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This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum‐wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite‐time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer‐based finite‐time time‐varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite‐time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented. [ABSTRACT FROM AUTHOR]

This article investigates the problem of monitoring wildfires using a quadcopter uncrewed aerial vehicle (UAV) subject to external perturbations and nonlinear uncertainties. A finite‐time control scheme is designed for the quadcopter UAV to monitor the elliptical propagation of a wildfire. The control scheme is composed of an improved nonsingular fast terminal sliding mode control (NFTSMC) and a higher order sliding mode observer (HOSMO). The HOSMO is constructed first to estimate external perturbations, nonlinear uncertainties, and incomplete system states resulting from the system's uncertainties. Then, a novel NFTSMC is developed that utilizes the estimated system states. The Lyapunov theorem is used to demonstrate that the quadcopter UAV can elliptically monitor a wildfire, and the tracking error can rapidly converge to zero within a finite time. Comparative simulation results are presented to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the H∞ control problem for polynomial time‐varying systems. The H∞ control problem has been much less investigated for time‐varying systems in comparison to the time‐invariant systems. Approximate dynamic programming (ADP) is an optimal method to solve the control problems. Therefore, it is valuable to solve the polynomial time‐varying H∞ control problem with the ADP approach. Considering the time as an independent variable for sum‐of‐squares (SOS) optimization problems, an SOS‐based ADP method is proposed to solve this problem. A policy iteration algorithm is presented, where in its policy evaluation step it is sufficient to solve an optimization problem. Some constraints are added to this optimization problem to guarantee the closed‐loop exponential stability. The convergence and stability properties of the proposed algorithm are stated and proven. Moreover, in order to design an H∞ controller with a smaller disturbance attenuation coefficient, a two‐loop algorithm is suggested. Finally, the effectiveness of the proposed method is demonstrated by simulation examples. [ABSTRACT FROM AUTHOR]

The stabilization problem for uncertain discrete‐time singular Markovian jump systems (DSMJSs) with time‐varying delays is comprehensively covered in this paper. An updated Lyapunov–Krasovskii functional is presented via a discrete state decomposition method. With the help of this constructed Lyapunov–Krasovskii functional, some delay‐ and mode‐dependent sufficient conditions for the open‐loop DSMJSs are derived. Based on these circumstances, a memory mode‐dependent state feedback control is used to create a closed‐loop DSMJS with parameter uncertainties that is regular and causal. And then, the stochastically admissible conditions are attained. Through the exact calculation of each decomposition component for the designed memory state feedback controller, the intended memory state feedback controller settings are determined. It should be mentioned that the algorithm suggested in this article expands the controller design's feasibility and flexibility. The numerical results show how the approach is superior to previous ones, and the given results are less conservative. [ABSTRACT FROM AUTHOR]

This paper studies the control problem of free‐floating space manipulators, and a control scheme is proposed to solve reactionless control of the end‐effector pose tracking with parameter uncertainty and input disturbance. First, based on dual modeling to treat the end‐effector as a virtual base spacecraft, the dynamics with uncertainties are established which map the joints' torque to the end‐effector pose and base spacecraft attitude, while the inverse kinematics and the derivative of the generalized Jacobi matrix can be avoided in controller design. Then, the reference acceleration stabilization schemes satisfying prescribed performance constraints are carefully designed for tracking errors, and based on these schemes the steady‐state and transient performance of the tracking control can be guaranteed. Further, the radial basis function neural network is adopted to estimate modeling errors caused by parameter uncertainty and input disturbance. In addition, a concurrent learning method is introduced in the network weights matrix update law, which allows the estimation errors to converge a neighborhood of zeros without the need for satisfying the persistent excitation condition. The simulation results verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive control (MPC) approach based on the extended disturbance observer (EDOB) for trajectory tracking of a coaxial octorotor unmanned aerial vehicle (UAV). First, the system dynamic model is derived using Newton–Euler relations in the presence of time‐varying centre of gravity (COG); then, a two‐loop cascade structure is presented to perform the trajectory tracking task. Both loops are controlled using MPC with feedforward compensation based on the EDOB to improve disturbance rejection abilities. When the mass changes, the moment of inertia and COG are affected. The EDOB simultaneously estimates the effects of time‐varying mass, external disturbances, and parametric uncertainties in six degrees of freedom. After obtaining virtual control inputs using designed controllers, constrained control allocation is used to obtain rotors speed in a valid range. The proposed control scheme is evaluated using simulation. The simulation results show the ability of the developed control strategy in accurate trajectory tracking and stable flight in different conditions and being robust to uncertainty and disturbance. [ABSTRACT FROM AUTHOR]

This paper concentrates on presenting a reliable procedure to compute the stabilizing region of fractional‐order proportional integral derivative (FOPID) controllers for interval fractional‐order plants having an interval time delay. An interval fractional‐order plant is defined as a fractional‐order transfer function whose denominator and numerator coefficients are all uncertain and lie in specified intervals. Also, an interval time delay points to a delay term whose value varies in a specific interval. The D‐decomposition technique and the value set concept are employed to determine the stabilizing region of FOPID controllers. In this study, first, a theorem is presented to compute the boundary of the value sets of systems having interval time day. Then, a lemma is provided for robust stability analysis of the given closed‐loop control system. For a convenient use of the paper results, an algorithm is proposed to solve the problem of robustly stabilizing interval fractional‐order plants with an interval time delay using FOPID controllers. Finally, four examples are provided to illustrate the proposed procedure. [ABSTRACT FROM AUTHOR]

Electrostatic micro electro mechanical system (MEMS) as an actuator is one of the most widely used actuators in micro dimension systems. Due to the high nonlinear characteristics in the micro actuator's model, nanopositioning has been a challenging problem for researchers. In addition, the system is subject to uncertainties associated with the system's unknown parameters and un‐modelled dynamics. Thus, to overcome these problems and achieve precise positioning a robust non‐linear control method is required. In this research, an optimal voltage control algorithm is initially designed utilizing the nonlinear predictive control approach. The Taylor expansion sequence is used to estimate the final position of the parallel plate actuator. To approximate unknown disturbances, a radial basis function neural network (RBFNN) has been designed. The estimations made were used in updating the control input in the previous step, and the obtained results showed an increase in the robustness of the designed controller. The stability of the closed‐loop system is also analyzed. Finally, a comparison is accomplished between the potential of the proposed controller and that of the feedback linearization and sliding mode controllers. The obtained results indicate better performance and robustness of the suggested controller. [ABSTRACT FROM AUTHOR]

In this paper, the trajectory tracking problem of controlling a constrained quadrotor with unmeasurable system states in an environment with stochastic wind‐gust disturbance is considered. The mathematical model of the quadrotor is divided into the translational system and the rotational system, while only the measurement output of the quadrotor can be accessed. A new output‐based control method is developed for solving this problem. In the translational control system, an output feedback stochastic model predictive control (MPC) algorithm is proposed to generate the optimal control sequence with less conservativeness, by taking into account the information on the distribution of the disturbances and the uncertainty resulting from the attitude tracking error. The closed‐loop probabilistic constraints satisfaction, the recursive feasibility and the stability of the algorithm are further proved. In the rotational system, the active disturbance rejection control (ADRC) method to estimate and compensate for external disturbances is leveraged and robust control for attitude tracking is accomplished. The convergence of the disturbance estimator and the stability proof are provided. Finally, the robustness and effectiveness of the proposed control strategy are verified by an illustrative example. [ABSTRACT FROM AUTHOR]

The shaking table test has been widely recognized as one of the most reliable methods for assessing the dynamic response of structures and systems when exposed to a range of vibrations. Replicating these vibrations, recorded as acceleration signals, necessitates precise controller designs. However, traditional controller designs primarily rely on linear system assumptions, leading to complications in incorporating parametric uncertainty and uncertain non‐linearity. This paper presents a novel approach to the acceleration trajectory control problem of single‐axis hydraulic shaking table systems from a non‐linear perspective. This strategy employs a controller combined with an extended state observer (ESO) and adaptive control, offering a design that more closely reflects real‐world scenarios and requires less parameter adjustment. The ESO estimates the uncertain non‐linearity using the displacement signal, while the parametric uncertainty is tackled through adaptive control. The proposed controller's effectiveness is validated through theoretical proof using Lyapunov analysis. The analysis demonstrates that asymptotic tracking performance is guaranteed when the uncertain non‐linearity is time‐invariant. Moreover, with time‐variant uncertain non‐linearity, the controller can also ensure both prescribed transient tracking performance and final tracking accuracy. Comparative experiment results underscore the superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

In this work, an adaptive learning robust controller is proposed to suppress the vibration of offshore platforms, which are subject to waves, winds, varying control delays and parametric perturbations. To realize nonlinear uncertainty approximation under the bounded H∞$H_\infty$ performance, the H∞$H_\infty$ controller incorporates both an online adaptive part and an offline fixed part. The adaptive part constructed by neural networks adjusts online, while the fixed part is obtained by regulating the H∞$H_\infty$ performance. Importantly, adaptive updating strategy does not require accurate values or upper bounds for real‐time control delay or uncertainty. Several comparable experiments demonstrate the feasibility and effectiveness in vibration‐suppression of the designed adaptive controller in shallow/deep water. This scheme significantly reduces system response variations due to structural and hydrodynamic uncertainty, as well as additional random environmental forces caused by winds. [ABSTRACT FROM AUTHOR]

The quadratic matrix equation (QME) Q(X)=AX2+BX+C=0,$$\begin{equation*} Q(X)=AX^{2}+BX+C=0, \end{equation*}$$occurs in the branches such as the quadratic eigenvalue problems and quasi‐birth‐death processes. Also, the numerical solution of QMEs is an essential step in many computational methods for linear‐quadratic and robust control, filtering, controller order reduction, inner‐outer factorization, spectral factorization, and other applications. In this study, schemes are presented to solve the QME based on the Hermitian and skew‐Hermitian splitting (HSS). It is shown that the proposed schemes converge to the solutions of the QME. Finally, some examples are solved to discover the application of the schemes in comparison with Newton's method. [ABSTRACT FROM AUTHOR]

This paper presents a novel input channel gain adaptive active disturbance rejection control (ICGA‐ADRC) framework for the nonlinear control system with large load variation. This paper first introduces the design of ICGA‐ADRC and proves its stability through the rigorous Lyapunov approach. Subsequently, the proposed controller is simulated by subjecting it to a nonlinear mass‐spring‐damping system characterized by significant load variations. Furthermore, the position‐tracking performance of the controller is tested experimentally by using a motor‐driven pendulum system with various loads. Finally, the results of both simulations and experiments show that the ICGA‐ADRC is capable of compensating for system model uncertainty and external perturbation while accurately estimating the system input channel gain in real‐time. The innovation of this paper is that the robustness of active disturbance rejection control (ADRC) to the system load variation is significantly strengthened by integrating robust adaptive finite‐time parameter estimation method into the conventional ADRC control architecture. [ABSTRACT FROM AUTHOR]

This work develops a new robust multi‐objective model predictive control (MoMPC) strategy for constrained non‐linear systems with bounded disturbances. The multiple objectives are always contradictory, and the presence of disturbances may result in the violation of state constraints and instability of the controlled system. Firstly, the conflict between multiple objectives is reconciled by minimizing the distance of the cost function vector to the vector of independently minimised objectives obtained by solving a set of steady‐state optimisation problems. Then the contractive state set, which takes into account the effect of disturbances on system states, is constructed to guarantee the robust satisfaction of state constraints. Finally, a stability constraint updated online by solving an auxiliary optimization problem is established to ensure the robust stability of the system under MoMPC. An isothermal chemical reactor system is employed to verify the effectiveness of the controller proposed here. [ABSTRACT FROM AUTHOR]

In this paper, a composite fixed‐time sliding mode controller is proposed to address the problems of finite‐time escape and large initial error inherent in disturbance observer (DOB) based controllers. The proposed hybrid control strategy utilizes a fixed‐time DOB and is designed to stabilize a class of second‐order systems in the presence of both matched and mismatched disturbances, while ensuring fixed‐time convergence. First, a fixed‐time DOB is developed to estimate both types of disturbances. Second, a robust sliding mode variable with variable exponent coefficient and a former finite‐time sliding mode controller are proposed to guarantee state convergence without using DOB estimation, thereby preventing the state from diverging due to the unknown large initial error caused by DOB and ensuring convergence prior to switching. Third, a latter robust fixed‐time DOB‐based controller is designed after exact estimation of disturbances is achieved and an estimation of the overall convergence time is provided. Furthermore, strict Lyapunov analysis is employed to prove that the disturbed system under the fixed‐time DOB and composite sliding mode controller is fixed‐time stable. Simulation results for a standard planar uncertain system and Buck converter with mismatched disturbance demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The issue of robust stability here is carefully thought about for uncertain neutral type descriptor systems (NTDSs) with mixed delays. An augmented Lyapunov–Krasovskii functional considering the cross information of the singular matrix and state delay derivative is constructed. With the help of the zero‐value equation technique and an improved matrix inequality, a novel delay‐dependent admissible criterion is developed to make sure of the regularity, non‐impulsiveness, and stability of the NTDSs. On this basis, the robust stability of the uncertain NTDSs is further explored, and a novel robust stability criterion is developed. These criteria have less conservatism. Through three number‐based examples, some comparisons are provided to show the superiority and effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive robust control problem based on reinforcement learning for an affine nonlinear system with unknown time‐varying uncertainty. Inspired by the ability to estimate uncertainty of neural network, a novel policy iteration algorithm is proposed which alternates between the value evaluation, uncertainty estimation, and policy update steps until the adaptive robust control law is obtained. Especially during the step of uncertainty estimation, the unknown time‐varying uncertainty is approximated by a radial basis function neural network and introduce it into the reinforcement learning framework. By designing an appropriate utility function, the algorithm improves both convergence rate and final approximate error comparing with existing reinforcement learning algorithm. The Lyapunov stability theorem provides theoretical demonstrations of the stability and convergence. Furthermore, the uniformly ultimately bounded stability of the affine nonlinear system is demonstrated with unknown time‐varying uncertainty. Finally, the performance of the proposed algorithm is demonstrated through a torsion pendulum system. [ABSTRACT FROM AUTHOR]

This paper proposes a wheeled mobile robot (WMR) robust control scheme. The feasible strategy is developed to achieve an efficient and robust autonomous mobile robot motion. To realize kinematic autonomous planning and control of the WMR, a novel controller is designed based on control Lyapunov function. This part can be divided into the following two aspects: 1) considering the nonholonomic constraints in the autonomous mobile robot trajectory tracking, a dynamic feedback‐linearization is adopted by utilizing differential flatness‐based integrated control framework to achieve full‐state controllability; 2) to compensate the structured uncertainties and slippage disturbances related to the robot kinematic model, a robust controller is designed based on control Lyapunov function with quadratic programming. Such a strategy can achieve autonomous motion even with unknown slippage disturbances subject to various constraints. Moreover, the sufficient condition is also analyzed to ensure the WMR system exponential stability. The effectiveness and performance of the proposed method are verified by numerical simulation. [ABSTRACT FROM AUTHOR]

A robust tube‐based distributed model predictive control method is proposed for spatially interconnected systems with constraints and disturbances. The system contains multiple discrete‐time piecewise affine subsystems, which are coupled to each other through states. The predictive states of each subsystem are dependent on its states, inputs, and neighbouring states, and shrinking constraints are constructed to deal with the disturbances and the model mismatches. The differences between the predicted states at the current time instant and the optimal states at the previous time instant are constrained, which improves the accuracy of the predicted model and enhances the control performance. The conditions which ensure the recursive feasibility of optimization problems and the asymptotic stability of closed‐loop system are obtained. The effectiveness of the proposed method is verified by the simulation results. [ABSTRACT FROM AUTHOR]

Underwater vehicle‐manipulator system (UVMS) is an emerging advanced equipment in underwater intervention scenarios. Several challenges limiting the performance of UVMS's manipulator are treated as burning issues, such as dynamic coupling effects, system uncertainties, disturbances etc. In this article, a novel lightly computational adaptive neural network based backstepping super‐twisting sliding mode control framework is proposed for underwater manipulator trajectory control in UVMS intervention scenario. First, a backstepping super‐twisting sliding mode control (BSSTSMC) scheme is derived, in which the chattering phenomenon of traditional sliding mode control is reduced effectively. Compared with other chattering‐free methods, the higher order control theory maintains the system robustness as well as control accuracy better. Second, a modified neural network (NN) is introduced to predict the unknown system dynamics with the consideration of coupling effects in real‐time. Then, a dimension compression strategy (DCS) for the NN's input layer is proposed to reduce the computational burden and improve the predict performance. Next, based on the Lyapunov method, the system stability is proved, in which the related error variables are guaranteed to be uniformly bounded. Finally, numerical simulations demonstrate the effectiveness and advantages of the proposed DCS‐NN‐BSSTSMC framework through the comparison with other controllers. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSM) have become prevalent in industry and play an essential role in managing industrial processes, automation systems, and renewable energy sources due to their superior efficiency, torque, and power density. However, because it operates like a non‐linear system with quick dynamics, variable parameters during operation, and unknown disturbances, PMSM presents challenges for machine control. Non‐linear controls are required to account for the non‐linearities of the permanent magnet synchronous machine. Recently, predictive control techniques for non‐linear multi‐variable systems have gained popularity. In this work, a novel approach to robust non‐linear generalized predictive control (RNGPC) has been developed for PMSM, with the aim of tracking the reference speed while maintaining minimum reactive power, robustness to external disturbances, and parameter uncertainties. A new finite horizon cost function is integrated, with an integral action introduced in the control law. The main advantage of this technique is that it does not require the measurement and observation of external disturbance as well as parametric uncertainties. The control strategy method has been tested in the MATLAB/Simulink environment with various operating conditions. The results showed good robustness against parameter changes and ensured fast convergence. [ABSTRACT FROM AUTHOR]

In collaborative haptic training systems, a novice operator is interfaced with an expert operator and cooperatively performs some task on a real/virtual environment. Most control architectures for collaborative haptic training systems do not consider the switching task dominance together with investigating overall stability in the presence of nonlinear dynamics and uncertainty. In this paper, a theoretical framework is presented for switching task dominance in collaborative haptic training systems based on supervision and intervention of the expert operator. To that effect, the novice operator performs the operation with as little as possible interference haptic signals in the normal operational conditions. On the other hand, the expert operator is able to intervene the operation to guide the novice operator when it is necessary. The most challenging part of controller design for such systems is to provide the mentioned supervisory framework in a way that the stability of interaction between the operators and the system is ensured with acceptable task performance in various operational conditions. This work offers a variable‐gain dual robust control scheme to address the above problem. The key idea is that the tracking gain of each controller is adjusted in real‐time to switch the task authorities. It is verified that the input‐to‐state stability property is satisfied for each subsystem. Then, the overall stability is proved by leveraging the small gain theorem. Finally, the functionality and performance of the suggested control architecture is demonstrated through simulation and experimental studies. [ABSTRACT FROM AUTHOR]

It is still an open and challenging issue to the typical position control problems of the three‐axis electrical‐optical gyro‐stabilized platform systems (TEOGSP), due to inherent characteristics, for example, measurement noise, input saturation, parametric uncertainties, largely unknown load disturbance. To solve this problem, a saturated adaptive robust feedback controller using an adaptive cascaded extended state observer (SAFCESO) is proposed for compromising between the measurement noise effect and the sensitivity to disturbances. Firstly, the matched and mismatched disturbances existing in the TEOGSP system are estimated and rejected by the cascaded adaptive extended state observers (CESO). Secondly, the parametric uncertainties are evaluated by the adaptive control, and the match disturbances are attenuated by the robust control. Moreover, the adaptive robust control law does not require the velocity measurement signal and internal dynamics information of the system, which is practical to implement. Hence, all various uncertainties could be mainly compensated. Then, the improved auxiliary systems governed by smooth switching functions are developed and incorporated into the control design to compensate for the effect of the input saturation. Finally, the command filters are introduced to limit the magnitude of the virtual control and to calculate the derivative of the virtual control, respectively. The extensive comparative experimental results in the TEOGSP systems showed that the proposed SAFCESO method had superiorities in terms of high‐precision tracking accuracy, robustness, and noise reduction. [ABSTRACT FROM AUTHOR]

This paper develops a novel Taylor‐based adaptive sliding mode control method (ASMC) for robot manipulators. In the first new scheme, sliding mode control (SMC) is effectively enhanced using the Taylor expansion for achieving a less conservative sign‐function gain that enables chattering attenuation. After that, a new Taylor‐based adaptive SMC scheme is proposed without prior knowledge of the upper bound of uncertainty and the Taylor expansion coefficients. Then, a new Taylor‐based boundary layer ASMC scheme is proposed for chattering attenuation. As a fact, no chattering is expected to be observed as long as the sliding surface remains an invariant set. However, the sliding surface will not be an invariant set unless the sign‐function is precisely determined on the sliding surface. It is verified that the sign‐function cannot be precisely determined on the sliding surface due to the presence of uncertainty, hence, the chattering phenomenon. In the new Taylor‐based boundary layer ASMC scheme, the conventional ASMC is modified by removing the sign‐function in the vicinity of the sliding surface and using the Taylor expansion to estimate and compensate for the uncertainty. Compared to a time‐delay ASMC applied to a robot manipulator, the Taylor‐based ASMC scheme exhibits a less conservative sign‐function gain resulting in chattering attenuation. [ABSTRACT FROM AUTHOR]

In this work, a novel model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL). Firstly, a generalized nonlinear nonaffine system with external disturbance is transformed into an affine system via the FFDL method, which contains a linearly parametric term affine to the control input and preceding output data, and an unknown nonlinear time‐varying term. Then, an adaptive estimation method and a discrete‐time extended state observer (DESO) are used to estimate the pseudo gradient (PG) vector and lumped uncertainties, respectively. Furthermore, an integral sliding mode control scheme containing a modified prescribed performance function and an anti‐windup compensator is designed to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state and to suppress the influence of input saturation. Simulation results demonstrate the superiority of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Most studies on a multiobjective optimal control problem (MOCP) with nonlinear stochastic jump‐diffusion system (NSJDS) constraints assume the state vector is available, but in practice, this is not necessarily the case. The observer‐based MOCP is worthy of further research. Furthermore, the hybrid multiobjective differential evolution algorithm (HMODEA) is usually employed to help solve MOCPs with dynamical system constraints, and such problems often have a higher computational burden. To address these two issues, a grid‐based front‐squeezing searching algorithm (GBFSA) is proposed to solve the observer‐based MOCP with NSJDS. Takagi‐Sugeno (T‐S) fuzzy model is used to approximate the NSJDS and convert the problem into an MOCP with linear matrix inequality (LMI) constraints. Then, the GBFSA efficiently searches for the Pareto front by merging the LMIs and the squeezing theorem. To automatically select a preferred Pareto controller, the minimum Manhattan distance (MMD) approch is applied. Mathematical proofs are given to show that the obtained Pareto optimal controller can concurrently stabilize the associated NSJDS and achieve the desired performance indices. In addition, computational complexity and convergence analysis are also provided. [ABSTRACT FROM AUTHOR]

This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time‐varying disturbance observer for a discrete‐time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air–ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time‐varying air‐ground cooperative tracking control model based on the nonholonomic constraints AMR and UAV is established by polar coordinate transformation. Second, to estimate disturbances of the time‐varying model, a multiple harmonics disturbance observer with time‐varying gains is designed. A hybrid tracking control scheme for the AMR based on the estimated states and MPC method with relaxing factor and kinematics constraints is proposed. Finally, experimental results show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive reinforcement learning control method with interval type‐3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed reinforcement learning controller is independent of the system's dynamics, and only relies on measurable signals of the system. An adaptive robust controller in collaboration with the suggested reinforcement learning method is designed to significantly improve the robustness of the control system. The maximum overshoot/undershoot, convergence rate and final tracking accuracy are ensured a priori by the prescribed performance control methodology. To develop the proposed controller and to achieve a high‐performance closed‐loop system, a high‐gain observer is employed in order to estimate the velocity and acceleration of the quadrotor unmanned aerial vehicles system. The uniform ultimate boundedness stability of the proposed control algorithm is achieved by a Lyapunov‐based stability analysis. Finally, in the simulation section, it is shown that the presented intelligent controller with the proposed learning algorithm result in a better performance in contrast to the other kind of conventional control techniques. [ABSTRACT FROM AUTHOR]

Nowadays, interconnected cyber‐physical systems (CPSs) are widely used with increasing deployments of Industrial Internet of Things (IIoT) applications. Other than operating properly under system uncertainties, CPSs should be secured under unwanted adversaries. To mark such challenges, this paper proposes the solution of secure decentralized robust control for uncertain CPSs under replayed time‐delay and false‐data injection attacks altogether. Potentially, considered attacks can force the whole system to instability and crash. Three challenges are addressed, and solutions are presented: (1) model non‐linearity and uncertainties, (2) existing simultaneous time‐delay and potential false‐data injection attacks with skew probability density functions, and (3) requirement to use real‐time attack detection. Thus, a novel, robust control method to deal with thwart attacks on a closed‐loop control system is proposed to provide the system's trustworthiness. Additionally, novel attack detection methodologies are presented to detect these advanced attacks rapidly based on statistical methods such as Spearman's correlation coefficient, Neyman–Pearson (NP) error classification, and trend analysis. Ultimately, the proposed novel attack detection and robust control protocol are verified and evaluated in real‐time. [ABSTRACT FROM AUTHOR]

This paper concentrates on robust deadlock control problems for an automated manufacturing system with uncontrollable, unobservable events and resource failures, which is modeled by a subclass of Petri nets. A recovery subnet is added to the holder of each unreliable resource to model resource failure and recovery. In order to prevent a strict minimal siphon in a Petri net model from being emptied, an extended constraint set is constructed based on the complementary set of a strict minimal siphon to obtain a robust liveness constraint. Due to the uncontrollability and unobservability of the transitions, an automated manufacturing system cannot enforce inadmissible robust liveness constraints. It is necessary to decide the admissibility of robust liveness constraints and transform the inadmissible constraints into admissible ones. To this end, a monitor is designed for each admissible robust liveness constraint such that it can be enforced. Finally, an iterative algorithm is developed to perform the above steps and a robust liveness controlled system is obtained. The feasibility of the control strategy is demonstrated by examples. [ABSTRACT FROM AUTHOR]

In this paper, the design problem of Gain‐Scheduled Output‐Feedback (GSOF) controllers using inexact scheduling parameters for morphing aircraft during the wing transition process is addressed. Both the stability of the closed‐loop system and the L2 gain performance can be guaranteed under the controller based on measured (not actual) scheduling parameters. Firstly, the linear parameter‐varying (LPV) model of morphing aircraft is established by Jacobian linearization and the additive uncertainty is introduced into the scheduling parameters. By employing non‐linear transformations, the problem is formulated as the solution to a set of parameter‐dependent linear matrix inequalities (LMI) with a single‐line search parameter. Finally, the robustness of the flight control system to the wing transition process is verified under the condition of both the uncertainty of aerodynamic parameters and of scheduling parameters. [ABSTRACT FROM AUTHOR]

In this paper, a homogeneous polynomial Lyapunov function (HPLF) is employed in robust observer‐based Integrated Fault‐Tolerant Control (IFTC) of nonlinear systems modelled by the polynomial fuzzy model (PFM). This makes the design problem benefit from the conservative reduction property of polynomial Lyapunov functions (PLFs) and simultaneously avoid the emergence of non‐convex terms due to differentiation of the polynomial Lyapunov matrix. As a result, the less conservative sum of square (SOS) conditions are obtained in the form of polynomial matrix inequalities (PMI), which are solved via SOSTOOLS. For the first time, a non‐quadratic Lyapunov function is used to design a polynomial fuzzy unknown‐input observer that estimates the system's states and actuator faults in the presence of model uncertainties and external disturbances. The effectiveness of the proposed approach in providing more relaxed and less conservative results along with the better fault tolerance is illustrated through three simulating examples and compared with the quadratic Lyapunov function (QLF) approach. [ABSTRACT FROM AUTHOR]

For the sake of safety, the vehicle path tracking control should not only ensure the stability of the path tracking error containing the lateral offset and the orientation error but also guarantee that both the transient and steady states of the lateral offset are within a specified safe boundary. However, the time‐varying uncertainties of a vehicle system make the control design a tough task. This paper develops an adaptive robust control (ARC) which guarantees both the tracking stability and the bounded error property for autonomous vehicles. First, to handle the bounded error requirement, a barrier function based state transformation which converts the constrained lateral offset into an unconstrained state is proposed. Then, the path tracking control task is cast into an equality constraint of the system state. On this basis, a novel adaptive robust constraint‐following controller is developed to make the transformed system follow the proposed equality constraint. Through Lyapunov minimax analysis, it is proved that the resulting control guarantees the approximate constraint‐following performance and the bounded error property despite the presence of system uncertainties. Finally, the main theoretical results are verified through CarSim‐Simulink co‐simulation results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of aerodynamic modelling and robust attitude control for a novel ducted coaxial‐rotor UAV (DCUAV) with time‐varying model uncertainties and external disturbances. In order to achieve robust attitude stabilization and tracking control for the highly non‐linear and coupled system of the DCUAV, a united H∞$H\infty $ dynamic observer (DO) based control design is proposed, which can deal with the uncertainty and disturbance simultaneously without estimating and compensating them during the design problem. Through the combination of the H∞$H\infty $ theory and the DO technique, the developed control method can achieve a better H∞$H\infty $ performance and more accurate tracking control compared with the existing statice observer‐based control design approaches. In addition, the parameterization of the proposed controller is derived from the solution of new LMI formulations based on the transformation of the algebraic constraint. By combining the designed H∞$H\infty $ DO‐based controller with the linearized DCUAV system that includes model uncertainties and disturbances, better robustness and tracking performance has been achieved. Simulation results are carried out to demonstrate the efficiency and good performance of the proposed control design. [ABSTRACT FROM AUTHOR]

Here, a robust sparse decentralized controller (RSDC) for uncertain DC‐linked hybrid microgrids is designed. The robustness against load/generation uncertainty is guaranteed using Kharitonov's stability criteria and sparsity is promoted by appending an l1 term to the typical Linear Quadratic Regulator (LQR). To improve the convergence rate, the differentiable term of the composite cost function is second order approximated, the coordinate descent technique is adopted, and the "active set" idea which limits the search to the effective regions is employed. In addition, the level of sparsity is automatically and optimally adjusted not to compromise the system's stability and robustness. The algorithm is applied to a 69‐bus (consist of 7 hybrid microgrid) distribution system and the controller sparsity percentage, design convergence rate, and performances are compared with the H2 and H∞ designs. The results indicate that the proposed method is superior to the others in all aspects, which are presented by the numerical indices, the time‐domain transient responses, and the closed‐loop poles map. [ABSTRACT FROM AUTHOR]

This research seeks to address a new integrated kinematic/dynamic adaptive Taylor series‐based control design for the robust tracking of electrically‐driven differential drive wheeled mobile robots (WMRs). This control design includes two loops, namely the outer loop (a kinematic control law) and the inner loop (a dynamic controller). Being capable of compensating for far initial conditions from a desired trajectory, a new kinematic control law is designed to make the posture tracking error converge to zero asymptotically as well as to generate a desired trajectory for a dynamic controller. The key role of the dynamic controller is to compensate for lumped uncertainties. To do this, the proposed chattering‐free dynamic controller guarantees that the defined sliding surface which is a function of tracking error and its time derivative will be converged to zero within a finite time. The exact stability analysis of inner closed‐loop system is developed via two Lyapunov‐like positive definite functions to ensure not only the boundedness of all signals but also the finite‐time convergence of sliding surface to zero. The proposed control algorithm is validated by means of various simulations, including comparisons with well‐designed kinematic and integrated kinematic/dynamic control literature. [ABSTRACT FROM AUTHOR]

This paper addresses constrained robust control for dynamic positioning of the Liquefied Natural Gas (LNG) Carrier in side‐by‐side offloading operations subject to input time delay. During the LNG offloading operation, the relative motions of the multibody system are connected by the LNG offloading arm. To ensure the operation safety, robust control with asymmetric Barrier Lyapunov Function is proposed considering predefined output constraints under the unknown dynamics and external disturbances. In offshore engineering, due to the mechanical effects, the thrusters of the LNG carrier in the presence of time‐delay should be investigated to guide the design practice. The predictor‐based method is employed to deal with the time delay induced by the control inputs. It is proven that the proposed scheme can guarantee semi‐global uniform ultimate boundedness. The performance and feasibility of the proposed control are further verified by simulation studies. [ABSTRACT FROM AUTHOR]

For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision‐making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) has been widely used to improve motion performance when reference trajectories and external disturbances are strictly repetitive. However, the occurrence of non‐repetitive trajectories and disturbances would significantly deteriorate the performance of traditional ILC methods. To solve this problem, a time‐iteration‐domain integrated learning control (TIDLC) scheme is proposed for enhancing robustness against non‐repetitive trajectories and disturbances. The TIDLC scheme consists of an ILC term and a time‐domain compensator (TC) term. While the ILC term that performs learning in the iteration‐domain preserves excellent performance under repetitive tasks, the TC term that is updated in the time‐domain can compensate for the variations of trajectories and disturbances. Stability and performance analyses are discussed in both the time‐domain and iteration‐domain. Experimental results on a permanent magnet linear synchronous motor (PMLSM) positioning system verify the validity of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article examines a decentralized event‐triggered robust model predictive control (MPC) for a class of networked large‐scale non‐linear Lipchitz systems. It is assumed that the subsystems are geographically distributed and the connections can be made over a communication network and therefore local event generator modules are used. An event‐triggering condition is then proposed for each module, which only uses local information to trigger data via the communication channel. In this way, the information exchange between subsystems can be reduced significantly compared to time‐triggered conventional control approaches, while the asymptotic stability of the closed‐loop is maintained. In contrast to the reported event‐triggered MPC results, the optimized controller is calculated based on state feedback control law for individual subsystems, which minimizes the upper limit on the infinite horizon cost function subject to constraints on the control inputs. The validness of the proposed scheme is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

Compared to control bandwidth, low‐frequency uncertainties or disturbances like step signals can be well rejected by many methods having two‐degree‐of‐freedom (2‐DOF). Due to robustness constraint, technically more challenging is the rejection of medium frequencies, especially for bandwidth‐limited systems. Here, the equivalent‐input‐disturbance (EID) approach is extended to deal with the main medium‐frequency oscillation of a pantograph‐catenary system. First, a general EID estimator is developed with a low‐frequency estimator as a special case. Then, a fair comparison is conducted to clarify the essential differences between the conventional 1‐DOF‐based and the developed 2‐DOF‐based control systems. Furthermore, a robust stability condition is derived for the 2‐DOF‐based closed‐loop control system. A design algorithm together with design guidelines is provided, where the frequency characteristics of the uncertainties are utilized in the parameter design. Finally, simulations are carried out to validate the developed 2‐DOF‐based method for the pantograph‐catenary system in realistic environment. [ABSTRACT FROM AUTHOR]

This study deals with the problem of robust iterative learning control (ILC) for linear continuous‐time systems with input delay subject to uncertainties in input delay, plant dynamic, reference trajectory, initial conditions and disturbances. Using the internal model control (IMC) structure in the frequency domain, an ILC scheme is proposed in which the IMC structure is responsible for coping with uncertainties in both delay time and plant dynamic. Sufficient conditions are derived to ensure that the tracking error expectation is bounded and converges monotonically to a small neighbourhood of zero (in the L2‐norm sense) when uncertainties in reference trajectory, initial conditions and disturbances vary randomly from trial to trial. It is shown that the derived conditions are still valid to guarantee both boundedness and monotonic convergence of the tracking error variance (in the L2‐norm sense). Illustrative examples are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper discusses the state estimation problems of an uncertain linear discrete time‐varying state space model with d‐step state delay. Based on the principle of minimising the expectation of estimation errors and the method of state augmentation, a robust state estimation algorithm is proposed. Specially, this estimator retains the form of Kalman‐like filter and the characteristics of fast recursive calculation. Moreover, the conditions of bounded estimation error covariance and the proof of asymptotic unbiasedness of the filter are given. Finally, numerical examples are used to verify the effectiveness and the wide applicability of this estimator. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy integrating the non‐linear extended state observer (NLESO) and the non‐singular terminal sliding mode control (NTSMC) for the trajectory tracking of wheeled mobile robots subject to bounded disturbances. A new transformation method of chained model in terms of Lie derivative is presented to simplify the controller design. A specific NLESO combining linear term and non‐linear term is designed to estimate the disturbances with a faster convergence performance. A scheme for determining the gain range of NLESO is explicitly given to facilitate the tuning of experimental parameters. Meanwhile, the NTSMC achieves finite time convergence of the tracking error system and the chattering phenomenon in NTSMC is dramatically alleviated with the compensation from NLESO. The experimental results validate the strong robustness and good performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

A supplemental state observer‐based sliding mode control (SSOBSMC) for a dynamic system is proposed in this paper. First, a supplemental state vector is formulated including system output and supplemental output. A supplemental state observer (SSOB) is proposed to estimate the unavailable states. Furthermore, the SSOBSMC consisting of SSOB and sliding mode control (SMC) is proposed to perform state estimation and robust tracking control for the dynamic system with some unavailable states. To demonstrate the application of the proposed methods, a mass‐spring‐damper (MSD) system is used as an application example to perform numerically. From the simulation results, the SSOB shows a good ability for state estimation, while the SSOBSMC simultaneously demonstrates accuracy in state estimation ability as well as a robust tracking control performance for the non‐linear MSD system. The major contributions of this paper are the proposed SSOB that can accurately estimate the unavailable states for the non‐linear dynamic system with time‐varying disturbances; and the SSOBSMC, which simultaneously displays accuracy in state estimation ability and robust tracking control performance. [ABSTRACT FROM AUTHOR]

In this article, robust static output feedback (SOF) Nash games for a class of uncertain Markovian jump linear stochastic systems (UMJLSSs) are investigated, in which each player may have access to local/private SOF information. It is proved that the robust SOF Nash strategy set can be obtained by minimizing the upper bounds of the cost functions based on a guaranteed cost control mechanism. By using the Karush–Kuhn–Tucker (KKT) condition, the necessary conditions for the existence of the robust SOF Nash strategy set are established in terms of the solvability conditions of nonlinear simultaneous algebraic equations (NSAEs). A heuristic algorithm is developed to solve the NSAEs. Particularly, it is shown that the robust convergence of the heuristic algorithm is guaranteed by combining the Krasnoselskii–Mann (KM) iterative algorithm with a new convergence condition. Finally, a simple practical example is presented to show the reliability and usefulness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates robust path tracking issue of the four‐wheel independent driven robot (FWIDR) under time‐varying system uncertainties and unavoidable external disturbances. A robust optimal integral sliding mode tracking control (OISMTC) scheme based on double feedback recurrent neural network (DFRNN) is proposed for the FWIDR system. Firstly, the presented OISMTC scheme modifies nominal optimal control part by exploiting an additional integral term to improve the tracking accuracy. Then, the designed DFRNN utilizes a double feedback loops structure to enhance the robustness against large system uncertainties by learning to approximate nonlinear systems. The adaptive law of the DFRNN is presented based on the Lyapunov theory to obtain favourable approximation performance in the presence of the time‐varying operating conditions. Moreover, the asymptotic stability of the resultant FWIDR system is guaranteed by mathematical analysis. Finally, practical experiments are conducted to demonstrate the advantages of the proposed DFRNN‐OISMTC method. [ABSTRACT FROM AUTHOR]

Controlling the movements of trains to desired target speed and distance without breaking through safety regions is of primary importance in practical applications for safety reasons. In the classical train control and protection framework, automatic train operation regulates the speed and distance with respect to tracking desired ones under the supervision of automatic train protection, an independently operating subsystem, to prevent the phenomenon of over‐speed and signal passed at danger. This motivates to develop an integrated control scheme combining functions of control and protection, achieving protective tracking control against over‐speed and signal passed at danger doubtlessly. Meanwhile, computationally inexpensive control structure is desired for practical applications due to limited computing resource provided by on‐board computer on trains. In this paper, a robust control with PI structure and protective tracking is proposed for decentralized‐power trains regardless of model uncertainties. Specifically, the circumvent problem of over‐speed and signal passed at danger is formulated as prescribed performance control. It is proved rigorously that the proposed approach results in stable closed‐loop system, and finally, comparative simulation results are given to demonstrate the effectiveness and advantages. [ABSTRACT FROM AUTHOR]

In this study, the authors present an effective online tube‐based model predictive control (T‐MPC) solution for autonomous driving that aims at improving the computational load while ensuring robust stability and performance in fast and disturbed scenarios. They focus on reformulating the non‐linear original problem into a pseudo‐linear problem by transforming the non‐linear vehicle equations to be expressed in a linear parameter varying (LPV) form. An scheme composed by a nominal controller and a corrective local controller is proposed. First, the local controller is designed as a polytopic LPV‐H∞ controller able to reject external disturbances. Moreover, a finite number of accurate reachable sets, also called tube, are computed online using zonotopes taking into account the system dynamics, the local controller and the disturbance‐uncertainty bounds considered. Second, the nominal controller is designed as an MPC where the LPV vehicle model is used to speed up the computational time while keeping accurate vehicle representation. They test the presented scheme and compared the local controller performance against the LQR design as state‐of‐the‐art approach. They demonstrate its effectiveness in a disturbed fast driving scenario being able to reject strong exogenous disturbances and fulfilling imposed constraints at a very reduced computational cost. [ABSTRACT FROM AUTHOR]

An improved robust stability surface analysis method is proposed in this study for fractional‐order time‐delay systems. Through the stabilisation process, a synthesised fractional‐order PD controller can be designed with guaranteed robustness specifications. Firstly, the specification for improved robustness requirements is proposed. In order to find selectable robust controller design parameter combinations for the controlled system, stability region is discussed based on the stability boundary locus, and the robust stability surface is derived straight after. Secondly, the selectable parameter combinations are checked to find the one that best fulfils all the proposed robustness specifications. Finally, numerical simulations are given to demonstrate the effectiveness and flexibility of the presented control algorithm. [ABSTRACT FROM AUTHOR]