROM AUTHOR]

Proper control is necessary for ensuring that UAVs successfully navigate their surroundings and accomplish their intended tasks. Undoubtedly, a perfect control technique can significantly improve the performance and reliability of UAVs in a wide range of applications. Motivated by this, in the current paper, a new data-driven-based fractional-order control technique is proposed to address this issue and enable UAVs to track desired trajectories despite the presence of external disturbances and uncertainties. The control approach combines a deep neural network with a robust fractional-order controller to estimate uncertainties and minimize the impact of unknown disturbances. The design procedure for the controller is outlined in the paper. To evaluate the proposed technique, numerical simulations are performed for two different desired paths. The results show that the control method performs well in the presence of dynamic uncertainties and control input constraints, making it a promising approach for enabling UAVs to track desired trajectories in challenging environments. [ABSTRACT FROM AUTHOR]

The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia–Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786. [ABSTRACT FROM AUTHOR]

This article addresses the challenging problem of fixed-time output-constrained synchronization for master–slave chaotic financial systems with unknown parameters and perturbations. A fixed-time neural adaptive control approach is originally proposed with the aid of the barrier Lyapunov function (BLF) and neural network (NN) identification. The BLF is introduced to preserve the synchronization errors always within the predefined output constraints. The NN is adopted to identify the compound unknown item in the synchronization error system. Unlike the conventional NN identification, the concept of indirect NN identification is employed, and only a single adaptive learning parameter is required to be adjusted online. According to the stability argument, the proposed controller can ensure that all error variables in the closed-loop system regulate to the minor residual sets around zero in fixed time. Finally, simulations and comparisons are conducted to verify the efficiency and benefits of the proposed control strategy. It can be concluded from the simulation results that the proposed fixed-time neural adaptive controller is capable of achieving better synchronization performance than the compared linear feedback controller. [ABSTRACT FROM AUTHOR]

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach. [ABSTRACT FROM AUTHOR]

After decades of evolution and advancement, artificial intelligence has profoundly affected all fields of science and engineering, and has started to revolutionize all aspects of today's life. In this regard, neural networks, which are a stepping stone in the search for artificial intelligence, play an important role. Motivated by this, the current special issue aims to explore recent trends and developments in the modeling, analysis, synchronization, and practical application of chaotic variable-order fractional neural networks. [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]

Mathematical modeling can be utilized to find out how the coronavirus spreads within a population. Hence, considering models that can precisely describe natural phenomena is of crucial necessity. Besides, although one of the most significant benefits of mathematical modeling is designing optimal policies for battling the disease, there are a few studies that employ this beneficial aspect. To this end, this study aims to design optimal management policies for the novel coronavirus disease 2019 (COVID-19). This is a pioneering research that designs optimal policies based on multi-objective evolutionary algorithms for control of the fractional-order model of the COVID-19 outbreak. First, a fractional-order model of the disease dynamic is presented. The impacts of the fractional derivative's value on the modeling and forecasting of the disease spread are considered. After that, a multi-objective optimization problem is proposed by considering the rate of communication, the transition of symptomatic infected class to the quarantined one, and the release of quarantined uninfected individuals. Numerical results clearly corroborate that by solving the proposed multi-objective problem, governments can control the massive disease outbreak while economic factors have reasonable values that prevent economic collapse. [ABSTRACT FROM AUTHOR]

• A novel model-based clustering approach for sequential data, nTreeClus, is proposed. • nTreeClus introduces a Decision Tree Path encoder in an autoregressive manner. • The method's robustness to its only parameter (window size) has been examined. • nTreeClus shows competitive results compared to existing methods in sequence mining. The overwhelming presence of categorical/sequential data in diverse domains emphasizes the importance of sequence mining. The challenging nature of sequences proves the need for continuing research to find a more accurate and faster approach providing a better understanding of their (dis) similarities. This paper proposes a new Model-based approach for clustering sequence data, namely nTreeClus. The proposed method deploys Tree-based Learners, k -mers, and autoregressive models for categorical time series, culminating with a novel numerical representation of the categorical sequences. Adopting this new representation, we cluster sequences, considering the inherent patterns in categorical time series. Accordingly, the model showed robustness to its parameter. Under different simulated scenarios, nTreeClus improved the baseline methods for various internal and external cluster validation metrics for up to 10.7% and 2.7%, respectively. The empirical evaluation using synthetic and real datasets, protein sequences, and categorical time series showed that nTreeClus is competitive or superior to most state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this article, a neural integral sliding mode control strategy is presented for the finite-time fault-tolerant attitude tracking of rigid spacecraft subject to unknown inertia and disturbances. First, an integral sliding mode controller was developed by originally constructing a novel integral sliding mode surface to avoid the singularity problem. Then, the neural network (NN) was embedded into the integral sliding mode controller to compensate the lumped uncertainty and replace the robust switching term. In this way, the chattering phenomenon was significantly suppressed. Particularly, the mechanism of indirect neural approximation was introduced through inequality relaxation. Benefiting from this design, only a single learning parameter was required to be adjusted online, and the computation burden of the proposed controller was extremely reduced. The stability argument showed that the proposed controller could guarantee that the attitude and angular velocity tracking errors were regulated to the minor residual sets around zero in a finite time. It was noteworthy that the proposed controller was not only strongly robust against unknown inertia and disturbances, but also highly insensitive to actuator faults. Finally, the effectiveness and advantages of the proposed control strategy were validated using simulations and comparisons. [ABSTRACT FROM AUTHOR]

Nowadays, advances in different fields of technology have increased demands for reliable controllers. Uncertainty and disturbances, which are inevitable in most real‐world systems, as well as increasing complexity in the dynamics of many systems, are requiring the design and application of intelligent controllers. Fuzzy controllers, and specifically type‐2 fuzzy control techniques, could play a beneficial role in a variety of control purposes, since they are robust against uncertainties. Therefore, an immense body of research is devoted to these fuzzy control techniques. This paper introduces a comprehensive review about the most recent advances in the design and implementation of type‐2 fuzzy control schemes both for integer‐ and fractional‐order systems. Thus, in addition to the past and present achievements in this context, future trends are also delineated. Moreover, most important works dealing with type‐2 fuzzy controller are discussed, and their advantages and drawbacks are outlined. Consequently, we firmly believe the present paper will pave the way for future research in this area. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural adaptive fixed-time control approach for the attitude stabilization and vibration suppression of flexible spacecraft. First, the neural network (NN) was introduced to identify the lumped unknown term involving uncertain inertia, external disturbance, torque saturation, and elastic vibrations. Then, the proposed controller was synthesized by embedding the NN compensation into the fixed-time backstepping control framework. Lyapunov analysis showed that the proposed controller guaranteed the stabilization of attitude and angular velocity to the adjustable small neighborhoods of zero in fixed time. The proposed controller is not only robust against uncertain inertia and external disturbance, but also insensitive to elastic vibrations of the flexible appendages. At last, the excellent stabilization performance and good vibration suppression capability of the proposed control approach were verified through simulations and detailed comparisons. [ABSTRACT FROM AUTHOR]

This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system's behavior and its dynamical feature are shown. Then, tracking control and stabilization of the systems are studied. As it is obvious, the existence of faults and failures in the process of control of financial and economic systems is undeniable — this issue necessitates applying proper control techniques for the systems. So as to achieve appropriate results in the control of fractional financial risk system, two finite-time fault-tolerant controllers are proposed, namely, finite-time active fault-tolerant control and finite-time passive fault-tolerant control. Not only do these techniques force the system to reach desired values in finite time, but also, the proposed techniques are robust against uncertainties, faults, and failures in actuators. Through finite-time observers, the effects of all uncertainties are taken to account in the active controller. Finally, numerical simulations of tracking control and stabilization are presented. For numerical simulations, the fractional financial risk system is considered to be in the presence of unknown disturbance as well as faults and failures in actuators. It is assumed that the system is in the presence of various types of actuator faults. Numerical results affirm the ability of the offered control techniques for pushing the states of the fractional-order risk system to the desired value in a short period of time. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stabilization and tracking control of a hyperchaotic memristor oscillator using a novel disturbance-observer-based integral terminal sliding mode control. Firstly, the dynamic behaviours of the hyperchaotic memristor oscillator are investigated. Afterward, the fast disturbance-observer is combined with integral terminal sliding mode control for multi-input-multi-output (MIMO) nonlinear relative degree one systems in presences of external disturbance, unknown input saturation, and control singularity. The Lyapunov stability theorem is implemented to prove the finite time convergence of the closed-loop system. Furthermore, to specify the parameters of the proposed controller, the genetic algorithm optimization is applied to decrease chattering of the system's response. Simulation results are presented to show the appropriate performance of the proposed control scheme in terms of stabilization and tracking control in the presence of external disturbances and input saturation. [ABSTRACT FROM AUTHOR]

In this paper, the convergence guaranteed attitude stabilization of underactuated spacecraft using two control torques is investigated. By introducing the (w , z) parameters to describe the attitude motion of the underactuated spacecraft, a unified integrated attitude kinematic and dynamic model is originally derived, based on which the controllers can be directly designed for the two actuated axes. Then, three types of controllers are designed to stabilize the attitude and angular velocity along the two actuated axes to zero in fixed time. After that, it is strictly evaluated that the attitude and angular velocity along the underactuated axis can eventually stabilize to the constant values. In this way, the convergence property of the designed controllers can be guaranteed. The utilized controllers are the fixed-time proportional-differential (PD)-like controller, the fixed-time nonsingular terminal sliding mode (NTSM) controller, and the fixed-time integral sliding mode (ISM) controller, respectively. Among them, the fixed-time ISM controller is best since it is strongly robust against disturbances and has no singularity problem naturally, which overcomes the drawbacks of the fixed-time PD-like controller and the fixed-time NTSM controller simultaneously. Lastly, simulated studies are executed to examine the derived results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate some unrevealed features of a newly introduced megastable chaotic oscillator. This oscillator has a rich dynamical behavior including limit cycle, torus and strange attractor. Also it has coexisting self excited and hidden attractors. Such multi-stable oscillator with coexisting self excited and hidden attractors is very rare in the literature. We have studied the oscillator dynamics using an analog circuit. Also a novel fuzzy-based robust and adaptive control method is designed to control this oscillator. Highlights • In this paper, we investigate some unrevealed features of a newly introduced megastable chaotic oscillator. • This oscillator has a rich dynamical behavior including limit cycle, torus and strange attractor. • Also it has coexisting self excited and hidden attractors. • We have studied the oscillator dynamics using an analog circuit. • In addition to, a novel fuzzy-based robust and adaptive control method is designed to control this oscillator. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive controller subject to decoupled sliding mode controller as a supervisory controller has been implemented on the HIV infection dynamic model. A five-state dynamic model of HIV is utilized which the measurement of the CD4+T cells and the viral load counts are necessary to estimate all its parameters. Decoupled sliding mode control is a variable structure controller having significant and appropriate features, such as best tracking and regulation performance and robustness and elevate the performance of the controller. Generally, due to the importance of applied treatment strategy to mitigate viral escape, sliding mode control is utilized in accordance with PI control to deliver necessary control inputs. To achieve the least possible chattering, effectual methods such as the transfer function is used. To update the gains of PI control, an adaptation law is then employed. The results demonstrate the suitable performance of the controller via providing proper tracking performance, and also, elimination of the chattering problem and decrease the time of treatment. The number of infected CD4+ T-cells and the number of free virus particles can be controlled in less than five days. The proposed controller is capable of controlling the dynamic behavior of the virus concentration for different patients with the same control scheme. [ABSTRACT FROM AUTHOR]

Abstract Chaotic and hyperchaotic systems with interesting behaviors have been of interest in the recent years. In this paper we introduce a hyperchaotic memristor oscillator which shows both stable and unstable fixed points. Such systems have been less investigated in the literatures. It should also to be noted that the proposed hyperchaotic system has only one nonlinearity which is a flux controlled memristor. Depending on the control parameter, the system displays different behaviors and coexisting attractors. A linear quadratic regulator based synchronization of the proposed hyperchaotic system is designed and numerically analyzed. We also design such a fuzzy based PID controller for chaos control in such a memristor oscillator. [ABSTRACT FROM AUTHOR]

The neural network activation functions enable neural networks to have stronger fitting abilities and richer dynamical behaviors. In this paper, an improved fractional-order discrete tabu learning neuron (FODTLN) model map with a nonlinear periodic function as the activation function is proposed. The fixed points of the map are discussed. Then, the rich and complex dynamical behaviors of the map under different parameters and order conditions are investigated by using some common nonlinear dynamical analysis methods combined with the fractional-order approximate entropy method. Furthermore, it is found that fractional-order differential operators affect the generation of multiscrolls, and the model has infinitely many coexisting attractors obtained by changing the initial conditions. Interestingly, attractor growth and state transition are found. Finally, the map is implemented on the DSP hardware platforms to verify the realizability. The results show that the map exhibits complex and interesting dynamical behaviors. It provides a fundamental theory for the research of artificial neural networks. [ABSTRACT FROM AUTHOR]

We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day. A courier's duty is to pick up an order from a restaurant and deliver it to a customer. We model this service as a Markov decision process and use deep reinforcement learning as the solution approach. We experiment with the resulting policies on synthetic and real-world datasets and compare those with the baseline policies. We also examine the courier utilization for different numbers of couriers. In our analysis, we specifically focus on the impact of the limited available resources in the meal delivery problem. Furthermore, we investigate the effect of intelligent order rejection and re-positioning of the couriers. Our numerical experiments show that, by incorporating the geographical locations of the restaurants, customers, and the depot, our model significantly improves the overall service quality as characterized by the expected total reward and the delivery times. Our results present valuable insights on both the courier assignment process and the optimal number of couriers for different order frequencies on a given day. The proposed model also shows a robust performance under a variety of scenarios for real-world implementation. • Our MDP model for the courier assignment task characterizes on-demand meal delivery service. • We tailor deep reinforcement learning algorithms to address the problem in a dynamic environment. • We incorporate the notion of order rejection to reduce the number of late orders. • We investigate the importance of intelligent repositioning of the couriers during their idle times. [ABSTRACT FROM AUTHOR]

Due to the vital role of financial systems in today's sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system. [ABSTRACT FROM AUTHOR]

Over the last years, distributed consensus tracking control has received a lot of attention due to its benefits, such as low operational costs, high resilience, flexible scalability, and so on. However, control methods that do not consider faults in actuators and control agents are impractical in most systems. There is no research in the literature investigating the consensus tracking of supply chain networks subject to disturbances and faults in control input. Motivated by this, the current research studies the fault-tolerant, finite-time, and smooth consensus tracking problems for chaotic multi-agent supply chain networks subject to disturbances, uncertainties, and faults in actuators. The chaotic attractors of a supply chain network are shown, and its corresponding multi-agent system is presented. A new control technique is then proposed, which is suitable for distributed consensus tracking of nonlinear uncertain systems. In the proposed scheme, the effects of faults in control actuators and robustness against unknown time-varying disturbances are taken into account. The proposed technique also uses a finite-time super-twisting algorithm that avoids chattering in the system's response and control input. Lastly, the multi-agent system is considered in the presence of disturbances and actuator faults, and the proposed scheme's excellent performance is displayed through numerical simulations. [ABSTRACT FROM AUTHOR]

Control of supply chains with chaotic dynamics is an important, yet daunting challenge because of the limitations and constraints there are in the amplitude of control efforts. In real-world systems, applying control techniques that need a large amplitude signal is impractical. In the literature, there is no study that considers the control of supply chain systems subject to control input limitations. To this end, in the current study, a new control scheme is proposed to tackle this issue. In the designed control input, limitations in control inputs, as well as robustness against uncertainties, are taken into account. The proposed scheme is equipped with a fixed time disturbance observer to eliminate the destructive effects of uncertainties and disturbances. Additionally, the super-twisting sliding mode technique guarantees the fixed-time convergence of the closed-loop system. After that, a symmetric supply chain system is presented, and its chaotic attractors are demonstrated. Finally, the proposed controller is applied to the symmetric supply chain system. Numerical simulations exhibit the proposed scheme's excellent performance even though the system is subjected to control input limitations and time-varying uncertainties. [ABSTRACT FROM AUTHOR]

Although most of the early research studies on fractional-order systems were based on the Caputo or Riemann–Liouville fractional-order derivatives, it has recently been proven that these methods have some drawbacks. For instance, kernels of these methods have a singularity that occurs at the endpoint of an interval of definition. Thus, to overcome this issue, several new definitions of fractional derivatives have been introduced. The Caputo–Fabrizio fractional order is one of these nonsingular definitions. This paper is concerned with the analyses and design of an optimal control strategy for a Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic. The Caputo–Fabrizio fractional-order model of HIV/AIDS is considered to prevent the singularity problem, which is a real concern in the modeling of real-world systems and phenomena. Firstly, in order to find out how the population of each compartment can be controlled, sensitivity analyses were conducted. Based on the sensitivity analyses, the most effective agents in disease transmission and prevalence were selected as control inputs. In this way, a modified Caputo–Fabrizio fractional-order model of the HIV/AIDS epidemic is proposed. By changing the contact rate of susceptible and infectious people, the atraumatic restorative treatment rate of the treated compartment individuals, and the sexual habits of susceptible people, optimal control was designed. Lastly, simulation results that demonstrate the appropriate performance of the Caputo–Fabrizio fractional-order model and proposed control scheme are illustrated. [ABSTRACT FROM AUTHOR]

Since ecological systems are history-dependent, incorporating fractional calculus and especially variable order ones could significantly improve the emulation of these systems. Nonetheless, in the literature, no study considers ecological processes by variable-order fractional (VOF) model. This study is motivated by this issue. At first, we propose to extend a predator–prey mathematical model with VOF derivatives. The underlying assumption in the proposed model lies in considering values of fractional derivatives as time-varying functions instead of constant parameters. Some system's dynamic features are investigated, and then the control of the proposed system is studied. To this end, a nonlinear model predictive control is offered for the VOF system. The necessary optimality and sufficient conditions for solving the nonlinear optimal control problem in the form of fractional calculus with variable-order derivative are formulated, and the controller's design procedure is delineated. Finally, numerical simulations are performed to demonstrate the developed control technique's effectiveness and performance for the VOF predator–prey model. [ABSTRACT FROM AUTHOR]

In this paper, we mainly concern the fixed-time consensus control problem for nonlinear leader-following multi-agent systems(MASs). An edge-based adaptive control protocol is presented with part of parameters updated by relative information of system state. Compared with the common node-based adaptive protocols, the proposed edge-based adaptive control protocol assigning different time-varying weights to different communication edges is more accurate. By selecting special Lyapunov functions and theoretical analysis, the obtained conclusions indicate that both sufficient conditions and the settling time of the fixed-time consensus are independent of the system's global information such as the spectrum of Laplacian matrix, which implies that the given protocol is fully distributed. Finally, numerical simulation is illustrated to support the correctness of the main theoretical results. [ABSTRACT FROM AUTHOR]

This brief presents a finite‐time output feedback control scheme for the velocity‐free attitude stabilization of rigid spacecraft under inertia uncertainties and disturbances. Note that either the finite‐time velocity observer or the finite‐time extended state observer contains the control torques, which makes the transient performance of the observer‐based output feedback control difficult to be regulated. Alternatively, the filter‐based output feedback control can overcome such problem naturally since the finite‐time velocity filter does not involve the control torques in its structure. Different from the existing finite‐time velocity filters, a finite‐time extended state filter is introduced in this work to estimate the pseudo angular velocity and total uncertain item simultaneously. Then, the developed controller is synthesized based on the recovered information, which does not require the angular velocity for feedback and has the excellent uncertainty and disturbance compensation capability. The global finite‐time stability of the resultant closed‐loop system is evaluated through the Lyapunov direct methodology and homogeneous system theory. Lastly, comparative simulations are carried out to examine the efficiency and superiority of the presented control scheme. [ABSTRACT FROM AUTHOR]

• In this paper, we present a new fractional-order hyperchaotic memristor oscillator. • The proposed system has been investigated through numerical simulations. • A novel robust adaptive controller is implemented to synchronize the system. • As an application, the system has been implemented for voice encryption. The present study proposes a new fractional-order hyperchaotic memristor oscillator. The proposed system is studied through numerical simulations and analyses, such as the Lyapunov exponents, bifurcation diagrams, and phase portraits. Then, using the sliding mode concept, a robust adaptive control scheme is designed to synchronize the proposed system. The adaptation mechanism is implemented to estimate the unknown parameters of the slave system. Then, the output of the proposed adaptation mechanism is used for the control scheme. The stability of the closed-loop system is proven via a fractional version of the Lyapunov stability theorem and Barbalat's lemma. Numerical simulations of synchronization are shown to investigate the performance of the developed control technique on the uncertain fractional-order hyperchaotic memristor oscillator. Finally, as an engineering application, the proposed fractional-order system is implemented for voice encryption. In this regard, to show the appropriate performance of the proposed system for voice encryption, statistical characteristic of the encryption and decryption processes are performed through different methods including correlation, entropy, root mean square, and root sum of squares. [ABSTRACT FROM AUTHOR]