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Investigation and implementation of Active Willis Metamaterials (AWM) have been done exclusively, in all the available literature, by approaches that do not rely on any solid control theory basis. When coupled with piezoelectric control elements, the available approaches have not included, from the first principles, the exact form of the constitutive relationship of the piezoelectric materials. Furthermore, in all these approaches, stability analysis, robustness, ability to accommodate uncertainty or parameter changes, or consideration of disturbance rejection has not been addressed at all. More importantly, the available formulations have always mixed the flow and effort variables of the AWM, resulting in a form that is totally incompatible for the use in generating, investigating, or even designing any appropriate sensing or control applications of the material. In this paper, the piezoelectric-based AWM is modeled, from the first principles, to develop a constitutive coupling form that enables its use in actuation, sensing, and as an integrated controller that can be analyzed, designed, and optimized using the classical, optimal, and robust control system theories. Lagrange dynamics formulation is used to generate the equations governing the Willis coupling, the piezoelectric coupling, as well as the active robust controller. With this developed controlled-based structure of the AWM, the inherent and powerful capabilities of the AWM that lie in its ability to robustly control the material properties themselves such as the compliance (or stiffness) and specific volume (or density) are demonstrated in great detail via several numerical examples. Controlling these properties enables the AWM to be used in numerous important and imaginative applications such as cloaking, beam shifting, beam focusing, as well as many other applications that are limited only by our imagination. [ABSTRACT FROM AUTHOR]

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively. [ABSTRACT FROM AUTHOR]

In automated manufacturing systems (AMSs), because of unpredictable failures, resources can lose functions such that the deadlock control methods, in existence, are invalidated. In this paper, a robust deadlock control approach is proposed for AMSs with multiple unreliable resources. The considered AMSs modelled by Petri nets (PNs) allow to acquire different types of resources at each processing stage. In order to visualise the fact that resource failures occur in AMSs, recovery subnets are designed for the modelling AMSs to depict the failures and recoveries of resources. Based on a siphon detection method performed by a set of integer linear programming formulations, a control specification is proposed. Control places (monitors) with their control variables are designed for the detected unmarked siphons at a marking to guarantee that they are always marked even if some unreliable resources break down. Iteratively, all unmarked siphons are detected and controlled. Therefore, a robust deadlock supervisor is synthesised to ensure the controlled system's liveness no matter there exist resource failures or not. The theoretical analyses and proof are given to verify the correctness of the proposed method. Finally, the comparative studies are presented to expound the proposed method's effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

We examine the nexus between financial inclusion and energy poverty. Analysing data for 27 energy-poor countries in the Sub-Saharan Africa region over 2004–2021, we employ sequential (two-stage), panel-corrected standard error (PCSE) and two-step dynamic system GMM (generalized method of moments) regression models, and control for endogeneity, CSD, slope heterogeneity as well as stationarity and cointegration patterns of the variables. Our empirical results show that financial inclusion significantly reduces energy poverty in the selected energy-poor countries. The study also finds a positive significant association between energy access and GDP per capita, while oil price and energy intensity are inversely associated with energy access. The results are robust to different control variables, estimation methods and subsamples. These findings have strong policy implications for energy-poor countries and point to the need for appropriate policies to promote financial inclusion for reducing energy poverty. [ABSTRACT FROM AUTHOR]

[Display omitted] • The construction of unmanned lunar bases requires the work of lunar-based equipment. • This study proposes a robust control algorithm for the stable movement of lunar based equipment. • The proposed robust control strategy performs well in real-time experiment. • The proposed robust control algorithm has advantages over existing control strategies. In lunar exploration, lunar-based equipment assumes the responsibility of mobility and transportation during the construction of unmanned lunar base. Legged robots are an important form of the lunar-based equipment due to their mobility and terrain adaptability. However, the influence of lunar terrain and lunar soil cannot be ignored when it comes to studying the mobile stability of lunar-based equipment. Utilizing the dynamic model of the robotic leg, this study proposes a new robust control algorithm, MBC (Model-Based with Continuous expression) algorithm, for lunar-based equipment's robotic leg, and its effectiveness is established through experimentation and theoretical validation with the Lyapunov second method. In the design and verification, the contact force of lunar soil and the lunar environment with low gravity are considered, indicating the applicability of the proposed MBC method in special lunar scenarios. Compared to the conventional PD (Proportional-Differential) and MPD (Model-Based Proportional-Differential) techniques, MBC robust algorithm ultimately demonstrates superior stability and rapidity with a smaller steady state error. [ABSTRACT FROM AUTHOR]

This research paper focuses on addressing the challenge of infinite-time linear quadratic tracking control (LQT) for linear systems with parametric uncertainty. Traditional solutions to the LQT problem often involve using a discount factor to prevent the cost function from growing unbounded over time. However, this approach can introduce instability in the closed-loop system. To overcome this issue, this paper proposes an alternative approach using an undiscounted cost function that ensures the asymptotic stability of the uncertain closed-loop system. To design a control scheme without requiring precise knowledge of the system dynamics, reinforcement learning (RL) algorithms are employed. However, for systems with uncertain parameters that may lead to instability, the convergence of RL algorithms to a stabilising solution is not guaranteed. To address this limitation, a robust optimal control structure is developed using on-policy and off-policy reinforcement learning algorithms, resulting in a model-free controller. The effectiveness of the proposed robust optimal controller is validated through comparative simulations on an uncertain model of a DC–DC buck converter connected to a constant power load. These simulations demonstrate the advantages and benefits of the robust optimal controller in handling parametric uncertainty and ensuring stability in the control system. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic positioning control scheme for offshore drilling platforms using the equivalent-input-disturbance (EID) method with separated observers. The scheme incorporates observer-based disturbance compensation, wave-filtering state estimation, input transformation, and state feedback control to achieve robust control performance. The main contributions include the design of an EID scheme with separated observers, enabling enhanced disturbance-rejection performance, incorporating a notch filter in the EID estimator to reduce energy loss, and giving an observer tuning scheme to achieve a balanced performance between disturbance rejection and wave filtering. Simulation results demonstrate the effectiveness of the proposed scheme. This work provides valuable insights into designing stable and efficient dynamic positioning systems for offshore drilling platforms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel finite control set predictive direct power control method for grid‐connected converters without cost function evaluations. Unlike conventional predictive direct power control, since the proposed method does not use the model parameters, their uncertainties do not cause prediction error and inappropriate voltage vector selection. The method employs a new form of voltage vector selection based on the slopes of active and reactive powers. The slopes are predicted in a manner with a low sensitivity to sampling noise, without updating a look‐up table, and recursive methods. Hence, there are no stagnation and convergence issues. Also, the proposed method avoids startup problems caused by data‐lacking due to directly regulating the active and reactive power by a switching logic. Flexible power oscillations control with balanced sinusoidal grid currents without any signal sequence extraction can also be achieved under this method in unbalanced grid conditions. The proposed method is assessed by both simulation and experimental studies, and its performance is compared with existing robust combined and model predictive control methods. The outcomes highlight the influence of the proposed approach and establish its superiority over the other considered methods. [ABSTRACT FROM AUTHOR]

Success of the UK's Spherical Tokamak for Energy Production (STEP) programme requires a robust plasma control system. This system has to guide the plasma from initiation to the burning phase, maintain it there, produce the desired fusion power for the desired duration and then terminate the plasma safely. This has to be done in a challenging environment with limited sensors and without overloading plasma-facing components. The plasma parameters and the operational regime in the STEP prototype will be very different from tokamaks, which are presently in operation. During fusion burn, the plasma regime in STEP will be self-organizing, adding further complications to the plasma control system design. This article describes the work to date on the design of individual controllers for plasma shape and position, magneto hydrodynamic instabilities, heat load and fusion power. Having studied 'normal' operation, the article discusses the philosophy of how the system will handle exceptions, when things do not go exactly as planned. This article is part of the theme issue 'Delivering Fusion Energy – The Spherical Tokamak for Energy Production (STEP)'. [ABSTRACT FROM AUTHOR]

This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple. [ABSTRACT FROM AUTHOR]

As photovoltaic energy is clean, renewable, and less noisy, it is increasingly integrated into the grid. This integration aims to overcome energy deficits and get rid of pollution from conventional sources. In this paper, a two-stage configuration of PV energy conversion to a three-phase grid has been studied. The control of this configuration can be divided into two parts, such as DC bus control and AC bus control. The DC bus is controlled by MPPT control. The AC bus is controlled by DC link voltage control, phase-looked loop control, and voltage source inverter control. The contribution in this study aims to improve the quality of energy injected via a new hybrid approach to MPPT control. The proposed control is a combination of robust sliding-mode control and incremental inductance. Unlike conventional hybridization, the proposed control estimates correct the error upon entry of the conductance incremental control. This correction provides an adaptive incremental step for increment conductance control. The proposed control is compared with three other controls under MATLAB/Simulink. Such as incremental conductance, variable step size incremental conductance, and conventional hybrid MPPT incremental inductance-sliding mode control. The simulation results show a remarkable minimization of the ripple phenomenon and the chatter phenomenon. Thus, the quality of the energy injected into the network is improved, such as by reducing the total harmonic distortion of the current and increasing efficiency. [ABSTRACT FROM AUTHOR]

Rehabilitation exoskeleton robots play a crucial role in restoring functional lower limb movements for individuals with locomotor disorders. Numerous research studies have concentrated on adapting the control of these rehabilitation robotic systems. In this study, we investigate an affine state-feedback control law for robust position control of a knee exoskeleton robot, taking into account its nonlinear dynamic model that includes solid and viscous frictions. To ensure robust stabilization, we employ the Lyapunov approach and propose three methods to establish stability conditions using the Schur complement, the Young inequality, the matrix inversion lemma, and the S-procedure lemma. These conditions are formulated as Linear Matrix Inequalities (LMIs). Furthermore, we conduct a comprehensive comparison among these methods to determine the most efficient approach. At the end of this work, we present simulation results to validate the developed LMI conditions and demonstrate the effectiveness of the adopted control law in achieving robust position control of the knee exoskeleton robot. [ABSTRACT FROM AUTHOR]

This research introduces an innovative methodology for the integrated modeling, simulation, and analysis of two fingers, with particular emphasis on their fundamental roles in everyday tasks. In this study, we provide two nonlinear control strategies, specifically Sliding Mode Control (SMC) and Feedback Linearization Control (FLC), to achieve accurate and stable finger movements. As mentioned earlier, the controllers are utilized in the context of a biomechanical model consisting of two fingers, each possessing two degrees of freedom. These controllers enable the coordination of flexion and extension movements. The research conducted in our study emphasizes the coordinated regulation of finger movements, enabling the achievement of flexion through the utilization of two nonlinear controllers. By implementing these sophisticated control mechanisms, we can effectively showcase our model's fidelity in adhering to the physiological limitations inherent to human fingers in their natural state. In addition, the proposed controllers demonstrate sound mitigation of non-linearities, such as load variations, different velocities, positional changes, and damping forces. This approach presents several advantages, such as handling non-linearities, guaranteeing robustness, choosing suitable parameters, and conducting comparative analysis. In order to substantiate our findings, we develop the nonlinear model utilizing the MATLAB/Simulink software. The findings of our study demonstrate effective regulation and control of the two-finger model's position. In our study, we were able to get flexion angles of θ 1 = 1. 221 rad and θ 2 = 1. 396 rad using the sliding mode control (SMC) technique, and flexion angles of θ 1 = 1. 047 rad and θ 2 = 1. 134 rad using the fuzzy logic control (FLC) technique, all within a time frame of 5 s. These results serve to illustrate the applicability and significance of our proposed methodology. [ABSTRACT FROM AUTHOR]

ABSTRACT In this brief, an optimal active fuzzy fault‐tolerant control (OAFFTC) scheme is proposed for a class of unknown perturbed multi‐input multi‐output (MIMO) nonlinear systems with nonaffine nonlinear actuator faults and time‐varying sensor faults. The control strategy can handle automatically (online updating) with three additives (bias, drift, and loss of accuracy) along with one multiplicative (loss of effectiveness) sensor faults and nonlinear state‐dependent actuator faults. In order to deal with uncertain system dynamics, sensor and actuator faults, and external disturbances, fuzzy systems (FSs) and backstepping techniques were combined to provide the adaptive control term as well as a robust control term. Butterworth filter is used to get rid the algebraic loop problem. The suggested robust term, can deal with approximation errors of the fuzzy systems (FSs). To automatically optimize the adaptive parameters and the starting conditions, particle swarm optimization (PSO) approach is introduced. The Lyapunov approach is employed to demonstrate the closed‐loop system's stability. To assess the efficacy and correctness of the suggested scheme, quadrotor dynamic model is performed on the simulation part. [ABSTRACT FROM AUTHOR]

In this paper, the problem of output tracking control is considered for a class of uncertain strict-feedback nonlinear systems with virtual control coefficients and full state constraints. It is assumed only that the nonlinear uncertainties of the systems are some smooth functions enough to guarantee the existence of solutions to the differential equations which describe uncertain systems, and that the virtual control coefficients are unknown. Based on backstepping algorithm, by combining the barrier Lyapunov function with an integral inequality reported in the current control literature, a design method is presented whereby a simple adaptive robust output tracking control scheme can be synthesised. The presented design method can evade the repeated differentiation problem appearing in using backstepping algorithm, and need not know all the nonlinear upper bounds of uncertainties, which are repeatedly employed at each step of backstepping procedure. It is also shown that the output tracking error of the uncertain nonlinear systems with both unknown virtual control coefficients and full state constraints can converge uniformly exponentially towards a ball which can be as small as desired. Finally, a numerical example is given, and the corresponding simulations are also implemented to demonstrate the simplicity of the proposed method and the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

In networked control systems, the communication load is a main concern for implementing model predictive control (MPC). This paper introduces a self-triggered MPC algorithm under network environments to reduce communication load, for discrete-time linear systems with bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly based on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered model predictive control of a nominal system. Moreover, the new cost function explicitly adopts communication load is in consideration. The proposed algorithm is proved to drive the system to an invariant set with respect to the bounded disturbances. Finally, simulation results is provided to verify the effectiveness of the proposed robust output feedback MPC algorithm. [ABSTRACT FROM AUTHOR]

A methodology is presented for the design and implementation of robust controllers that optimise inter-sample performance for a broad range of nonlinear systems operated within a sampled-data environment. The methodology applies to nonlinear continuous-time systems described by state equations, and it allows for modelling uncertainties and constraints on maximal control effort. It is shown that there exist optimal robust state-feedback controllers that minimise inter-sample tracking errors for such systems, as long as an appropriate controllability requirement is met. A relatively simple design and implementation procedure for such controllers is described. [ABSTRACT FROM AUTHOR]

This work proposes a resource efficient robust control scheme for missile-target engagement scenarios subjected to external disturbances. The robustness is achieved by using an annular event-triggered artificial time-delayed control (ET-TDC) methodology with input saturation. The ET-TDC philosophy uses the TDC strategy through a dynamic predefined triggering mechanism which overcomes the requirement to update the control periodically for every sampling instant, unlike conventional TDC and other robust control schemes. Thus the proposed methodology can tackle uncertainties with minimal a-priori knowledge while significantly reducing the over-utilisation of system resources. In addition, the adopted event-triggered mechanism facilitates further conservation of energy which might be crucial for mid-to-long range engagement scenarios. The closed-loop stability is derived analytically and the simulation results illustrate the efficacy of the proposed guidance framework in comparison with other state-of-art robust control methodologies. [ABSTRACT FROM AUTHOR]

Wheelchair upper limb exoskeletons can present a revolutionary approach to aid individuals with neuromuscular disorders in their daily tasks, which require two primary factors, i.e., estimating the human intention to decrease human–robot interactions and generating the adaptive optimal control signals. Therefore, firstly, this study aimed to propose an adaptive neuro-fuzzy sliding mode controller to generate the optimal control signals, in which and an adaptive optimal multi-critic-based neuro-fuzzy system was designed to tune the control gains of the sliding mode controller according to the changes in the joint torques and tracking errors. Secondly, a neural network model was designed as a measurement function in the structure of an extended Kalman filter to infer the human's continuous movement intention, which was trained to establish a comprehensive relationship between inputs, i.e., joints' kinematics and pushrim force, and outputs, i.e., electromyography features. Data acquisition relied on Biometrics Ltd.'s DataLink software and hardware, including surface electromyography and electrogoniometer sensors, while a force sensor system was designed for pushrim force acquisition. The features were extracted using the nonnegative matrix factorization method. The results of the proposed controller were compared with two of the newest control strategies for position-tracking in the wheelchair upper limb exoskeleton robotic system, i.e., proportional derivative-based fuzzy sliding mode control and a fuzzy-based nonsingular terminal sliding mode control structures, which both used the sliding mode control approach with different proposed sliding surfaces to generate the required control signals and also used the neuro-fuzzy system to improve the performance. For the proposed measurement model, the results were compared with the linear regression model and the adaptive neuro-fuzzy inference system, which are two of the most used classification methods. The results affirmed the efficacy of the proposed controller as a robust and accurate control structure for trajectory tracking, both in the absence and presence of disturbance, thereby underscoring its potential for practical applications in manual wheelchair propulsion. Furthermore, the findings underscored the proposed measurement model's robustness and adaptability, making it a promising tool for estimating continuous joint movements in rehabilitation and assistive applications. [ABSTRACT FROM AUTHOR]

To cover the problem of dangerous state prediction ahead of vehicle rollover and rescue the vehicle under abrupt cornering condition, a dynamic Bayesian network (DBN) merged robust control is developed to balance the vehicle ride comfort and handling performance. To discretize the automobile state attributes and prepare for the prediction, class attribute contingency coefficient (CACC) is adopted to pre-process the data conveniently and establish the category labels. The key contributions of this paper are efficient rollover prediction with probabilistic and numerical representation, a mapping rule from rollover probabilities to T–S fuzzy membership values, and an intelligent objective switchable control between ride comfort and roll stability. The co-simulation method is adopted to verify the effectiveness of this method with passive suspension, semi-active suspension, and optimal control active suspension. It is shown that the DBN-based robust control is able to reduce the roll angle by more than 27% compared to the passive suspension under double-lane change condition and has the best balancing performance. From the perspective of ride comfort testing on bounce sinusoidal roads, the vehicle DBN incorporating robust controllers can effectively reject vibrations and switch control objectives based on its running conditions. [ABSTRACT FROM AUTHOR]

The problem of robust tracking and model following is reconsidered for a class of uncertain nonlinear systems with some uncertainties and dead-zone input constraints. In this paper, the system uncertainties are regarded as some nonlinear functions which are not required to must satisfy the matching conditions. That is, being different from traditional robust control theory, the matched structures of uncertainties are sufficient, but not necessary, for being able always to design some types of robust tracking control schemes. In addition, the external disturbances of dynamical systems are assumed to be any bounded functions which is not also required to satisfy the matching condition. By making use of the integral inequality, instead of Lyapunov function, a class of robust tracking control schemes is constructed to guarantee the uniform exponential boundedness of the model tracking errors. Moreover, the upper bounds of nonlinear uncertainties are not required to be known, and it is also unnecessary to know or estimate the characteristic parameters of dead-zone functions. Therefore, the resulting robust tracking control schemes have a simple structure. Actually, the resulting robust tracking control scheme is a linear feedback control one with a self-tuning control gain. Finally, an illustrative numerical example is provided to demonstrate the validity of the presented theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback controller using iterative learning algorithm is proposed for stabilising a class of uncertain nonlinear single-input single-output (SISO) systems with unknown bounded disturbances. By dividing the time interval into equal iteration periods, iterative learning algorithm is integrated into output feedback control and is carried out under alignment condition. Using the output signal, control input and estimates of uncertain parameters, adaptive state observers are established to generate state estimates which will be employed to design the control law. Based on the backstepping technique, the output feedback controller is developed consisting of the ILC part and robust control part. Different from the traditional ILC update laws of uncertain parameters with constant gains, a novel type of parameter update law is developed where the ILC gain is variable and determined by the real-time state estimates such that system stability can be guaranteed. By virtue of the composite energy function, it is proved that all the signals of the closed-loop system are bounded and the output will uniformly converge to zero along the iteration axis, which indicates the stabilisation of the output signal over the time domain. Simulation verification is conducted to apply the proposed controller to a one-degree-of-freedom suspension system to validate the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

In previous studies, structure vibration control mainly focused on integer-order systems or commensurate fractional-order (FO) dynamics systems. In this paper, we propose an adaptive radial basis function (RBF) neural network feedback (ARBF-FK) controller for FO building structure vibration systems with viscoelastic (VE) dampers, uncertain structure parameters and unknown seismic waves. Firstly, use a FO multi-order state space description for FO building structural vibration systems. Then, the design of the ideal feedback controller is based on the stability theory of FO multi-order systems. Moreover, to reduce the cost and facilitate the practical implementation of the control, unknown earthquake seismic waves is approximated by a RBF neural network, the ARBF-FK controller is proposed. In addition, to guarantee the stability of the closed-loop control system and avoid falling into local optimum, network weights are adapted by the FO Lyapunov stability theory instead of gradient descent algorithms. Finally, the convergence rate of the system is analyzed and perform various tests on the ARBF-FK controller. The simulation results demonstrate that the ARBF-FK controller has superior performance and is very robust to control FO building structure vibration systems with uncertain structure parameters and unknown external earthquake excitation. • A FO multi-order state space description method of FO building vibration systems is constructed. • An online adaptive ARBF-FK robust control strategy for FO building vibration systems is proposed. • Adaptive weights of neural networks are derived using the FO Lyapunov theory. [ABSTRACT FROM AUTHOR]

In this paper, the problem of highly performance motion control of tank bidirectional stabilizer with dead zone nonlinearity and uncertain nonlinearity is addressed. First, the electromechanical coupling dynamics model of bidirectional stabilizer is developed finely. Second, the dead zone nonlinearity in bidirectional stabilizer is characterized as the combination of an uncertain time-varying gain and a bounded disturbance term. Meanwhile, an adaptive robust controller with dead zone compensation is proposed by organically combining adaptive technique and extended state observer (ESO) through backstepping method. The adaptive technique is employed to reduce the impact of unknown system parameter and dead zone parameter. Furthermore, the ESO is constructed to compensate the lumped uncertainties including unmodeled dynamics and dead zone residual, and integrated together via a feedforward cancellation technique. Moreover, the adaptive robust control law is derived to ensure final global stability. In stability analysis, the asymptotic tracking performance of the proposed controller can be guaranteed as the uncertainty nonlinearities in tank bidirectional stabilizer are constant. It is also guaranteed to achieve bounded tracking performance when time-varying uncertainties exist. Extensive co-simulation and experimental results verify the superiority of the proposed strategy. • A mechatronic dynamics model of the tank bidirectional stabilizer is developed, which is accurately modeled including couplings, nonlinearities and uncertainties. • The parametric adaptive function is constructed to update the unmeasurable dead zone parameters online, and the extended state observation technique is introduced to eliminate the dead zone nonlinearity and unmodeled dynamics. • The Lyapunov stability analysis demonstrates that the asymptotic tracking capability is guaranteed when the system uncertainty is constant, and the uniformly bounded tracking performance is guaranteed owing to the existence of time-varying uncertainties. • Comparative analysis of co-simulation and experiment confirms the effectiveness and feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The aim of the present article is to design a robust fractional-order (FO) finite-time (FnT) control able to tackle Hölder disturbances of second-order nonlinear systems. First, a novel sliding manifold with Arc-Tangent function is suggested for second nonlinear systems. It has been proven that the system states globally converge to the origin in FnT using the proposed sliding mode variable. To ensure a FnT stability of the sliding variable, a robust control is developed. By using fractional operators, a uniformly continuous control law is designed to tackle Hölder disturbances. Furthermore, the suggested approach is shown to be resistant to matched Hölder disturbances and uncertainties that are continuous but not necessarily differentiable. Moreover, the FnT stability of quadrotors using the proposed control, that is our second result. The quadrotor simulations analysis demonstrates the practicality of the proposed FnT controller in the presence of Hölder disturbances. • A simple nonlinear manifold design is proposed to ensure a finite-time convergence of quadrotors unlike the existing nonlinear sliding variables (Mousavi et al., 2021). • Hölder disturbances are addressed using fractional switching control law in the design of the proposed control scheme. • Compared to existing rejection disturbances (Ju et al., 2022; Labbadi and Cherkaoui, 2020; Razzaghian, 2022; Mousavi et al., 2021), the proposed FOFTC scheme has a faster convergence speed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel controller design using adaptation based modified super twisting control to facilitate trajectory tracking and hovering maneuvers for the quadrotor. The controller gains of the existing modified super twisting control require bounds on the disturbance for trajectory tracking and hovering of the quadrotor. In this paper, the controller gains are adapted using the proposed dynamic adaptation law without knowing the actual disturbance or their upper bounds. The controller is designed within a nonlinear framework without performing linearization of quadrotor dynamics, which enables the proposed controller to remain effective even when the states deviate significantly from their nominal values. The performance of trajectory tracking and hovering of the quadrotor in the presence of disturbance is demonstrated using numerical simulations. In order to assess the effectiveness of the controller, the performance of the adaptation based modified super twisting control is compared to the existing modified super twisting control, and the proposed controller outperforms the existing one. • Adaptive modified super twisting control for robust trajectory tracking and hovering. • Dynamic adaptation law relaxes the need for an upper bound on disturbance. • Numerical simulations show the validity of the proposed approach. • Performance is compared with the existing modified super twisting control. • Efficacy is demonstrated with Dryden wind and sinusoidal disturbances. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology that combines fractional-order control theory with robust control under a parametric uncertainty approach to enhance the performance of linear time-invariant uncertain systems with integer or fractional order, referred to as Fractional-Order Robust Control (FORC). In contrast to traditional approaches, the proposed methodology introduces a novel formulation of inequalities-based design, thus expanding the potential for discovering improved solutions through linear programming optimization. As a result, fractional order controllers are designed to guarantee desired transient and steady-state performance in a closed-loop system. To enable the digital implementation of the designed controller, an impulse response invariant discretization of fractional-order differentiators (IRID-FOD) is employed to approximate the fractional-order controllers to an integer-order transfer function. Additionally, Hankel's reduction order method is applied, thus making it suitable for hardware deployment. Experimental tests carried out in a thermal system and the assessment results, based on time-domain responses and robustness analysis supported by performance indices and set value analysis in a thermal system test-bed, demonstrate the improved and robust performance of the proposed FORC methodology compared to classical robust control under parametric uncertainty. • New method for fractional controllers using Interval poles, improving performance. • Sets region to sweep frequency based on settling time and stability region of α. • Reduces conservativeness, allowing stability changes for flexible performance. • Ensures target with chosen fractional-order controller based on desired actions. [ABSTRACT FROM AUTHOR]

Given a state space description of a linear time-invariant multivariable system, a similarity transformation is applied to get a controllability canonical form. This transformation assures a Bond Graph (BG) realization and avoids the use of active bonds. Active or signal bonds do not preserve the continuity of power flows and consequently, energy conservation properties are lost. The state matrix of the system in transformed coordinates contains an antisymmetric matrix associated with the storage field and the rest is associated with the dissipative field. Thus, in general, the dissipative field is implemented with a multiport element, and in order to give physical meaning, the storage field is implemented with one-port elements. The resulting BG model does not have zero-order causal paths, that is, algebraic loops between the dissipative elements. The results are applied to the BG realization of a linear robust multivariable controller that is designed for a two mass system modelled in BG. [ABSTRACT FROM AUTHOR]

Large wind turbines experience amplified asymmetrical loads at particular harmonics of the rotational frequency. Individual pitch control (IPC) has emerged as a potential controls solution to this problem. H∞$$ {\mathcal{H}}_{\infty } $$ control, which facilitates robust, multivariable controller synthesis in the frequency domain, is a candidate approach to IPC design for several reasons. First, the objectives of asymmetrical load attenuation can be readily described in the frequency domain. Second, the IPC signals and loads in orthogonal directions are known to be coupled, which necessitates a multivariable controller approach. Third, H∞$$ {\mathcal{H}}_{\infty } $$ synthesis is a method that can explicitly impose constraints on the robustness of the closed‐loop system. A downside of IPC is the significant increase in blade‐pitch travel incurred, which introduces additional loading on the blade‐pitch bearings over time. We investigate strategies to constrain the blade‐pitch travel in the controller tuning procedure. A comprehensive study is thus presented for a range of H∞$$ {\mathcal{H}}_{\infty } $$‐synthesized IPCs to attenuate asymmetrical loads on large rotors at harmonics of the rotational frequency while reducing blade‐pitch travel. All developed controllers are validated and compared using a 25‐MW fixed‐bottom offshore wind turbine model via (a) linear analysis of the response of the closed‐loop system to input and output disturbances, (b) nonlinear analysis in terms of structural load power spectra and damage‐equivalent loads (DELs), and (c) the actuator duty cycle (ADC) of the blade‐pitch actuator. [ABSTRACT FROM AUTHOR]

The exploitation of nature to convert energy to electrical power is the most important rule in power generation. Wind energy is one of the most important of those energies that are widely available, and its use does not affect the environment significantly compared to fossil energy. On the other hand, and in recent times, researchers have made great efforts in the field of intelligent control and optimization, which has led to great leaps in the development of these sciences. In this paper, a detailed study is proposed for filling the gaps and conducting an updating state-of-arts of the last pitch control methods in the wind turbine systems. The review is conducted by comparing the key requirements related to control, complexity, stability and speed rangeability. Furthermore, a new classification for the general controller is introduced according to the techniques. Several recommendations for future research related to the control and technical evaluation of wind energy are presented. In sum, the appropriate classification of such important issues and identification of their advantages and drawbacks may greatly contribute to find better solutions. [ABSTRACT FROM AUTHOR]

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller's effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life. [ABSTRACT FROM AUTHOR]

This paper is entirely devoted to analyzing robust control of sampled-data systems. For that, conditions for robust stability under static and dynamic norm-bounded uncertainty are presented and are all expressed through differential linear matrix inequality, a useful mathematical tool able to keep convex all problems to be dealt with. A suitable version of the celebrated small gain theorem (SGT) is then presented and proved in the context of sampled-data systems with inputs and outputs defined in both, continuous- and discrete-time domains. Some academic examples are solved to illustrate the theory and to put in evidence the computational burden needed to calculate their solutions. Since the SGT is only sufficient, the examples are used to discuss and evaluate its conservatism. [ABSTRACT FROM AUTHOR]

Induction motors (IMs) are widely used in industrial applications, but harmonic distortion in IM drives can lead to inefficiencies and reduced lifespan. Traditional control strategies and filtering methods have limitations in mitigating lower-order harmonics, particularly the 5th harmonic, which can generate reverse torque and increase overheating risk. Existing solutions often struggle to fully address these issues, especially in high-power applications with dynamic loading conditions. This paper proposes a control strategy to reduce voltage and current harmonics in a Z-source inverter (ZSI)-fed IM drive. The system incorporates an observer-based output feedback robust servomechanism controller, designed using Linear Quadratic Regulator (LQR) and Linear Matrix Inequality (LMI) techniques. It ensures closed-loop stability, precise tracking regulation, and effective disturbance rejection, while also featuring a speed adaptation mechanism for sensorless operation. Simulation results demonstrate significant harmonic mitigation, with the controller achieving 1.01% lower voltage THD and 1.03% lower current THD compared to conventional approaches. This improvement aligns with industry standards and outperforms several existing techniques. The proposed method contributes a comprehensive solution for harmonic mitigation in high-power IM drives, applicable to various industrial applications requiring high-quality power and precise speed control. [ABSTRACT FROM AUTHOR]

Motivated by the fully actuated system (FAS) approach, this paper proposes a direct robust control method for a type of uncertain multi‐order sub‐fully actuated systems (MOSFASs). Unlike numerous previous results, this paper broadens to a more general multi‐order model and the more challenging sub‐fully actuated case with the presence of singular problems. Firstly, a general uncertain MOSFAS model is presented, where the uncertainty merely needs to satisfy a common assumption. Secondly, a direct robust control method is proposed, giving a robust controller which only needs to satisfy very loose and basic requirements to ensure that the states ultimately converge into an arbitrarily small region. Simultaneously, by constraining the initial values of the states, it is perfectly guaranteed that the states always remain in a "safe" zone free of singular points, thus preserving the realizability of the controller. Finally, under certain conditions, a less conservative robust control scheme is introduced, which can relax the constraint of initial values easily. A simulation for a coarse‐fine angle system demonstrates the validity and value of the proposed robust control method. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization problem of uncertain switched nonlinear systems. Uncertainty and saturated impulses are first considered together in switched nonlinear systems. It appears that the considered systems are robustly locally exponentially stable under the fast average dwell time method and the designed saturated impulsive controller even though subsystems are unstable. At the same time, our conclusions cover some previous results. A step‐by‐step strategy for optimizing multiple objectives is designed to estimate the basin of attraction of the systems as large as possible. At last, the efficacy of our conclusions is illustrated by two given examples. [ABSTRACT FROM AUTHOR]

Under the theoretical support of fuzzy sets theory and confidence index, the proposed approach involves the development of a high‐order robust control method that incorporates optimization techniques to address uncertain systems. First, the utilization of fuzzy sets theory is employed to establish a fuzzy dynamical model. Second, the design of a high‐order robust controller is performed. Third, the design parameters are optimized based on confidence index, and the values are found that minimize the control cost. The simulation results show that this high‐order robust control has significant advantages in dealing with uncertainty, as well as minimizing the control cost. It can be ensured the uniform boundedness and uniform ultimate boundedness. Thus, this proposed method offers the advantage of achieving both robustness and optimality simultaneously. [ABSTRACT FROM AUTHOR]

In this article, we deal with finite‐time optimal control of the Euler–Lagrange (E–L) multi‐body mechanism under: uncertain its kinematics and dynamics, displacement blocking of the damaged joint caused by the actuator failure, undesirable forces/torques exerted on the end‐effector and unknown friction forces originating from joints directly driven by the actuators, as well as completely unstructured forces arising from the kinematic singularities appearing on the mechanism trajectory. Based on the suitably defined task space non‐singular terminal sliding manifold and the Lyapunov stability theory, we propose a class of new fault tolerant estimated generalized Jacobian controllers, which seem to be effective in reducing (minimizing) the joint velocity jumps as well as in counteracting the unstructured forces/torques. On account of the fact, that the multi‐body mechanism is a redundant one, a useful criterion function (energy consumption +$$ + $$ manipulability measure) has been utilized in our controller to temporarily optimally track a desired trajectory. The proposed controllers' performance is demonstrated by computer simulations conducted on a redundant manipulator in conditions of unexpected actuator failure and the corresponding joint lock. Additionally, numerical comparisons are also provided with other representative controllers, found in the literature. [ABSTRACT FROM AUTHOR]

The enhanced model reference adaptive control (EMRAC) algorithm is an effective full‐state adaptive solution to control plants affected by nonlinear unmodelled dynamics and persistent disturbances. The EMRAC strategy improves the tracking performance by equipping the MRAC algorithm with adaptive switching and adaptive integral control actions. However, the need for the plant state prevents the applicability of the EMRAC algorithm to engineering control problems where only the plant output is measurable. To cover this gap and extend the range of plants controllable by EMRAC solutions, this article presents an output‐based EMRAC algorithm leveraging the closed‐loop (CL) MRAC formulation. The robustness of the closed‐loop control system is analytically analysed, not only with respect to plant parameter uncertainties and square measurable disturbances, but also to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unmodelled terms and disturbances. The ultimate boundedness of the closed‐loop control system is assessed with respect to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unknown nonlinear terms and disturbances, by using Lyapunov theory for Filippov systems, as the adaptive switching control action makes the closed‐loop system discontinuous. To assess the effectiveness of the CL‐EMRAC strategy to impose reference trajectories despite the unmodelled plant dynamics and persistent bounded disturbances, the problem of vehicles' direct yaw moment control is used as an engineering case study. The closed‐loop tracking performance is also quantitatively evaluated through a set of key performance indicators and compared to those provided by four benchmark controllers, that is, two LQ‐based strategies and two MRAC‐based control solutions. The CL‐EMRAC and benchmark controllers are implemented and tested in a co‐simulation environment based on a high‐fidelity IPG CarMaker vehicle model. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of robust adaptive safety control of a class of nonlinear systems with parametric uncertainties. Two novel definitions, that is, robust adaptive control Lyapunov function (RACLF) and robust adaptive control barrier function (RACBF) are introduced in this article. Also, an estimation of unknown parameters is obtained based on multiple identification models, state predictors and a logic switching mechanism. The switching mechanism plays a vital role in enhancing the transient properties of nonlinear systems. Meanwhile, a safe motion framework for differential robots is provided based on the existence of parametric uncertainties in RACLF and RACBF, and a safe and stable controller is obtained by solving quadratic programming. Finally, simulation results of differential robot capable of safe motion in space are presented to verify the validity of the design method. [ABSTRACT FROM AUTHOR]

The problem of fixed‐time formation control for a class of second‐order nonlinear multi‐agent systems is studied. For a class of such systems, a control algorithm is proposed to maintain the connections among the agents while avoiding collisions. Furthermore, a radial basis function neural network is used in the design to precisely approximate the nonlinear function for the nonlinear terms in the model. Then, a dynamic sliding mode control method is proposed to suppress the chattering phenomenon that may arise due to the sliding mode control. A sufficient condition for the system to achieve fixed‐time formation is obtained by using different methods, such as Lyapunov stability. Finally, the effectiveness of the proposed algorithm is verified by example. Simulation experiments reveal that the proposed method has faster error convergence and better robust control than conventional algorithms. [ABSTRACT FROM AUTHOR]

Predator–prey interactions are fundamental to ecological and evolutionary dynamics. Yet, predicting the outcome of such interactions—whether predators intercept prey or fail to do so—remains a challenge. An emerging hypothesis holds that interception trajectories of diverse predator species can be described by simple feedback control laws that map sensory inputs to motor outputs. This form of feedback control is widely used in engineered systems but suffers from degraded performance in the presence of processing delays such as those found in biological brains. We tested whether delay-uncompensated feedback control could explain predator pursuit manoeuvres using a novel experimental system to present hunting fish with virtual targets that manoeuvred in ways that push the limits of this type of control. We found that predator behaviour cannot be explained by delay-uncompensated feedback control, but is instead consistent with a pursuit algorithm that combines short-term forecasting of self-motion and prey motion with feedback control. This model predicts both predator interception trajectories and whether predators capture or fail to capture prey on a trial-by-trial basis. Our results demonstrate how animals can combine short-term forecasting with feedback control to generate robust flexible behaviours in the face of significant processing delays. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro‐fuzzy inference system (ANFIS) with the simplicity of a proportional‐integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG‐based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS‐PI) is implemented in the machine‐side converter (MSC) and grid‐side converter (GSC) of a variable speed PMSG‐based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications. [ABSTRACT FROM AUTHOR]

The control of quadrotor vehicles under state and parameter uncertainty is a well studied problem that is vitally important to the deployment of these systems under real world conditions. In this article, we propose a linearization‐based extension to nonlinear systems of the existing scenario model predictive control (MPC) framework, which quantifies the impact of uncertainty on the vehicle dynamics through repeated sampling of the uncertainty space. Given the computational costs of such an approach, we also propose two simplifications of the scenario MPC algorithm that are significantly more tractable. In order to evaluate the performance of the algorithms, the specific problem of the control of a bidirectionally actuated quadrotor vehicle is considered. Simulations are carried out for each scenario MPC scheme as well as for a reference deterministic MPC scheme. When a sufficiently large sample count is considered, each of the scenario MPC algorithms achieves safer performance than the deterministic formulation without sacrificing any optimality. Additionally, the approximate solution techniques conclusively outperform the original nonlinear scenario MPC formulation for the same computational cost. [ABSTRACT FROM AUTHOR]

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. [ABSTRACT FROM AUTHOR]

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator. [ABSTRACT FROM AUTHOR]

The precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim. [ABSTRACT FROM AUTHOR]

This article addresses robust output regulation for systems affected by disturbances generated by an uncertain exosystem as well as matched external disturbances. The robustness of the feedback loop is achieved by combining internal model based control with sliding mode control, resulting in a structurally simple controller. The design of the proposed controller is presented first for the full information problem, that is, the disturbances generated by the exosystem and the state‐information is available for the control algorithm. This requirement is relaxed in the second step of the controller design, where the observer‐based robust output feedback‐loop structure is developed. Results from a comparative simulation study are presented to illustrate the advantages and the effectiveness of the proposed controller concept. [ABSTRACT FROM AUTHOR]

Path tracking plays a critical role in autonomous driving for autonomous ground vehicle (AGV). However, AGV faces challenges in accurate tracking and chatter reduction due to external disturbances, making it difficult to meet the tracking performance requirements. Currently, sliding mode control (SMC) and disturbances observer are primarily employed for disturbance estimation. However, ensuring finite‐time robust control remains a significant challenge. To ensure rapid convergence of tracking errors and effective disturbance rejection, this paper proposed a novel non‐singular fast terminal sliding mode (NFTSM) control scheme based on finite‐time disturbance observation (FDO). First, a novel NFTSM controller based on AGV dynamic model is developed to achieve fast convergence of tracking errors. Then, to mitigate disturbances effects and suppress chatter, an innovative FDO method is employed. Finally, based on FDO, the NFTSM‐FDO establishes a control scheme that enhances disturbances suppression and accelerates convergence. The simulation and experimental results demonstrate the innovation of the proposed method. Compared with other SMC methods, the results validate the effectiveness and advantages of the proposed approach, exhibiting fast convergence and superior tracking performance. [ABSTRACT FROM AUTHOR]

This article investigates the robust consensus problem for nonlinear multiagent systems against time‐varying false data injection attacks on actuators and sensors. First, the root‐mean‐square (RMS) theory is used to extend the assumption of the slow‐varying or constant attack signals to the case of time‐varying attack signals. Second, a novel distributed multivariate observer (DMO) is designed to estimate the followers' system states and the time‐varying attack signals on actuators and sensors. With the help of the outputs of DMO, a distributed robust consensus control arithmetic is proposed, which can compensate for actuator attacks and isolate sensor attacks so that exponential consensus and robust consensus are achieved. In particular, the robust performance of estimation errors and consensus errors is ensured by establishing the RMS gain index via linear matrix inequality, in which the zero initial conditions of estimation errors and consensus errors are not required. Finally, two simulation examples, including a network of four aircraft longitudinal dynamic systems, are given to verify the effectiveness of the proposed arithmetic. [ABSTRACT FROM AUTHOR]

This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event‐triggered control. Introducing a time‐varying constraint function transforms the original practical prescribed time‐tracking control issue into a tracking error constraint problem. An event‐triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples. [ABSTRACT FROM AUTHOR]