This paper proposes a novel dynamic memory event-triggered predefined-time control method for stochastic nonlinear time-delay systems with unknown input hysteresis. Different from existing results, this study focuses on the predefined-time stability of stochastic nonlinear systems. In the design of the predefined-time controller, command-filter technology is integrated, and the impact of filter errors on system performance is effectively mitigated. Considering the significant effect of historical data of unknown input Bouc-Wen hysteresis signals on current input signals, a dynamic memory event-triggered controller is proposed to enhance control accuracy. Simulations are conducted to validate the performance of the proposed control method.

During tunnel excavation, designing a trajectory tracking controller based solely on a pure rigid body model is insufficient to achieve optimal section forming quality. Because when the cutting head is subjected to complex coal-rock loads, the presence of hydraulic oil elasticity will inevitably arise system vibration, further causing roadway over-excavation, ultimately deteriorating section quality. Hence, a rigid-flexible coupling model of the cutting mechanism, considering hydraulic system stiffness and damping, is first established in this article, and it is further decomposed into a slow-varying subsystem (SVS) and boundary layer subsystem (BLS) using singular perturbation theory for independent design of controllers. Secondly, a new time scale function is proposed for designing a predefined-time (PT) extended state observer, achieving estimation of the disturbance term suffered by the cutting system within a PT for the first time. Then, a PT dynamic surface controller with tunable convergence time is developed for SVS. A PT state constraint controller is designed for BLS to suppress vibration acceleration. Ultimately, through comparative experiments on a roadheader prototype, it is verified that the constructed composite controller can effectively attenuate system vibration while tracking cutting trajectory within a PT, thereby significantly improving section quality and beneficially extending key component lifespan.

This paper presents a learning-enhanced predefined-time adaptive optimal control strategy for a quadrotor unmanned aerial vehicle subject to disturbances. First, a predefined-time disturbance observer with a tunable convergence time bound is developed to ensure rapid and accurate estimation. Within a command filtered backstepping architecture, actor-critic neural networks are incorporated to achieve adaptive optimal control with learning capability for both position and attitude subsystems. Specifically, novel learning laws facilitate the rapid online updating of network weights, where the critic network approximates the value function while the actor network optimizes the control policy to minimize control cost. The proposed framework effectively compensates for disturbances and filtered error effects, ensuring that all tracking errors converge within a predefined time. Rigorous analysis establishes the predefined-time stability of the closed-loop system. Finally, comparative simulation results are provided to demonstrate the effectiveness of the proposed strategy.

This paper proposes a disturbance observer-based adaptive finite-time singular perturbation control scheme for flexible joint manipulators with state constraints. Firstly, a fuzzy logic system-based observer is designed to estimate unknown external disturbances. Then, a fuzzy adaptive finite-time singular perturbation controller is developed to address model uncertainties and improve the response speed of the rigid subsystem. In particular, the singular perturbation method avoids the design of unnecessary virtual control laws and error compensation signals by decoupling the original system into the reduced-order rigid and fast subsystems, which reduces the computational burden. Stability analysis verifies that the closed-loop signals converge within finite time, while ensuring that all states of the rigid subsystem remain within constraint bounds. Finally, the effectiveness of the proposed control scheme is demonstrated by simulation results.

Accurate and fast detection of traffic signs is critical for autonomous driving, particularly in complex environments with diverse sign scales and varying detection distances. Existing approaches, incorporating attention modules or modifying detection heads, frequently encounter high rates of false positives and omissions due to the increased sampling depth. To address these limitations, we propose MDSF-you only look once (YOLO), a novel detection framework that integrates multiscale sequence fusion (MSF) for synergistic feature integration across granularities, enhancing the precision of both localization and semantic information fusion. Additionally, our dilated-wise residual (DWR) module leverages dilated convolutions and channel-wise reparameterization to improve fine-grained feature extraction. The architecture further introduces a $P_{2}$ detection head for shallow features and fully decouples all detection heads, optimizing target localization and category identification. Extensive experiments on the TT100K and CCTSDB2021 datasets demonstrate the superiority of MDSF-YOLO over benchmark models, including YOLOv11s, with significant improvements in mAP by 8.8% and 2.4% on respective datasets while substantially reducing false positives and leakage rate. Besides, the marked improvement of MDSF-YOLO on the VisDrone2019 dataset verifies its enhanced capability to address drone-based object detection. These advances underscore the efficiency and robustness of the proposed model, providing a promising solution for autonomous driving and similar object detection scenarios.

This article investigates iterative learning control of robot arms with initial errors and actuator faults. Resorting to backstepping technique, an actual controller that removes the iterative convergemt sequence is constructed to simplify the design. New updating laws with equilibrium factor are developed to treat the nonstrictnegative difference of composite energy function. A contraction mapping-based composite energy function method is executed to provide the convergence of errors. Finally, the validity of the proposed method is verified through a robot arm example.

This article is devoted to data-driven optimal control of linear discrete systems with sensor fault via a performance triggering approach. A quadratic inequality is introduced to equivalently describe systems subject to a fault. A barrier function is provided by the input and output to treat the gain constraint. An optimal control law is built on the adaptive dynamic programming (ADP) method to improve control performance. A dynamic triggering mechanism is constructed by instantaneous data and performance index to balance triggering frequency and control performance, especially under an emergent situation. Sufficient conditions are supplied to ensure the ultimately uniform boundedness of the closed-loop systems. An illustrative example is presented to verify the validity of the proposed strategy.

Three-phase inverters have been widely implemented for stand-alone power conversion applications where the utility grid is not available. In these applications, high-quality output voltage regulation of inverters is crucial for the reliable operation of local loads. However, critical load conditions (e.g., unbalanced loads, nonlinear loads, and load variations) bring time-varying load disturbances, deteriorating the steady-state and transient performance of the output voltage. To address this issue, a finite-time adaptive control (FTAC) with a cascade structure is proposed in this article. Firstly, novel adaptive laws are designed to estimate time-varying load disturbances. By incorporating the designed adaptive laws, cascade finite-time controllers are then constructed for both the outer voltage loop and inner current loop. The stability analysis shows that the voltage tracking errors tend to an arbitrarily small neighborhood of zero within a finite time, enabling fast and accurate output voltage control of stand-alone inverters under load disturbances. Meanwhile, all the signals in the closed-loop system are bounded. Simulation and experiment results validate the effectiveness and superiority of the FTAC strategy.

This study focuses on solving the finite-time distributed optimization issue of fractional-order multiagent systems (FOMASs) over unbalanced directed graphs (digraphs) that are subject to disturbances. Each agent in the FOMASs has a local cost function that is only available to itself, which may or may not be convex and quadratic. To address this challenge, the study proposes a fully distributed gradient-sum-estimation (GSE) optimization algorithm as a continuous control law, which comprises three parts. In the first part, a disturbance estimator term is proposed for each agent to estimate its disturbance within a finite time. In the second part, a sliding-mode control (SMC) term is presented to ensure that all agents reach the sliding surface within a finite time. In the last part, a novel GSE-based optimization term is constructed to capture the global optimal solution within a finite time. The GSE is fully distributed and used for estimating the sum of all gradients within a finite time. This fully distributed GSE optimization algorithm has zero-error finite-time convergence, disturbance rejection, and chatter avoidance properties. Finally, the study verifies the validity and superiority of the proposed GSE optimization algorithm by comparing some graphical simulation results.Note to Practitioners—Due to the presence of disturbances in actual industrial systems, the distributed optimization problem of disturbed FOMASs is studied in this study, which can be applied to energy consumption optimization, parameter estimation and scheduling, economic dispatch, and distributed energy resources optimal in power systems. Our study proposes a novel completely distributed fractional-order controller that is endowed with the properties of disturbance rejection, zero-error finite-time optimal convergence, and chatter avoidance. The commonly existing limitation that each local cost function is convex or quadratic hinders its implementation in real-world scenarios. We address this limitation and broaden the scope of local cost functions to include nonconvex and nonquadratic functions. In addition, by comparing the simulation results, the fractional-order controller of FOMASs has superior steady-state and transient performance over the conventional first-order MASs controller, such as faster convergence and lower energy consumption. As a result, the proposed fractional-order controller is more in line with practical engineering applications.

This paper is dedicated to a dynamic event-triggeredH∞filtering method of fuzzy Markov jump systems via a mismatched quantization scheme. The system outputs are triggered by a dynamic event-triggered mechanism and then quantized via a mismatched quantizer before being sent to the remote filter. The dynamic triggering scheme with a special diagonal matrix structure threshold is built to reduce the network burden. The quantizer is constructed in a multi-channel paradigm with a time-varying mismatch degree. Then, the remote reduce-order filter is designed to be both fuzzy-rule and mode-dependent. By adopting Finsler's Lemma and the vertex separation method, sufficient conditions are derived in terms of form matrix inequalities. At last, the effectiveness of the proposed method is demonstrated by a tunnel diode circuit.

The paper addresses the fault-tolerant optimized control of switched complex networks with unknown state and actuator fault. A proportional-integral intermediate observer is constructed to estimate unknown elements by relaxing known boundary requirement. An optimized controller is proposed to achieve fault-tolerant synchronization via adaptive dynamic programming scheme. A critic neural network is employed to solve the value of the Hamilton–Jacobi–Bellman equation. Sufficient conditions are established to ensure the uniformly ultimately bounded synchronization performance. Finally, an example is simulated to deliver the effectiveness of the proposed approach.

This article addresses synchronization control of switched complex network with unknown state and actuator fault. A mode-dependent proportional-integral intermediate observer is explored to estimate unknown elements with high-estimation accuracy. A hybrid controller is constructed to treat the asynchronous occurrence of impulses and switching moments. With the help of mode-dependent average dwell time and mode-dependent average impulsive interval, a mode-dependent criterion is established to guarantee the uniformly bounded synchronization performance. Two examples are simulated to deliver the effectiveness of the proposed method.

Neural network adaptive iterative learning control (ILC) is developed in this article to treat strict-feedback nonlinear systems with unknown state delays and input saturation. These delays are treated by constructing the Lyapunov-Krasovskii (L-K) functions for each subsystem. A command filter is employed to avoid the derivative explosion caused by continuous differentiation of the virtual controller. Corresponding auxiliary systems are designed and integrated into the backstepping procedure to compensate input saturation and the unimplemented part of the filter. Hyperbolic tangent functions and radial basis function neural networks (RBF NNs) are employed to treat singularity and related unknown terms, respectively. The convergence of the resultant strict-feedback systems is ensured in the framework of composite energy function (CEF). Finally, a simulation example is adopted to substantiate the validity of the proposed algorithm.

This article is dedicated to a fault-tolerant synchronization control method of complex networks (CNs) via an average observer. A projected operator matrix is constructed to reduce the dimension of CNs by clustering and aggregation. An average observer with derivative and integral terms is built to estimate unknown elements with high estimation accuracy. The fault-tolerant controller is a composite form of state-feedback and the estimated faults. Sufficient criterions are set up to guarantee the uniformly ultimately bounded synchronization of the resultant close-loop system. Finally, an example is simulated to deliver the effectiveness of the proposed approach.

This article aims to study event-triggered data-driven control of nonlinear systems via Q-learning. An input-output mapping is described by a pseudo-partial derivatives form. A Q-learning-based optimization criterion is provided to establish a data-driven control law. A dynamic penalty factor composed of tracking errors is supplied to accelerate errors convergence. Consequently, a novel triggering rule related to this factor and performance cost is proposed to save communication resources. Sufficient conditions are developed for guaranteeing the ultimately uniform boundedness of the resultant tracking errors system. Two simulation studies are executed to verify the effectiveness of the presented scheme.

This article presents an event-triggered adaptive acrlong NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The acrlong ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.

In this article, we investigate an improved dynamic guaranteed cost event-triggered-based anti-disturbance control for Takagi–Sugeno fuzzy wind-turbine systems subject to external disturbances. A guaranteed cost event-triggered paradigm with dynamic threshold and sector structure is constructed to alleviate unnecessary triggers caused by outlier measurement. An additional event condition is designed to deal with the difference of premise variable between the system and controller. A PI-type intermediate estimator is introduced to simultaneously estimate the system state and external disturbance. Subsequently, an event-triggered fuzzy controller is built to actively compensate the external disturbances. With the help of Finsler's lemma, sufficient criteria are derived in terms of linear matrix inequalities to make the wind-turbine systems asymptotically stable. Finally, the proposed method is verified by comparative studies.

The widely studied finite/fixed-time control guarantees fast convergence of the controlled systems. Yet, the adjustment of settling time remains complex, and the optimality of control signal is not considered. In this article, a predefined-time optimal tracking control scheme is proposed for uncertain nonlinear systems with input saturation. With the aid of fuzzy approximation, the reinforcement learning actorcritic structure is established, in which the actor and critic network are used to implement control actions and evaluate execution costs, respectively. Then, by introducing the actorcritic structure into the command filtered backstepping design framework, the approximated optimal control signals containing the predefined-time parameter are derived, and an easily tunable upper bound on the settling time with respect to the predefinedtime parameter is obtained. With the approximation of saturated nonlinearity using tanh function, the input saturation constraint is satisfied. Stability analysis proves that all signals in the closedloop system can converge to a small neighborhood near the origin in a predefined time. Eventually, comparative simulations on quadrotor attitude system are carried out to assess the validity of the developed control strategy.

In this paper, a discrete-time adaptive fuzzy command filtered backstepping control scheme is presented for the altitude and attitude control problems of the quadrotor unmanned aerial vehicle (UAV). The discrete-time controller is designed by using a novel command filtered backstepping method based on the discrete-time system model of the quadrotor, and model uncertainties are handled by using adaptive fuzzy control. Furthermore, the problem of causality is solved by utilizing the first-order filters. The effectiveness of the proposed control scheme is demonstrated by simulation and experiment.

This brief presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation based on the adaptive neural network (NN) technique. First, the virtual control functions are reconstructed to address the uncertainties in subsystems by using command filters. A piecewise continuous function is employed to deal with the unknown input saturation problem. Next, an event-triggered tracking controller is developed by utilizing the adaptive NN technique. Compared with standard NN control schemes based on multiple-function-approximators, our controller only requires a single NN. The closed-loop system stability is analyzed based on the Lyapunov stability theorem, and it is shown that the Zeno behavior is also avoided under the designed event-triggering mechanism. Simulation studies are performed to validate the effectiveness of our controller.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

In this article, the synchronization issue of certain edge-colored networks is investigated. We start with a linear subspace determined by the colored edges and show that the invariant kernel of this linear subspace is precisely consists of all the synchronous states. Moreover, this invariant kernel is characterized by a cluster of algebraic equations. Especially, for network described by a polynomial vector field, due to the property of Noetherian rings, this invariant kernel can be determined by a finite number of algebraic equations. Furthermore, the equivalence between network synchronization and the asymptotic behavior of the aforementioned invariant kernel are proved. Based on the colored edges and the invariant kennel, we decompose the original network twice, arriving at a three-layer network: the first two layers, referred to as the external part, can only synchronize to an equilibrium point, while the third layer, known as the internal part, possesses the same colored edges. For this three-layer network, we construct two Lyapunov-type functions for the external part and the internal part, respectively, to establish the synchronization criteria. In particular, our criteria involve linear matrix inequalities and polynomial inequalities of smaller scale, which can be solved by the existing semi-definite programming tools. Finally, this article provides three examples to illustrate the effectiveness and advantages of the theoretical results presented.

Outlier detection is a challenging problem due to the complexity of real-life data. Specifically, an effective outlier detection method should be able to handle (1) different types of outliers: local outliers, global outliers, and cluster outliers; (2) a lack of prior knowledge of outlier number or percentage; (3) neighboring clusters with different densities; and (4) real-time detection. Unfortunately, few algorithms can tackle all these challenges simultaneously. In this paper, we propose a chain-based theory to address these issues, where a minority of data points will be considered outliers if their distances from normal data points change abruptly. We present the parallel chaining method based on this theory. Experiments demonstrate that the proposed method exhibits comparable performance to other state-of-the-art methods in the synthetic datasets and outperform other methods in the real-life datasets.

This article is concerned with the stabilization problem of switched Takagi–Sugeno fuzzy systems via the stabilizing switching behavior. Different from the situations considered in the existing literature, the stabilizing-switching-dependent average dwell-time method is applied to break through the limitation of the noncoexistence of dwell-time-dependent and dwell-time-independent destabilizing switching behavior. Then, the stability criteria are established by balancing the state divergence produced from the unstable mode and destabilizing switching behavior through the stabilizing switching behavior. The obtained results are suitable for the situation where all modes are unstable and applied to the design of the state feedback controller. The designed controller relaxes the stabilization assumption for the mode of the fuzzy system. Finally, the simulation illustrates the feasibility and superiority of the proposed method.

This article presents a modified command filter backstepping tracking control strategy for a class of uncertain nonlinear systems with input saturation based on the convex optimization method and the adaptive fuzzy logic system (FLS) control technique. First, the effect of complex uncertainties is eliminated by introducingncommand filters and a single FLS. Then, the update laws of FLS weights are designed based on the convex optimization technique. Next, a new piecewise continuous function is employed to deal with the input saturation problem. The closed-loop system performance is also analyzed using the Lyapunov stability theorem and the Lasalle invariant principle. Finally, the simulation and experimental results are presented to show the effectiveness of our controller.

This article studies the stability and stabilization of Takagi–Sugeno (T–S) fuzzy time-delay systems under sampled-data control via a new asymmetric Lyapunov–Krasovskii functional (LKF) method. The method improves the common symmetric basic functional term. Besides, we improve the looped-functional term and the discontinuous functional term through combining the integral about time delay and each sampling interval, respectively. Based on the abovementioned new approaches, we present the stability and stabilization results with less conservativeness than the literature through linear matrix inequalities. In the process, we introduce a simplification approach for stabilization of sampled-data control, which effectively reduces the computational complexity of results. At last, we provide three examples to verify the effects and advantages of our results.

This article is concerned with passivity analysis of neural networks with a time-varying delay. Several techniques in the domain are improved to establish the new passivity criterion with less conservatism. First, a Lyapunov-Krasovskii functional (LKF) is constructed with two general delay-product-type terms which contain any chosen degree of polynomials in time-varying delay. Second, a general convexity lemma without conservatism is developed to address the positive-definiteness of the LKF and the negative-definiteness of its time-derivative. Then, with these improved results, a hierarchical passivity criterion of less conservatism is obtained for neural networks with a time-varying delay, whose size and conservatism vary with the maximal degree of the time-varying delay polynomial in the LKF. It is shown that the conservatism of the passivity criterion does not always reduce as the degree of the time-varying delay polynomial increases. Finally, a numerical example is given to illustrate the proposed criterion and benchmark against the existing results.

We develop a modified adaptive control scheme for uncertain nonlinear systems based on command-filtered backstepping in this study. Our main task is to construct the virtual stabilizing functions in the presence of the uncertain control gain functions. First, the command-filter technique is employed to predict the system performance. Next, a new adaptive control strategy is introduced to stabilize each subsystem. In the final step, the actual stabilizing function is designed by utilizing the hyperbolic tangent function. The proposed strategy overcomes the problem of the input saturation and guarantees the convergence of all the system signals. The simulation study for a numerical nonlinear system and experimental results from a PMSM control platform are presented to validate our control strategy.

This article investigates fault detection (FD) for discrete-time T–S fuzzy systems via an iterative interval estimation method. By means of system output and the iterative estimation of unknown disturbances, two iterative subsystems are employed to establish iterative state reconstruction free of faults. Resorting to a structure separation technique and the$H_{\infty }$requirement imposed on estimated errors, a sufficient condition is formulated in terms of linear matrix inequality to guarantee the asymptotically stability of the error systems. With the help of the zonotope reachability technique, the state interval without faults consideration is rebuilt in terms of the error boundary. Subsequently, an FD scheme is proposed by checking residual signals whether exceed the residual interval generated from the established error interval. Simulation comparison is provided to verify the validity of the proposed iterative FD scheme.

This article deals with the adaptive neural network (NN) finite-time (FT) command-filtered tracking control problem for a class of nonlinear systems with time-varying full-state constraints. Based on the asymmetric time-varying barrier Lyapunov functions (TVBLFs), the issue of time-varying full-state constraints is settled. The influence of unknown items in the system can be eliminated by the adaptive NN control method. Moreover, the improved FT command filter is introduced to relax the restriction on the input signal and solve the explosion of complexity (EOC) problem. Meanwhile, the FT error compensation mechanism is developed to eliminate the influence of filtering error. It is shown that the proposed strategy can guarantee FT boundedness of all the signals in the closed-loop system and FT convergence of the tracking error. An example verifies the effectiveness of the proposed control method.

The stability of neural networks with a time-varying delay is studied in this article. First, a relaxed Lyapunov-Krasovskii functional (LKF) is presented, in which the positive-definiteness requirement of the augmented quadratic term and the delay-product-type terms are set free, and two double integral states are augmented into the single integral terms at the same time. Second, a new negative-definiteness determination method is put forward for quadratic functions by utilizing Taylor's formula and the interval-decomposition approach. This method encompasses the previous negative-definiteness determination approaches and has less conservatism. Finally, the proposed LKF and the negative-definiteness determination method are applied to the stability analysis of neural networks with a time-varying delay, whose advantages are shown by two numerical examples.

In this article, we search for polynomial Lyapunov functions beyond the quadratic form to investigate the synchronization problems of nonlinearly coupled complex networks. First, with a relaxed assumption than the quadratic condition, a synchronization criterion is established for nonlinearly coupled networks with asymmetric coupling matrices. Compared with the existing synchronization criteria, our results are less conservative and have a wider application. Second, the synchronization problem for polynomial networks is characterized as the sum-of-squares (SOS) optimization one. In this way, polynomial Lyapunov functions can be obtained efficiently with SOS programming tools. Furthermore, it is shown that the local synchronization of certain nonpolynomial networks can also be analyzed by using the SOS optimization method through the Taylor series expansion. Finally, three numerical examples are presented to verify the effectiveness and less conservatism of our analytical results.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering errors caused by the used FTCFs are removed by using the error compensation mechanism (ECM). The proposed virtual control signal, the control torque, and the adaptive updating law can guarantee the set tracking errors converge to an adjustable neighborhood of the origin in finite time in the presence of uncertain parameters. Because the virtual control signal and ECM only use the local information, the established method is completely distributed. Two simulation examples are given to show the effectiveness of the proposed scheme.

This article is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and system states, a sequence of proportional–integral observers are established via the system outputs. With the help of a structure separation technique, the proportional–integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping-based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniformly ultimately bounded. Simulations are provided to verify the effectiveness of the proposed methods.

This article presents a new asymmetric Lyapunov–Krasovskii functional method on the stability and stabilization of Takagi–Sugeno fuzzy systems with time delay. For the reduction of conservativeness, combining the method with the membership-function-dependent approach, we propose a novel delay-dependent stability condition in the form of linear matrix inequalities. Based on the condition, we further obtain a novel condition of stabilization. At the end, we provide two numerical examples to verify the advantage of the stability and stabilization approaches, respectively.

This article presents a rolling optimization algorithm (ROA) for minimizing total vehicle delay for real-time traffic signal control in urban road networks, where the vehicle delay minimization problem is formulated as a quadratically constrained quadratic programming problem, which is NP-hard. The programming problem is relaxed and resolved using the ROA in a distributed manner. In particular, we introduce network partition to a large-scale urban road network and then present a regional ROA to eliminate the low efficiency caused by the large number of decision variables. Numerical experiments are performed on one of Beijing’s district road networks to validate the efficiency of the proposed method in benchmark against several typical techniques.

This paper is concerned with the motion control for a quadrotor with a cable-suspended load (QCSL). A fixed-time control strategy is presented to improve the transient response and robustness of the QCSL with external disturbance. The overall control scheme is designed with a cascade structure to better cope with the underactuated property of the QCSL and the indirect effect of the control force on the load’s velocity through the tensile force on the cable. The simulation results are given to demonstrate the performance of the proposed scheme. Furthermore, actual flight tests were performed on a new experimental QCSL to validate the effectiveness of the proposed control strategy.

In this article, the problem of finite-time adaptive fuzzy tracking control for multi-input and multi-output (MIMO) nonlinear systems with input saturation is investigated. The new finite-time command filter is introduced for generating command signals and their derivatives to work out the matter of “explosion of complexity,” and the modified fractional power-based error compensation mechanism (ECM) serves as removing the effect of filter error. Then, the finite-time adaptive control scheme is established via the backstepping recursive design technique. It guarantees all the signals of the closed-loop system (CLS) are finite-time bounded while the output tracking errors are regulated to a sufficiently small neighborhood of the origin in finite time. Finally, the effectiveness of the proposed finite-time control scheme is verified by a numerical comparison example.

Target search with multiple robots has attracted widespread attention for its numerous applications,$eg$., surveillance, reconnaissance and environmental exploration. In this paper, we propose a heuristic target search scheme for multi-robot, considering the prior information of targets is unreliable. To tactfully capture the multi-peak characteristics of the probability distribution map (PDM) of each target, we introduce the concept of high confidence area (HCA) based on the Gaussian mixture model. Then, a coordinated search method consisting of task allocation and path planning is designed to achieve efficient search performance. The main novelty of our method is twofold. First, the probability information of multi-peak is sufficiently captured by HCA and evaluated by reliability degree. Second, target allocation and path planning are designed coordinately, which dynamically update with real-time status and alleviate misleading effects even when PDM is not reliable, thereby largely reducing search time. Extensive contrastive simulations demonstrate that the HCA-based search method outperforms two other existing methods.

This paper deals with the H∞adaptive sliding mode fault tolerant control problem for uncertain Takagi-Sugeno (T-S) fuzzy fractional order systems (FOSs) of fractional order 0 <; α <; 1 with mismatched disturbances. Adaptive laws are designed to estimate the upper bounds of the nonlinear terms. A sliding surface with reduced dimension is constructed by the method of state transformation. A sufficient condition in terms of linear matrix inequalities (LMIs) is established which guarantees the stability of the sliding motion. Then, a new control law is designed to make the resulting control system reach the sliding surface in a finite time. Both state and output feedback control forms are addressed. The proposed methods are illustrated by numerical simulations.

The papers in this special section focus on machine learning for artificial intelligent-enhances healthcare and medical services. These services are always among the top concerns for humans, especially under the special situation of COVID-19 pandemic, started from early 2020. In the field of computational biology and bioinformatics, scientists seek various possibilities using computer technologies, especially artificial intelligence (AI) enhanced methods, for healthcare services and medical diagnoses. For example, over the past few years, scientists have been working hard to identify the internal relationships between gene microarrays, cells, tissues, organisms, diseases, etc., and apply the AI, machine learning and deep learning technologies looking for more innovative solutions for new diseases, such as COVID-19. In fact, nowadays, AI technology, such as the convolutional neural network (CNN), is considered has one of the most important computer technologies and has been widely applied in the fields of healthcare engineering, medical research, disease diagnosis, cancer/tumor analysis and etc.

This paper is concerned with the problem of reachable set estimation for discrete-time Markovian jump neural networks with generally incomplete transition probabilities (TPs). This kind of TP may be exactly known, merely known with lower and upper bounds, or unknown. The aim of this paper is to derive a precise reachable set description for the considered system via the Lyapunov-Krasovskii functional (LKF) approach. By constructing an augmented LKF, using an equivalent transformation method to deal with the unknown TPs and utilizing the extended reciprocally convex matrix inequality, and the free matrix weighting approach to estimate the forward difference of the constructed LKF, several sufficient conditions that guarantee the existence of an ellipsoidal reachable set are established. Finally, a numerical example with simulation results is given to demonstrate the effectiveness and superiority of the proposed results.

Recent developments in the field of neural networks, adaptive learning, and control will enable autonomous vehicles to operate in complex environments including urban, rural, and dangerous environments.

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal. In addition, the input saturation problem is dealt with by introducing the auxiliary system. Overall, it is shown that the designed controller drives the output tracking error to the desired neighborhood of the origin at a finite time and all the signals in the closed-loop system are bounded at a finite time. Two simulation examples are given to demonstrate the control effectiveness.

In this paper, the synchronization problem of complex networks with linearly diffusively coupled nonidentical nodes is investigated. Starting with the boundedness condition of network trajectories, we introduce an invariant set such that it contains all limit points of ultimately synchronous trajectories. Then, we develop a decomposition technique for the heterogeneous network. With this decomposition, the synchronization of the network can be investigated by the convergence of one decomposed network and the synchronization of the other decomposed homogeneous-like network. Moreover, for a particular case that the invariant set is a linear subspace, conditional synchronization analysis is provided to reduce the coupling complexity between the two decomposed networks. It is noted that our decomposition technique is quite simple yet general: by this technique, the synchronization of various heterogeneous complex networks can be transformed into the stability of nonlinear systems and synchronization of homogeneous-like complex networks. Finally, we present several numerical examples to demonstrate the effectiveness of the theoretical results. In particular, we use an example to show that our theoretical procedure is also feasible for some heterogeneous networks with a general invariant submanifold instead of linear subspace.

Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively. Note that, to cover the widest range of people, the time and monetary costs of CKD diagnosis have to be minimized with fewest patient tests. Thus, the reduced model using fewer features is desirable while it should still maintain high performance. To this end, the set-theory based rule is presented which combines a few feature selection methods with their collective strengths. The reduced model using about a half of the original full features performs better than the models based on individual feature selection methods and achieves accuracy, sensitivity and specificity, of 1.000, 1.000, and 1.000, respectively.

This article studies the problem of the fuzzy H∞filter design for nonlinear networked control systems through eventtriggered communication (ETC) scheme. First, a novel adaptive ETC scheme is given to determine whether the sampled measurement output should be released to communication network or not. Consequently, less communication resources are occupied under the desired H∞performance. Second, augmented fuzzy lineintegral Lyapunov function is introduced in the H∞performance analysis of filter error systems, such that the information of time derivative of membership functions are fully considered to reduce the conservativeness of networked fuzzy filter design. Different from the existing results, the upper bounds of time derivative of membership functions need not to be known prior. Third, the resulting filter error system is modeled as time-delay system under ETC mechanism and asynchronous premise in a unified framework. As a result, applying Lyapunov theory and inequality technique, new sufficient condition is obtained to meet the H∞performance for the filter error systems. Further, the corresponding filter and event-triggering parameters are codesigned and solved by a set of linear matrix inequalities. Finally, two examples are offered to demonstrate the advantage of the proposed method.

Nowadays, production-oriented manufacturing is transforming to service-oriented one, which leads to an increasing demand for high reliability of manufacturing systems. However, a popular approach to measuring terminal reliability of complex networked systems is based on graph theory, which has been shown to be an NP-hard problem. Though the NP-hard problem can be avoided by employing a statistical measure method for terminal reliability of random networks based on percolation theory, there is still lack of a general assessment approach to calculating terminal reliability of heterogeneous complex networks in intelligent manufacturing. In this paper, we propose a novel fractal-based approach to measuring the terminal reliability of heterogeneous networks. With help of the renormalization procedure that coarse grains a network into boxes containing nodes within given lateral size and inverse renormalization, which gives a fractal network growth model, a fractal network approximation of an arbitrary complex network is obtained. This fractal network topology can be described by a superposition of fractal elements based on fractal theory. Following this description, terminal reliability is the function of reliability of fractal elements. Then, a reliability assessment algorithm with computational complexity O(N2) based on fractal elements is developed. Numerical simulation is performed on a real network and a fractal network to validate the effectiveness of our method.

The nine papers in this special section focus on Big Data analytics in intelligent manufacturing systems. These systems can automatically adapt to changing environments and varying process requirements with minimal supervision and assistance from operators. It is essentially a cyber–physical production system that has enhanced intelligence due to learning, reasoning, adaptation, and decision making. The success of intelligent manufacturing relies on the timely acquisition, distribution, and utilization of various types of data from machines, manufacturing process, and products. The efficient use of big data can enhance the intelligence and automation of manufacturing process, provide high quality products and just-in-time production, and increase productivity and reduce costs. For example, by analyzing the factory floor data, equipment monitored data, and the enterprise manufacturing database, it could help to store, explore, and make complex decisions for the manufacturing system. While these big data topics have been widely discussed in the public media and the theory has been rigorously treated by statisticians and computer scientists from academia, little has been explored in the manufacturing research community from an engineering point of view. This special section aims to bridge the gap, and provides a platform for the communities to report recent findings and emerging research developments in the field.

This paper is concerned with the regularization and stabilization problems for rectangular discrete-time fuzzy systems with time delay. A dynamic compensation is designed to ensure that the close-loop system is square, and a necessary and sufficient condition is proposed to guarantee the existence of a dynamic compensation with which the close-loop system is regular and causal. Moreover, sufficient conditions are derived in terms of bilinear matrix inequalities to guarantee the admissibility of the closed-loop system. We present an efficient algorithm which is proved to be convergent to solve the conditions. Two examples are given to show the effectiveness and efficiency of the proposed method.

This paper addresses the exponential synchronization problem for neural networks with time-varying delays. First, a novel controller is presented by combining intermittent control with dynamic output feedback control. Next, a sufficient criterion is established based on the Lyapunov-Krasovskii functional approach and the lower bound lemma for reciprocally convex technique to ensure exponential stability of the resultant closed-loop system. Then, some solvable conditions of the proposed control problem are derived in terms of linear matrix inequalities. Notably, our results here extend the existing ones to the relaxed case because the derivative of time-varying delays is now an arbitrary bounded real number. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed method.

An increasing number of cities are implementing bicycle sharing systems to reduce traffic congestion. Determining the locations of bicycle stations is one of the fundamental challenges in planning of such systems. This paper provides a novel solution on it. The min-plus algebra is introduced to model transport systems for accessibility analysis. A unified model in the sense of the min-plus algebra for an integrated system with both buses and bicycles is presented to dynamically describe the state transitions of passengers in the system. A new accessibility is proposed with regards to a general index ω such as the geographical distance and the travel time. A necessary and sufficient condition on the accessibility is then provided. The minimization of bicycle stations under the accessibility is formulated to be a 0-1 integer programming problem. The case studies on two cities, Ningbo and Hangzhou, were performed, which show that compared with the current layouts of urban transportation networks, the proposed public bicycle sharing systems have remarkable advantages in topological characteristics and robustness against failures.

In modern manufacturing, retrieval and reuse of the pre-existed three-dimensional (3-D) computer-aided design (CAD) models would greatly save time and cost in the product development cycle. For the 3-D CAD model retrieval, one is confronted with the quality of searching in large databases with models in complex structure and high dimension. This paper proposes a new 3-D model matching approach that reduces the data dimension and matches the models effectively. It is based on diffusion maps which integrate the random walk and anisotropic kernel to extract intrinsic features of models with complex geometries. The high-dimensional data points in diffusion space are projected into low-dimensional space and the low-dimension embedding coordinates are extracted as features. They are then used with the Grovmov Hausdorff distance for model retrieval. These coordinates could capture multiscale spectral properties of the 3-D geometry and have shown good robustness to noise. In the experiments, the proposed algorithm has shown better performance compared to the celebrated eigenmap approach in the 3-D model retrieval from the aspects of precision and recall.

This paper is concerned with the asynchronous state estimation for a class of discrete-time switched complex networks with communication constraints. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the topology/coupling information. Also, the event-based communication, signal quantization, and the random packet dropout problems are studied due to the limited communication resource. With the help of switched system theory and by resorting to some stochastic system analysis method, a sufficient condition is proposed to guarantee the exponential stability of estimation error system in the mean-square sense and a prescribed performance level is also ensured. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.

This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables. Moreover, a delay-product-type term is introduced for the first time into the Lyapunov function candidate such that a delay-variation-dependent stability criterion with the bounds of delay change rate is established. Finally, the advantages of the proposed criteria are demonstrated through two numerical examples.

This paper proposes a new method for global optimization through randomized group search in contracting regions. For each iteration, a population is randomly produced within the search region, where the population size is chosen to ensure that the empirical optimum is an estimate of the true optimum within a predefined accuracy with a certain confidence. Fitness values are evaluated at the samples in the population. A very small subset of them with top-ranking fitness values are selected as good points. Neighborhoods of these good points are used to form a new and smaller search region, in which a new population is generated. It is easy to implement the algorithm. Extensive simulation on benchmark problems shows that the proposed method is fast and reasonably accurate.

The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low rank and sparse matrices, and further recast as a semidefinite programming problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low-rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers, and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered "clean" data from the proposed method can give much better parameter estimation compared with that based on the raw data.

This paper is concerned with the nonfragile distributed H∞filtering problem for a class of discrete-time Takagi-Sugeno (T-S) systems in sensor networks. Additive filter gain uncertainties that reflect imprecision in filter implementation are considered. Based on the robust control approach, sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed H∞performance level and the eigenvalues of the filtering error system in a given circular region. The filter parameters are determined by solving a set of linear matrix inequalities. A simulation study on the nonlinear tunnel diode circuit system is presented to show the effectiveness of the proposed design method.

This paper is concerned with the H∞filtering for networked systems with multiple time-varying transmissions and random packet dropouts. We design a remote H∞filter for these two networked issues such that the filtering error system is exponentially stable and achieves a prescribed H∞performance level. A switched system approach is used to model the multiple time-varying transmission process, and a set of stochastic variables are employed to describe the random packet dropout phenomenon. By the switched system theory and some stochastic analysis methods, a sufficient condition for the existence of the H∞filter is derived in terms of linear matrix inequalities (LMIs). Moreover, the filter gains are determined by solving an optimization problem. Two numerical examples are given to illustrate the effectiveness of the proposed design method.

This note aims to point out one typographical error and one calculation error in the above paper (ibid., vol. 23, no. 5, pp. 827-834, May 2012).

This brief deals with the estimator design problem for discrete-time switched neural networks with time-varying delay. One main problem is the asynchronous-mode switching between the neuron state and the estimator. Our goal is to design a mode-dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a prescribed l2 gain (in the H∞ sense) from the noise signal to the estimation error. A new Lyapunov functional is constructed that may increase during the mismatched switchings. New results on the stability and l2 gain analysis are then obtained. The admissible estimator gains are computed by solving a set of linear matrix inequalities. The relations among the switching law, the maximal delay upper bound, and the optimal H∞ disturbance attenuation level are established. The effectiveness of the proposed design method is finally illustrated by a numerical example.

The robust control problem is investigated for a class of uncertain nonlinear time-delay systems. Via the Takagi-Sugeno (T-S) fuzzification, we obtain the T-S fuzzy systems with each local model in the form of time-delay systems with uncertain nonlinear functions. The mismatched nonlinear functions satisfy the Lipschitz condition, while the matched parts are bounded by nonlinear functions with unknown coefficients. Based on the input matrix, the system is decomposed into two cascade subsystems. The virtual controller is designed for the first subsystem, and then, a memoryless adaptive controller is presented. By employing a new Lyapunov Krasovskii functional, we show that the resulting closed-loop system is exponentially stable and the solutions are uniformly ultimately bounded. Finally, simulation examples are given to show the effectiveness of our main results.

In this paper, the robust-control problem is investigated for a class of uncertain nonlinear time-delay systems via dynamic output-feedback approach. The considered system is in the strict-feedback form with unknown control direction. A full-order observer is constructed with the gains computed via linear matrix inequality at first. Then, with the bounds of uncertain functions known, we design the dynamic output-feedback controller such that the closed-loop system is asymptotically stable. Furthermore, when the bound functions of uncertainties are not available, the adaptive fuzzy-logic system is employed to approximate the uncertain function, and the corresponding output-feedback controller is designed. It is shown that the resulting closed-loop system is stable in the sense of semiglobal uniform ultimate boundedness. Finally, simulations are done to verify the feasibility and effectiveness of the obtained theoretical results.

This paper is concerned with the$H_{\infty} $filter design for nonlinear systems with time-varying delay via Takagi–Sugeno fuzzy model approach. Delay-dependent design method is proposed in terms of linear matrix inequalities (LMIs), which forms the main contribution of this paper. The main technique used is the free-weighting matrix method combined with a matrix decoupling approach. The results for rate-independent case, delay-independent case, and delay-free case are also given as easy corollaries. An illustrative example is given to show the effectiveness of the present method.

This paper addresses the problem of observer-based Hinfincontrol for nonlinear systems with time-varying delay represented by Takagi-Sugeno (T-S) fuzzy model. It presents a single-step linear matrix inequality (LMI) method for the fuzzy control design, which overcomes the drawback of the two-step LMI approach often encountered in the literature. The derivation relies mainly on a proposed matrix decoupling technique using which a resultant matrix inequality can be equivalently converted to strict LMIs. When restricted to delay-free fuzzy systems, the present results improve or reduce to existing ones. Illustrative examples show the effectiveness and merits of the present results.

This correspondence studies the problem of observer-based H infinity control for time-delay Takagi-Sugeno (T-S) fuzzy systems. It provides a delay-dependent linear matrix inequality (LMI)-based method for the control design. It is known that the key important problem in the literature, even for delay-independent case, lies in the difficulty of decoupling matrix variables in corresponding matrix inequalities. This correspondence suggests a decoupling technique for solving matrix inequalities with coupled variables, and provides an LMI-based algorithm by adopting the idea of the cone complementarity problem. The derivation relies on the appropriate choice of Lyaponuv-Krasovskii functionals which incorporate the intersections among local systems. Illustrative examples are given to show the effectiveness of the present delay-dependent result.

In this paper, a simple framework for PID controller design is presented which leads to the important popular setting, Ti=4Td. This setting first appeared in the Ziegler and Nichols tuning and has been widely adopted so far. The framework also provides analytical PID tuning formulas with improved performance over the ZN tuning

A simple PID tuning method for dominant pole placement and phase margin specification is proposed in this paper. Time domain specifications as settling time and percentage overshoot are represented by a pair of dominant poles, which is combined with phase margin specification to achieve closed-loop stability and robustness. A graphical method is developed to determine PID settings to meet these specifications simultaneously. An example is given for illustration

This paper studies a class of fuzzy time-delay descriptor systems in the extended Takagi-Sugeno (T-S) fuzzy model. Sufficient conditions are derived for the stability and stabilization in terms of linear matrix inequalities (LMIs). Illustrative examples are given to show the effectiveness and the advantages of the present results

In this letter, the delay-dependent state estimation problem for neural networks with time-varying delay is investigated. A delay-dependent criterion is established to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. The proposed method is based on the free-weighting matrix approach and is applicable to the case that the derivative of a time-varying delay takes any value. An algorithm is presented to compute the state estimator. Finally, a numerical example is given to demonstrate the effectiveness of this approach and the improvement over existing ones.

This paper studies the problem of H(infinity) output tracking control for nonlinear time-delay systems using Takagi-Sugeno (T-S) fuzzy model approach. An LMI-based design method is proposed for achieving the output tracking purpose. Illustrative examples are given to show the effectiveness of the present results.

In view of a recent new application of variable structure control (VSC) to the stabilization problem for Takagi-Sugeno (T-S) fuzzy models, this paper aims to study the stabilization of uncertain fuzzy time-delay systems in T-S fuzzy model via VSC approach. There are mainly two features in this paper: one lies in the incorporation of time-delays (both smooth and nonsmooth delays) in which case Lyapunov functionals and Razumikhin Theorem are required to solve the stabilization problem; the other feature is that not only matched uncertainties but also mismatched uncertainties in the state variables are considered. As a sequence, the contribution of this paper consists of various control schemes proposed for the VSC design and the present results are in terms of linear matrix inequalities (LMIs). An illustrative example is given to show the effectiveness of our various results.

The issue for designing robust adaptive stabilizing controllers for nonlinear systems in Takagi-Sugeno fuzzy model with both parameter uncertainties and external disturbances is studied in this paper. It is assumed that the parameter uncertainties are norm-bounded and may be of some structure properties and that the external disturbances satisfy matching conditions and, besides, are also norm-bounded, but the bounds of the external disturbances are not necessarily known. Two adaptive controllers are developed based on linear matrix inequality technique and it is shown that the controllers can guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate. Furthermore, the radius of the ball can also be designed to be as small as desired by tuning the controller parameters. The effectiveness of our approach is verified by its application in the control of a continuous stirred tank reactor.

The output tracking control problem for nonlinear systems in the presence of both parameter perturbations and external disturbances is studied. Our approach is based on the Takagi-Sugeno (T-S) fuzzy modeling method and a variable structure control (VSC) technique. Therefore, the systems considered are not and need not be in the triangular and parametric strict-feedback form, which are prevalent among adaptive model following control for nonlinear systems, or in the normal form, which pervades almost all existing results in neuro-fuzzy model following control approach. We first study the problem of stabilization of T-S fuzzy systems by using a VSC technique. A method for the design of a switching surface based on linear matrix inequalities is developed and a stabilizing controller based on a reaching law concept in the presence of both parameter perturbations and external disturbances is proposed. Then, the method is extended to design controllers for output tracking of T-S fuzzy nonlinear systems in two cases, i.e. systems which possess the so-called strong passive subsystems and strong stable zero dynamics, respectively. Finally, illustrative examples are presented to demonstrate the whole design procedure from the original nonlinear systems to their fuzzification and finally to the realization of the desired controllers. Simulation results show that the goal of output tracking can be achieved by the proposed controllers.

The design problem of proportional and proportional-plus-integral (PI) controllers for nonlinear systems is studied. First, the Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties is used to approximate the nonlinear systems. Then a numerically tractable algorithm based on the technique of iterative linear matrix inequalities is developed to design a proportional (static output feedback) controller for the robust stabilization of the system in T-S fuzzy model. Next, we transform the problem of PI controller design to that of proportional controller design for an augmented system and thus bring the solution of the former problem into the configuration of the developed algorithm. Finally, the proposed method is applied to the design of robust stabilizing controllers for the excitation control of power systems. Simulation results show that the transient stability can be improved by using a fuzzy PI controller when large faults appear in the system, compared to the conventional PI controller designed by using linearization method around the steady state.