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This paper investigates the examination of global asymptotic stability pertaining to uncertain complex-valued stochastic neural networks (UCVSNNs) with multiple time delays. This paper employs the real-imaginary separated activation function to establish an equivalence between complex-valued neural networks (CVNNs) and two real-valued activation functions, representing the real and imaginary parts respectively. This equivalence is utilised to analyse the original UCVSNNs model in a comprehensive manner. The global asymptotic stability of the studied UCVSNNs model is ensured by constructing an appropriate Lyapunov-Krasovskii generalised function and applying the $ It\hat {o} $ It o ˆ formula, linear matrix inequality (LMI) and other analytical techniques to derive the system stability and other sufficient conditions. In the meanwhile, we provide two numerical examples to demonstrate the reliability and benefit of the discovered results. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC. [ABSTRACT FROM AUTHOR]

In this paper, the robust arbitrary trajectory tracking problem of multiple heterogeneous Euler–Lagrange (EL) systems with disturbances under a directed graph is investigated via a novel fully distributed event‐triggered control (ETC) strategy. Firstly, for an arbitrary tracked trajectory, the Fourier transform technique is employed to approximately obtain its linear form, which represents the leader of multiple EL systems. Since the dynamics and state of the leader are not available to all followers, fully distributed adaptive ETC‐based observers are proposed for each follower to reconstruct the leader's trajectory, where the global information requirements are avoided, and the Zeno behavior is excluded by giving the minimum triggering intervals. Armed with the observed information, a robust adaptive ETC protocol is further developed such that the position‐like state of each follower can track the leader with the intermittent controller updating, where a minimum control update interval is obtained theoretically. Moreover, the adaptive technique is used to eliminate the influence of the parameter uncertainties of multiple EL systems, thus the tracking is achieved asymptotically. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This research endeavour's primary attention is on the problems of stabilization and multiple disturbance rejection for a class of interval type-2 fuzzy stochastic systems that are prone to parameter uncertainties and multiple disturbances. Specifically, the multiple disturbances are classified into two aspects: the first aspect is matched disturbances, which emanate from an exogenous system, while the second aspect is norm-bounded mismatched disturbances. Further, to provide a precise estimation of the matched disturbances, a fuzzy rule-based multiple disturbance observer is built. Based on the estimated information from the framed observer, a disturbance estimation-based resilient control is devised that can withstand the effects of disturbances. Besides, the footprints of mismatched kind of disturbances are handled by employing the $ (\mathcal {Q},\mathcal {S},\mathcal {R})-\tilde \varsigma $ (Q , S , R) − ς ~ dissipative performance. Moreover, to insure the attainment of the foremost objective of this analysis, a collection of suitable criteria has been developed by means of linear matrix inequalities with the satisfied disturbance attenuation index through Lyapunov's theory of stability. In addition, under the lighting of adequate conditions, the gain matrices of the configured controller and the framed observer can be determined. Finally, two simulation examples are supplied as a conclusion to the examination, each serving to illustrate the potency and viability of the designed control scheme and the presented theoretical findings. [ABSTRACT FROM AUTHOR]

Anti-sliding stability safety is a critical issue that must be given sufficient and widespread attention during the entire lifecycle of gravity dams. The calculated anti-sliding stability safety factor (ASS-SF) is usually compared with the allowable value required by the standards in the traditional method, which ignores the influence of material parameter uncertainties and leads to unreasonable safety evaluation results. Therefore, the nonlinear functional relationship between the stability safety factor (SF) and the random variable parameters is constructed based on the response surface equations, and the distribution types of SF sequences calculated by the Monte Carlo sampling are determined, then a probabilistic stability evaluation method for concrete gravity dams is proposed. Engineering application shows that the calculated SF obeys the normal distribution; the minimum guaranteed rate of different sliding paths in a gravity dam is 86.66%, and the guaranteed rate for the overload safety factor (OSF) is 36.00%. The results imply that a guaranteed rate for the allowable value of the ASS-SF should be provided when making the stability safety evaluation of the dams, especially the OSF. The outcome of this research will advance the understanding of stability evaluation of concrete dams, and reduce the potential risk of sliding instability of concrete dams. [ABSTRACT FROM AUTHOR]

This study seeks to provide guidance on how top-down greenhouse gas emission reduction targets (GHG ERTs), deriving, for example, from corporate decarbonization strategies, can be translated into quantifiable targets for component measures. Furthermore, it shows how these targets need to be adjusted during the development process to account for parameter uncertainties resulting from the lack of data availability and validity in the early design stage. The scope of the analysis focuses on ecological measure magnitude (EMM) targets for mass reduction and the content of recycled material. The study is split into two sections: The first section introduces a method on how to calculate EMMs based on a partial carbon footprint assessment (CFPA). The second and main section elaborates on an analysis of how parameter uncertainties in the CFPA influence initially defined EMM targets by using perturbation analysis. In the presented paper, the method is applied exemplarily to an automotive component in an internal combustion engine vehicle. The study shows that a parameter uncertainty in the environmental impact of the mass-induced use phase or the primary material production (and semi-finished product provision) has a significant influence on the required EMMs. In the authors' opinion, this study can increase the awareness of how CFPA parameter uncertainties can affect the credibility of EMM development targets. The used approach can help designers and engineers to minimize the risk of a non-fulfillment of GHG emission-related development targets. [ABSTRACT FROM AUTHOR]

This article proposes an improved adaptive finite time command filtered controller for high-precision motion control of electro-hydraulic servo systems. During the controller design process, the effects of parameter uncertainty, unmodeled disturbances, and dead zone uncertainty in the system are effectively addressed by integrating parameter adaptive laws and disturbance estimation laws. By extending the system states, this strategy eliminates the need for the controller to rely on the first-order derivative of the tracking trajectory, making it applicable in tracking non smooth curves. Through Lyapunov stability analysis and comparative simulation experiments, it is shown that even in the presence of unmodeled disturbances, unknown system parameters, and unknown dead zone characteristics in the system, the developed controller can still achieve adjustable neighborhood tracking errors around the origin in a finite time. • A controller for an electro-hydraulic servo system that comprehensively considers multiple nonlinear factors is proposed. • Improved instruction filtering controller, weakened derivative requirements, and expanded application scenarios. • The proposed controller can achieve tracking error convergence within the specified error band within a finite time. • This article considers the nonlinear factors caused by the dead zone of the servo valve in the system. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of non-fragile ${H_\infty }$ filter design for a class of neutral Markovian jump systems with parameter uncertainties and time-varying delays. The parameter uncertainties are norm-bounded, and time-varying delays include state and neutral time-varying delays. First, by selecting the appropriate stochastic Lyapunov-Krasovskii functional and using the integral inequality technique, sufficient conditions are obtained to make the filtering error system not only stochastically stabilized, but also mode and delay dependent. Second, by the utilizing linear matrix inequality method, sufficient conditions are obtained for the filtering error system to be stochastically stable and to have a prescribed ${H_\infty }$ performance level $\gamma $. Based on this result, by processing the uncertainty terms, sufficient conditions for the existence of the filter are obtained, and mode-dependent filter parameters are given. Finally, by numerical simulation, the feasibility and validity of the theoretical results are verified.

This paper deals with the problem of non-fragile H∞ filter design for a class of neutral Markovian jump systems with parameter uncertainties and time-varying delays. The parameter uncertainties are norm-bounded, and time-varying delays include state and neutral time-varying delays. First, by selecting the appropriate stochastic Lyapunov-Krasovskii functional and using the integral inequality technique, sufficient conditions are obtained to make the filtering error system not only stochastically stabilized, but also mode and delay dependent. Second, by the utilizing linear matrix inequality method, sufficient conditions are obtained for the filtering error system to be stochastically stable and to have a prescribed H∞ performance level γ. Based on this result, by processing the uncertainty terms, sufficient conditions for the existence of the filter are obtained, and mode-dependent filter parameters are given. Finally, by numerical simulation, the feasibility and validity of the theoretical results are verified. [ABSTRACT FROM AUTHOR]

Unbalanced grid voltage will affect the normal grid connection operation of the wind energy conversion system WECS), resulting in fluctuations in system output power and distortion of output current. In addition, in the actual permanent magnet synchronous wind power system, there may be uncertainties in the network side circuit parameters due to changes in ambient temperature and detection errors. If the parameter changes, the performance of traditional control methods will decline. Based on this, a model-free adaptive control MFAC) scheme based on grey prediction was proposed to alleviate the performance degradation problem caused by parameter uncertainty and make the control system have better anti-interference ability. Furthermore, three independent control methods were adopted to solve the problem of grid connected output power fluctuation and current distortion under unbalanced grid voltage. The simulation results show that the proposed control strategy not only has good static and dynamic performance under nominal parameters, but also improves the robustness of the system when the grid side inductance changes, which verifies the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Unbalanced grid voltage will affect the normal grid connection operation of the wind energy conversion system WECS), resulting in fluctuations in system output power and distortion of output current. In addition, in the actual permanent magnet synchronous wind power system, there may be uncertainties in the network side circuit parameters due to changes in ambient temperature and detection errors. If the parameter changes, the performance of traditional control methods will decline. Based on this, a model-free adaptive control MFAC) scheme based on grey prediction was proposed to alleviate the performance degradation problem caused by parameter uncertainty and make the control system have better anti-interference ability. Furthermore, three independent control methods were adopted to solve the problem of grid connected output power fluctuation and current distortion under unbalanced grid voltage. The simulation results show that the proposed control strategy not only has good static and dynamic performance under nominal parameters, but also improves the robustness of the system when the grid side inductance changes, which verifies the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Summary: The connectivity of communication graph is indispensable for consensus of multi‐agent systems (MASs), which in many applications (e.g., wireless sensor) depends on the relative distances between agents. But, in the adaptive setting particularly with system nonlinearities and event‐triggered communication, it is rather difficult to enforce the relative distances within the limited range for the connectivity. This paper focuses on developing an adaptive event‐triggered control strategy with connectivity preservation in the context of nonidentical unknown control coefficients and heterogeneous nonlinearities coupling with parameter uncertainties. First, a group of potential functions are introduced acting as control barrier functions to constrain the relative distances between agents within the limited range for all time. Also, two dynamic gains are specialized for each agent to handle the system uncertainties, system nonlinearities and negative effect of the execution error. Then, an adaptive event‐triggered protocol is designed for each agent such that the connectivity‐preserving consensus of MASs is achieved and Zeno behavior is excluded. Moreover, an extended study is conducted on a leader‐following scenario. Two simulation examples illustrate the effectiveness of the proposed event‐triggered control strategy. [ABSTRACT FROM AUTHOR]

The global robust stability of uncertain delayed fuzzy cellular neural networks (UDFCNNs) was analyzed in this paper. The major results of this paper provided some new criteria for the existence and uniqueness of the equilibrium point of UDFCNN. Furthermore, suitable Lyapunov-Krasovskii functionals was designed for obtaining the adequate conditions for the global asymptotic robust stability and global exponential robust stability of UDFCNN. Finally, several numerical examples was provided to verify the validity of the results. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.

In this article, the issue of joint state and fault estimation is ironed out for delayed state-saturated systems subject to energy harvesting sensors. Under the effect of energy harvesting, the sensors can harvest energy from the external environment and consume an amount of energy when transmitting measurements to the estimator. The occurrence probability of measurement loss is computed at each instant according to the probability distribution of the energy harvesting mechanism. The main objective of the addressed problem is to construct a joint state and fault estimator where the estimation error covariance is ensured in some certain sense and the estimator gain is determined to accommodate energy harvesting sensors, state saturation, as well as time delays. By virtue of a set of matrix difference equations, the derived upper bound is minimized by parameterizing the estimator gain. In addition, the performance evaluation of the designed joint estimator is conducted by analyzing the boundedness of the estimation error in the mean-squared sense. Finally, two experimental examples are employed to illustrate the feasibility of the proposed estimation scheme. [ABSTRACT FROM AUTHOR]

In the paper, we study the robust synchronization of complex dynamic networks (CDNs) with deviating arguments and parameter uncertainties via self-feedback control, the model involves both advanced and delayed arguments. In addition, based on the Gronwall inequality and inequality techniques, we derive upper bounds on the length of the arguments and the magnitude of the parameters, when the parameters and arguments of CDNs are below the upper bounds, the CDNs continue to exhibit exponential synchronization. In comparison to linear matrix inequalities and Lyapunov's method in the existing literature, we obtain elaborate bounds. Finally, Several simple examples can demonstrate the effectiveness of the results. [ABSTRACT FROM AUTHOR]

• A novel hierarchical optimization control for electrohydraulic suspension is proposed. • The leaf spring described by a novel resistor-capacitor operator hysteresis model. • The filter adaptive backstepping control generates desired active force. • The projective adaptive fuzzy sliding mode control tracks required target force. • Control parameters optimized by stratified sampling algorithm. The controller design for integrated electrohydraulic suspension with leaf spring is a complicated and challenging task, aiming to achieve optimal dynamic performance for heavy vehicles. Considering the hysteresis property of leaf spring, parameter uncertainties and nonlinearity, a novel hierarchical optimization control strategy including upper and bottom controllers is proposed to effectively suppress the vertical vibration of vehicle body. To describe the hysteresis property of leaf spring, a novel resistor-capacitor operator hysteresis model is identified by experiment and optimized by genetic algorithm. Based on hysteresis model, the filter adaptive backstepping control with sprung mass uncertainty is designed as upper controller to generate the desired active force for electrohydraulic actuator. Then, the projection adaptive fuzzy sliding mode control is designed as bottom controller for nonlinear actuator with time-varying fluid parameter to accurately track the required target force. Additionally, a stratified sampling algorithm is investigated to optimize the parameters of hierarchical control strategy for addressing multi-objective optimization issue of electrohydraulic suspension with leaf spring. Finally, the results show that the proposed method has better robustness and effectiveness, and it can ensure ride comfort, safety and optimal performance. [ABSTRACT FROM AUTHOR]

A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values. [ABSTRACT FROM AUTHOR]

Anti-sliding stability safety is a critical issue that must be given sufficient and widespread attention during the entire lifecycle of gravity dams. The calculated anti-sliding stability safety factor (ASS-SF) is usually compared with the allowable value required by the standards in the traditional method, which ignores the influence of material parameter uncertainties and leads to unreasonable safety evaluation results. Therefore, the nonlinear functional relationship between the stability safety factor (SF) and the random variable parameters is constructed based on the response surface equations, and the distribution types of SF sequences calculated by the Monte Carlo sampling are determined, then a probabilistic stability evaluation method for concrete gravity dams is proposed. Engineering application shows that the calculated SF obeys the normal distribution; the minimum guaranteed rate of different sliding paths in a gravity dam is 86.66%, and the guaranteed rate for the overload safety factor (OSF) is 36.00%. The results imply that a guaranteed rate for the allowable value of the ASS-SF should be provided when making the stability safety evaluation of the dams, especially the OSF. The outcome of this research will advance the understanding of stability evaluation of concrete dams, and reduce the potential risk of sliding instability of concrete dams.

In this paper, an adaptive gain tuning rule is designed for the nonlinear sliding mode speed control (NSMSC) in order to enhance the dynamic performance and the robustness of the permanent magnet assisted synchronous reluctance motor (PMa-SynRM) with considering the parameter uncertainties. A nonlinear sliding surface whose parameters are altering with time is designed at first. The proposed NSMSC can minimize the settling time without any overshoot via utilizing a low damping ratio at starting along with a high damping ratio as the output approaches the target set-point. In addition, it eliminates the problem of the singularity with the upper bound of an uncertain term that is hard to be measured practically as well as ensures a rapid convergence in finite time, through employing a simple adaptation law. Moreover, for enhancing the system efficiency throughout the constant torque region, the control system utilizes the maximum torque per ampere technique. The nonlinear sliding surface stability is assured via employing Lyapunov stability theory. Furthermore, a simple sliding mode estimator is employed for estimating the system uncertainties. The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed speed estimation and the NSMSC approach for a 1.1-kW PMa-SynRM under different speed references, electrical and mechanical parameters disparities, and load disturbance conditions.

This article investigates the finite/fixed‐time synchronization problem of Markovian jumping delayed chaotic recurrent neural networks with uncertainties by employing sliding mode control method. In order to cope with Markovian jump and parameter uncertainties, sliding model control is used for its strong robustness and insensitivity character to external disturbances and parameter variations. Above all, two simplified sliding mode surfaces are constructed. Following that, matching controllers are constructed to guarantee the finite/fixed‐time reachability of sliding mode surfaces. Additionally, the dynamics of the sliding mode surfaces can be achieved in a finite/fixed‐time. Based on the Lyapunov stability theory, some sufficient conditions are proposed to guarantee the finite/fixed‐time synchronization. Compared with the conclusions of the existing literature, this article generalizes the corresponding results to Markovian jump, parameter uncertainties, and nonidentical systems. Finally, two numerical examples are given to illustrate the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Natural disasters may lead to failure of critical assets part of railway systems. Slope failure often results in major economic consequences. Due to the economic importance that railways systems have, it is pertinent to ensure good performance and long‐term. The objective of the present research is to propose tools that may allow obtaining the failure probability of soil slopes under a seismic event by employing surrogate models to ease the computational cost of considering the uncertainties associated with geotechnical and geometrical properties of a case study located in Portugal. The presented methodology and derived fragility curves, using peak ground acceleration (PGA) as the intensity measure (IM), can be used to assess slope performance under a seismic event for different safety levels, thus providing useful information for prioritizing assets and taking preventive actions to maintain the desired performance of the railway system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University of Engineering Science / Shanghai Gongcheng Jishu Daxue Xuebao is the property of Journal of Shanghai University of Engineering Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work addresses the synchronization problem for discrete complex dynamical networks (CDNs) that are associated with Markov jump outer coupling, parameter uncertainties, actuator saturation, and time delay. Especially, the uncertainties which occur in the inner coupling matrix are categorized with the use of the interval matrix approach. The main purpose of this work is to design an appropriate state feedback controller with a saturation effect that guarantees asymptotic synchronization and also satisfy a prescribed level of mixed H∞ and passivity performance for the addressed CDNs. Moreover, a new set of adequate criteria are expressed in the form of linear matrix inequalities to synchronize the considered network plant. The above synchronization criteria are retrieved with the support of an appropriate Lyapunov–Krasovskii functional, Kronecker product properties together with Jensen's inequality and Abel lemma. Eventually, the efficiency and superiority of our proposed synchronization criteria are demonstrated through two numerical examples. [ABSTRACT FROM AUTHOR]

This paper concerns with the preassigned-time projective synchronization issue for delayed fully quaternion-valued discontinuous neural networks involving parameter uncertainties through the non-separation method. Above all, based on the existing works, a new preassigned-time stability theorem is established. Subsequently, to realize the control goals, two types of novel and simple chattering-free quaternion controllers are designed, one without the power-law term and the other with a hyperbolic-tangent function. They are different from the existing common power-law controller and exponential controller. Thirdly, under the Filippov discontinuity theories and with the aid of quaternion inequality techniques, some novel succinct sufficient criteria are obtained to ensure the addressed systems to achieve the preassigned-time synchronization by using the preassigned-time stability theory. The preassigned settling time is free from any parameter and any initial value of the system, and can be preset according to the actual task demands. Particularly, unlike the existing results, the proposed control methods can effectively avoid the chattering phenomenon, and the time delay part is removed for simplicity. Additionally, the projection coefficient is generic quaternion-valued instead of real-valued or complex-valued, and some of the previous relevant results are extended. Lastly, numerical simulations are reported to substantiate the effectiveness of the control strategies, the merits of preassigned settling time, and the correctness of the acquired results. [ABSTRACT FROM AUTHOR]

Cranes are widely employed for transportation in many industrial fields. However, strong couplings and high nonlinearities increase difficulty of designing effective control methods for crane systems. Additionally, complex environments may bring uncertainties and unfavorable factors for the control problem. In this paper, we design an effective tracking approach for double pendulum cranes considering variable rope lengths problem by using sliding mode technique, to further improve the robustness and achieve the control objectives of accurate tracking, while effective suppression for double pendulum swing is also ensured. We design a proper sliding mode surface containing all state variables, which effectively suppresses swing angles of the payload and the hook. Then, the time delay estimator technique is used to estimate parameter uncertainty-related terms and the estimation errors are effectively tackled by using sliding mode technique. The effectiveness is rigorously proved by Lyapunov stability theory. At last, comprehensive simulations are implemented to show the proper performance. • The effective tracking objective for the trolley and the rope is achieved. • The parameter uncertainty is successfully solved by sliding mode technique. • The swing suppression performance is theoretically proved. [ABSTRACT FROM AUTHOR]

The vehicle active suspension has attracted considerable attention owing to its great contributions to the vertical dynamics of vehicle. This paper investigates the non-fragile output feedback control problem of the uncertain vehicle active suspension with stochastic network-induced delay. Firstly, taking the variation of sprung and unsprung masses into consideration, an interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is introduced to describe the nonlinear characteristics of active suspension systems (ASSs). Secondly, to ensure that the control strategy is practicable when some states are unmeasured, a novel output feedback method is proposed by employing a variable substitution approach. Meanwhile, in order to approximate the real physical conditions of the control system, the gain perturbations are taken into account. Thirdly, with regard to the complexity of signal transmission delay in network control process, a more generalized lumped delay form is employed to represent the network-induced delay. Moreover, to describe the stochasticity of lumped delay, a Markovian process is introduced. Finally, both numerical simulations and experimental tests are carried out to examine the effectiveness and practicability of the proposed controller. [ABSTRACT FROM AUTHOR]

In the paper, we study the robust synchronization of complex dynamic networks (CDNs) with deviating arguments and parameter uncertainties via self-feedback control, the model involves both advanced and delayed arguments. In addition, based on the Gronwall inequality and inequality techniques, we derive upper bounds on the length of the arguments and the magnitude of the parameters, when the parameters and arguments of CDNs are below the upper bounds, the CDNs continue to exhibit exponential synchronization. In comparison to linear matrix inequalities and Lyapunov’s method in the existing literature, we obtain elaborate bounds. Finally, Several simple examples can demonstrate the effectiveness of the results.

This paper aims at deriving fire fragility curves for a prototype steel pipe-rack in an industrial plant subjected to localised fires. In particular, starting from a reference case study, uncertainties related to the structural capacity and the size of the localised fires caused by a hole in a tank or a hole in a pipe are included in the analyses. Thus, the influence of uncertainties in the derivation of the fragility functions was highlighted by comparing four sets of analyses in which both demand and capacity uncertainties were progressively introduced. Moreover, alongside the cloud analysis (CA), the suitability of the multiple stripe analysis (MSA) to build relevant probabilistic fire demand models was assessed. Fire fragility curves were derived by considering the interstorey drift ratio (ISDR) as engineering demand parameter (EDP) and by assessing different relevant intensity measures (IMs) that represent the severity of localised fires. It was found that by introducing uncertainties in the steel yield strength, lower probabilities to exceed the life safety and the near collapse limit states with respect to the reference case study were observed. Moreover, the inclusion of further uncertainties, described with continuous physically-based probability functions of the size of the fire diameter, affected the probabilistic models by lowering the probability of exceedance. These functions provide a more realistic description of the fire scenario, enabling a better representation of the structural vulnerability. For this case study, the CA exhibited better suitability for the derivation of fire fragility curves than the MSA. All the analysis results are thoroughly discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper discusses a class issue of finite-time lag projective synchronization (FTLPS) of delayed fractional-order quaternion-valued neural networks (FOQVNNs) with parameter uncertainties, which is solved by a non-decomposition method. Firstly, a new delayed FOQVNNs model with uncertain parameters is designed. Secondly, two types of feedback controller and adaptive controller without sign functions are designed in the quaternion domain. Based on the Lyapunov analysis method, the non-decomposition method is applied to replace the decomposition method that requires complex calculations, combined with some quaternion inequality techniques, to accurately estimate the settling time of FTLPS. Finally, the correctness of the obtained theoretical results is testified by a numerical simulation example. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, the quasi-consensus problem of the Markovian switching heterogeneous multi-agent system with cooperative–competitive interactions and parameter uncertainties is investigated. The switch of the agent's dynamics is combined with Markov processes, and the topologies among agents switch between cooperation and competition. Based on graph theory and Lyapunov function theory, the sufficient conditions are given to ensure the quasi-consensus of the Markovian switching multi-agent system. Finally, a numerical simulation is given to illustrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a new control strategy to deal with three different types of uncertainties for a class of time-varying delay nonlinear systems. Quite different from related celebrated works, the considered systems allow the existence of parameter uncertainties in the output function and nonlinearities, dynamic uncertainties of the unmeasurable state, and continuous external disturbances, which invokes the motivation of the paper. The objective is to construct a continuous output feedback controller by utilizing a new restructured integral function and the double-domination method. The novelty control design is the capability of tackling zero-dynamic and unknown output function simultaneously, thereby guaranteeing the state convergence of the closed-loop system. Finally, the proposed scheme is applied to a practical example and a numerical one simultaneously to demonstrate the effectiveness and the superiority. • This paper is an important theoretical work to deal with the output feedback stabilization problem of time-delay nonlinear systems, since it provides a new idea to handle parameter uncertainties in the output function and nonlinearities, dynamic uncertainties of the unmeasurable state, and continuous external disturbances. • This method overcomes the influence of dynamic/parametric uncertainties and time-varying terms, and provides an insight for the combination of output feedback stabilizer and state observer for uncertain control systems. • To eliminate the effect of dynamic uncertainties, a nondecreasing positive continuous function is used to construct an appropriate integral Lyapunov function in stability analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of fault-tolerant control of the preset performance of autonomous underwater vehicles (AUVs) in case of external disturbances and actuator failures. To begin with, a specific new nonlinear auxiliary function with dual performance constraints is designed to establish the foundation for achieving prescribed performance in the closed-loop system. Subsequently, an event-triggered fault tolerant controller employing Nussbaum gain technique is introduced to accomplish the precision trajectory tracking control of the AUVs by predetermining control index. It is shown that the effect by resulting in actuator faults is on-line compensated by an adaptive estimator. Theoretical analyses demonstrate the boundedness of the signals of the designed closed-loop control system, and the Zeno phenomenon can be effectively avoided. Finally, simulation experiments are conducted to verify the effectiveness of the proposed methods. • A novel nonlinear auxiliary function with dual performance constraints is proposed, which not only predetermines the performance of the error, but also directly constrains the rate of change of the error. • Combining the improved event triggering mechanism and the proposed sliding mode function, the designed controller guarantees the intended performance. • By utilizing an adaptive fault tolerance technique based on Nussbaum function, actuator faults and unknown disturbances are handled efficiently to enhance the robustness of the system. [ABSTRACT FROM AUTHOR]

Aiming at the semi-active control problem of interconnected air suspension, an interconnected mode switching control strategy is designed based on the variation law of comprehensive indexes under different interconnected modes. Considering the parameter uncertainties in the process of model linearization, a semi-active control strategy of vehicle interconnected air suspension considering parameter uncertainties is proposed based on robust control theory. Based on BP neural network method, the mapping relationship between spring force and air spring volume is established, and the tracking of the spring force is realized. The numerical simulation of air suspension semi-active control system considering parameter uncertainties is carried out to verify the effectiveness of the semi-active control system. The simulation and real vehicle test results show that the proposed control strategy can not only improve the suspension performance and have strong robustness to parameter uncertainties.

This paper focuses on event-trigger control of automotive suspension systems. Firstly, fuzzy T-S systems which suitable for automobile suspension systems are studied. Parameter uncertainties and disturbances are included in the fuzzy T-S systems. Secondly, based on linear matrix inequalities (LMIs) and Lyapunov function, the conditions for the stability of fuzzy T-S systems are given. In the meantime, the controller and observer of the fuzzy T-S systems are designed. Finally, the theory is applied to the automotive suspension systems in the simulation. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) is a promising technology for vehicle platoons to increase roadway capacity. This paper proposes a networked Lagrange system (NLS) based CACC dynamic model based on which a hierarchical anti-disturbance controller is developed to solve the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology. First, a hierarchical anti-disturbance controller for NLS is constructed which comprises three layers, these are, the adaptive smooth estimator-control layer, the distributed classifying amplitude-related layer, and the anti-disturbance control layer, in which the parameter uncertainties are estimated in the adaptive smooth estimator-control layer, the disturbance classification and distribute control are executed in the distributed classifying amplitude-related layer and the anti-disturbance control layer respectively. In addition, the proposed controller is extended to address the stability problem of CACC in vehicle platoons with time-varying parameter uncertainties, external disturbances, and directional dynamic communication topology conditions, which shows the versatility of the controller. Finally, comparison studies and simulation results are provided to demonstrate the effectiveness, significance, and advantages of the presented controllers. [ABSTRACT FROM AUTHOR]