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Many existing control techniques proposed in the literature tend to overlook faults and physical limitations in the systems, which significantly restricts their applicability to practical, real-world systems. Consequently, there is an urgent necessity to advance the control and synchronization of such systems in real-world scenarios, specifically when faced with the challenges posed by faults and physical limitations in their control actuators. Motivated by this, our study unveils an innovative control approach that combines a neural network-based sliding mode algorithm with fuzzy logic systems to handle nonlinear systems. This proposed controller is further enhanced with an intelligent observer that takes into account potential faults and limitations in the control actuator, and it integrates a fuzzy logic engine to regulate its operations, thus reducing system chatter and increasing its adaptability. This strategy enables the system to maintain regulation in the face of control input constraints and faults and ensures that the closed-loop system will achieve convergence within a finite-time frame. The detailed explanation of the control design confirms its finite-time stability. The robust performance of the proposed controller applied to autonomous and non-autonomous systems grappling with control input limitations and faults demonstrates its effectiveness.

Purpose – This research study aims to design a novel risk-managed time-series momentum approach. The present study also examines the time-series momentum effect in the Indian equity market. Apart from this, the study also proposes a novel risk-managed time-series momentum approach. Design/methodology/approach – The study considers the adjusted monthly closing prices of the stocks listed on the Bombay Stock Exchange from January 1996 to December 2020 to formulate long-short portfolios. Newey–West t statistics were used to test the significance of momentum returns. The present research has considered standard risk factors, i.e. market, size and value, to evaluate the risk-adjusted performance of time-series momentum portfolios. Findings – The present research reports a substantial absolute momentum effect in the Indian equity market. However, absolute momentum strategies are exposed to occasional severe losses. The proposed time-series momentum approach not only yields 2.5 times higher return than the standard time-series momentum approach but also causes substantial enhancement in downside risks and higher-order moments. Practical implications – The study's outcomes offer valuable insights for professional investors, capital market regulators and asset management companies. Originality/value – This study is one of the pioneers attempting to test the time-series momentum effect in emerging economies. Besides, current research contributes to the escalating literature on risk-managed momentum by suggesting a novel revised time-series momentum approach.

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.

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.

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.

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.

This paper provides new insights to the long-standing debate initiated by Nelson-Plosser (1982) regarding the mean reverting behaviour of real GDP per capita. The empirical analysis is based on wavelet framework introduced in Aydin and Pata Aydin (2020) which considers not only frequency domain along with time domain but also takes care of smooth structural changes ignored by earlier wavelet based unit root tests, and covers latest available data on 177 countries across the globe. Specifically, the countries are classified into seven different regions (East Asia and Pacific Region, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia and Sub-Saharan Africa) as per guidelines of World Bank. The empirical findings yield interesting insights and several new lessons for practitioners and policy analysts.

The human immunodeficiency virus (HIV), as one of the most hazardous viruses, causes destructive effects on the human bodies’ immune system. Hence, an immense body of research has focused on developing antiretroviral therapies for HIV infection. In the current study, we propose a new control technique for a fractional-order HIV infection model. Firstly, a fractional model of the HIV model is investigated, and the importance of the fractional-order derivative in the modeling of the system is shown. Afterward, a type-2 fuzzy logic controller is proposed for antiretroviral therapy of HIV infection. The developed control scheme consists of two individual controllers and an aggregator. The optimal aggregator modifies the output of each individual controller. Simulations for two different strategies are conducted. In the first strategy, only reverse transcriptase inhibitor (RTI) is used, and the superiority of the proposed controller over a conventional fuzzy controller is demonstrated. Lastly, in the second strategy, both RTI and protease inhibitors (PI) are used simultaneously. In this case, an optimal type-2 fuzzy aggregator is also proposed to modify the output of the individual controllers based on optimal rules. Simulations results demonstrate the appropriate performance of the designed control scheme for the uncertain system.

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among interest rates was established by employing the Vector Error Correction modeling (VECM), which revealed the validation of the Expectation Hypothesis Theory (EH) of the term structure of interest rates, taking into account long-run deviations from equilibrium and inherent nonlinearities. We unveiled short-run dynamic adjustments for the term structure of the BRICS, subject to regime switches. We then used Markov Switching Vector Error Correction models (MS-VECM) to forecast them dynamically during an out-of-sample period of May 2016 through August 2019. The MSIH-VECM forecasts were found to be superior to the VECM approaches. The novelty of our paper is mainly due to the exploration of the possibility of parameter instability as a crucial factor, which might explain the rejection of the restricted version of the cointegration space, and on the dynamic out-of-sample forecasts of the term structure over a more recent time span in order to assess further the usefulness of our nonlinear MS-VECM characterization of the term structure, capturing the effects of the global and domestic financial crisis.

A dynamical model linking stress, social support, and health has been recently proposed and numerically analyzed from a classical point of view of integer-order calculus. Although interesting observations have been obtained in this way, the present work conducts a fractional-order analysis of that model. Under a periodic forcing of an environmental stress variable, the perceived stress has been analyzed through bifurcation diagrams and two well-known metrics of entropy and complexity, such as spectral entropy and C0 complexity. The results obtained by numerical simulations have shown novel insights into how stress evolves with frequency and amplitude of the perturbation, as well as with initial conditions for the system variables. More precisely, it has been observed that stress can alternate between chaos, periodic oscillations, and stable behaviors as the fractional order varies. Moreover, the perturbation frequency has revealed a narrow interval for the chaotic oscillations, while its amplitude may present different values indicating a low sensitivity regarding chaos generation. Also, the perceived stress has been noted to be highly sensitive to initial conditions for the symptoms of stress-related ill-health and for the social support received from family and friends. This work opens new directions of research whereby fractional calculus might offer more insight into psychology, life sciences, mental disorders, and stress-free well-being.

A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment.

We develop a new robust control scheme for a non-holonomic spherical robot. To this end, the mathematical model of a pendulum driven non-holonomic spherical robot is first presented. Then, a recurrent neural network-based robust nonsingular sliding mode control is proposed for stabilization and tracking control of the system. The designed recurrent neural network is applied to approximate compound disturbances, including external interferences and dynamic uncertainties. Moreover, the controller is designed in a way that avoids the singularity problem in the system. Another advantage of the proposed scheme is its ability for tracking control while there exists control input saturation, which is a serious concern in robotic systems. Based on the Lyapunov theorem, the stability of the closed-loop system has also been confirmed. Lastly, the performance of the proposed control technique for the uncertain system in the presence of an external disturbance, unknown input saturation, and dynamic uncertainties has been investigated. Also, the proposed controller has been compared with a Fuzzy-PID one. Simulation results show the effectiveness and superiority of the developed control technique.

This article is devoted to the determination of numerical solutions for the two-dimensional time–spacefractional Schrödinger equation. To do this, the unknown parameters are obtained using the Laguerre wavelet approach. We discretize the problem by using this technique. Then, we solve the discretized nonlinear problem by means of a collocation method. The method was proven to give very accurate results. The given numerical examples support this claim.

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.

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.

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.

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.

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.

The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich dynamics and some other complex formations in biochemical reactions. In the present work, the synchronization problem of the glycolysis reaction-diffusion model is handled and examined. In addition, a novel convenient control law is designed in a linear form and, on the other hand, the stability of the associated error system is demonstrated through utilizing a suitable Lyapunov function. To illustrate the applicability of the proposed schemes, several numerical simulations are performed in one- and two-spatial dimensions.

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.

A novel approach to solve optimal control problems dealing simultaneously with fractional differential equations and time delay is proposed in this work. More precisely, a set of global radial basis functions are firstly used to approximate the states and control variables in the problem. Then, a collocation method is applied to convert the time-delay fractional optimal control problem to a nonlinear programming one. By solving the resulting challenge, the unknown coefficients of the original one will be finally obtained. In this way, the proposed strategy introduces a very tunable framework for direct trajectory optimization, according to the discretization procedure and the range of arbitrary nodes. The algorithm’s performance has been analyzed for several non-trivial examples, and the obtained results have shown that this scheme is more accurate, robust, and efficient than most previous methods.

The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

The risk‒return trade-off is a fundamental relationship that has received a large amount of attention in financial and economic analysis. Indeed, it has important implications for understanding linear dynamics in price returns and active quantitative portfolio optimization. The main contributions of this work include, firstly, examining such a relationship in five major fertilizer markets through different time periods: a period of low variability in returns and a period of high variability such as that during which the recent global financial crisis occurred. Secondly, we explore how entropy in those markets varies during the investigated time periods. This requires us to assess their inherent informational dynamics. The empirical results show that higher volatility is associated with a larger return in diammonium phosphate, potassium chloride, triple super phosphate, and urea market, but not rock phosphate. In addition, the magnitude of this relationship is low during a period of high variability. It is concluded that key statistical patterns of return and the relationship between return and volatility are affected during high variability periods. Our findings indicate that entropy in return and volatility series of each fertilizer market increase significantly during time periods of high variability.

There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty.