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This article addresses the $H_{\infty }$ control issue for Takagi–Sugeno (T–S) fuzzy systems via an event-triggered control mechanism. First, a hybrid event-triggered control strategy combined with an adaptively adjusted approach is proposed, by which the data transmission can be effectively reduced, and meanwhile, the Zeno behavior is excluded. Then, a discontinuous Lyapunov function is constructed for the resulting hybrid event-triggered control system, and a novel lemma is developed for studying the exponential stability and disturbance attenuation performance. By utilizing a time-varying variable to manage the error during the interevent intervals together with using the newly proposed lemma, sufficient criteria are established for the stability and control design of the event-triggered T–S fuzzy system with the imperfect premise matching. Finally, numerical simulations are presented to illustrate the usefulness and advantages of the obtained theorems. [ABSTRACT FROM AUTHOR]

This article aims to propose a more efficient control algorithm for uncertain nonlinear systems. An intelligent self-organizing double function-link fuzzy brain emotional control system is proposed which comprises a self-organizing double function-link fuzzy brain emotional controller (SDFLFBEC) and a compensation controller. The proposed SDFLFBEC consists of four substructures and a fuzzy inference system. The substructures are the prefrontal cortex, the amygdala, a double function-link network (FLN) and a self-organizing structure. The prefrontal cortex and the amygdala networks work as a mathematical form that presumes the judgment and emotion of a brain. Specifically, a new double FLN is designed to support the above networks for updating their weights. Next, a self-organizing structure can automatically add or prune the layers to achieve efficient network structure. In addition, the fuzzy inference rules are presented to explain the inference processes of the amygdala and orbitofrontal networks. From the above factors, the proposed SDFLFBEC can effectively reduce the tracking error and achieve favorable control performance. The parameters of the control system are adjusted online using the derived adaptation laws that are taken from a Lyapunov function so that the stability of the system is ensured. Simulation studies of a biped robot and the experimental results of a magnetic levitation system are employed to validate the effectiveness and superiority of the proposed SDFLFBEC. [ABSTRACT FROM AUTHOR]

A novel class of high-order robust controls is presented for uncertain fuzzy systems in this article. The optimal tunable parameters are also obtained based on the Stackelberg game theory. First, a dynamical system structure is formulated with uncertainty. The uncertain portion in the system is bounded, nonlinear, and time-varying which is within prescribed fuzzy set. Thus, this is a fuzzy system. Then, the proof based on the Lyapunov minimax approach shows that the novel high-order robust controls are able to assure deterministic system performance, which is uniform boundedness and ultimate uniform boundedness. Furthermore, the optimal choice of the tunable parameters in the control is considered. We creatively apply the Stackelberg strategy to solving the optimization problem. Two parameters are regarded as leader and follower in a sequential game, respectively. Based on the Stackelberg game rules, we are able to design different cost functions for different parameters. The cost functions include both performance measures and control cost. Finally, the simulations of an electronic throttle system are presented for demonstration. [ABSTRACT FROM AUTHOR]

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles. [ABSTRACT FROM AUTHOR]

This article focuses on the security control for Takagi–Sugeno (T–S) fuzzy systems with adaptive event-triggered mechanism (AETM) and multiple cyber-attacks, which include deception attacks and denial-of-service (DoS) attacks. A multiple cyber-attacks model is first established for T–S fuzzy systems by considering deception attacks and DoS attacks at the same time. An AETM is introduced to relieve the network load, where the threshold of event-triggering condition can be adaptively adjusted while preserving the system performance. Then a novel mathematical model for T–S fuzzy systems with multiple cyber-attacks and AETM is proposed first. Based on the built model, sufficient conditions to guarantee the exponentially mean square stability of the system are achieved by utilizing the Lyapunov stability theory. Moreover, the controller gains are derived with the help of a linear matrix inequality technique. Finally, simulated examples are presented for illustrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We introduce a new configuration of a robust and adaptive autopilot system for a model-scale flapping-wing aircraft. The system is specifically designed to achieve high performance flapping angle tracking in the face of large uncertainties. To describe the dynamics of the system, we leverage the benefits of both first principle modeling and data-driven approach (system identification technique). We introduce a high-performance robust and adaptive nonlinear control system by means of a feedback linearization (FL) technique, supported with an interval Type-2 fuzzy system due to its ability to accommodate the footprint-of-uncertainties (FoUs). While the first stage of our nonlinear control system is to cancel some predictable nonlinearities using an FL technique, the second phase of control is to accommodate the existing uncertainties in the system by way of an interval Type-2 fuzzy control technique, e.g., due to imperfect cancelation and modeling errors. This way, the stability and the robustness of the closed-loop control system can be guaranteed. We quantify the relative merit of our hybrid control system with respect to an FL technique, supported with a fixed gain state feedback controller and a Type-1 fuzzy system. Lastly, we also conduct stability analysis of the overall closed-loop control system. [ABSTRACT FROM AUTHOR]

This article addresses the issue of input–output finite-time stability (IO-FTS) for nonlinear systems by employing sliding-mode control (SMC) methodology. Many practical systems subject to complex factors, such as the single-link robot arm model (SLRAM), can be characterized as nonlinear systems. Our attention is focused on designing a fuzzy-model-based finite-time SMC law to attenuate the influences of uncertainty, nonlinear term, and external disturbance during the finite-time region. First, a novel integral sliding-mode surface is proposed based on the Takagi–Sugeno fuzzy rule. Then, by using the key point of Lyapunov function theory, an appropriate fuzzy SMC law is designed to make sure that the signal variables can arrive at a domain within the assigned fixed-time level. Moreover, some new IO-FTS criteria are constructed for the resulting sliding dynamics over the whole finite-time level, including reaching phase and sliding motion phase. Via the SLRAM, we demonstrate the effectiveness of the proposed SMC approach. [ABSTRACT FROM AUTHOR]

This article considers the event-driven fault detection (FD) problem for discrete-time interval type-2 (IT2) fuzzy networked control systems (NCSs) with nonlinear perturbations. To reduce signal transmission, an event-driven mechanism in the NCSs is introduced. A novel FD fuzzy filter is designed for generating a residual signal and detecting actuator faults by considering the event-driven strategy, nonlinear perturbations, communication delay, and mismatched membership functions. It is shown that the proposed event-driven FD technique is effective to optimize performance compared with the existing FD strategy. Finally, two examples are presented to verify the advantages of the novel design technique. [ABSTRACT FROM AUTHOR]

By use of the method of sliding-mode control, the stabilization of the T–S fuzzy stochastic system with time delay is considered in this article. The characteristic of the proposed method is that the input matrices of the fuzzy system are not common. And the proposed control method of this article can resist the disturbance. First, a novel sliding-mode surface is introduced. Using the new surface, the stability of the fuzzy system can be studied even if the input matrices are not common. Second, a new lemma is given to judge if the surface can be utilized in the considered system. Third, a new control law is constructed according to the new sliding-mode surface. The control law is designed to deal with the problems produced by time delay and stochastic systems. At last, two simulations are provided. In this part, the input matrices of fuzzy systems are different from each other and there is a disturbance in this considered system. From the simulations, the results illustrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

To deal with a class of nonlinear systems with unknown control directions, a command filter-based adaptive tracking controller is designed in this paper. In the design process, fuzzy logic system is required to handle nonlinear functions, command filter is employed to settle the explosion of complexity problem and Nussbaum function is introduced to compensate the influence of unknown directions problem. Finally, the proposed control approach guarantees that error signals converge into bounded compact sets around the origin and all closed-loop signals are bounded. The effectiveness of the presented scheme is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

The real-time data generated from various smart devices will be released and shared for public to obtain numerous benefits. However, it will lead to individual privacy leakage because of data mining or analysis. Currently, many existing privacy protection models either fail to be directly applied in real-time data release or are unsatisfactory in terms of data utility and privacy protection. Toward this end, based on m-signature and fuzzy processing, an anonymous privacy protection model, named PMF, is proposed in this paper. Specifically, for the proposed model there are five advantages: 1) PMF defines m-signature for making each bucket with at least m different sensitive values instead of generating any counterfeit tuples, which can not only resist h-difference attack but also improve practical value; 2) the buckets satisfying m-signature are variable over time, and this flexibility of m-signature can improve the efficiency of dynamic update; 3) PMF can effectively insert, delete, and modify real-time data for release; 4) PMF applies fuzzy processing to handle the tuples in the candidate list, which strikes a good balance between the utility of released data and privacy protection; and 5) PMF adopts greedy heuristic algorithm to process update operations, which greatly reduces the information loss of released data. Furthermore, PMF is obviously more secure than the existing models in the real-time data release. Finally, the results of the comparison experiments on real-world and synthetic datasets illustrate that PMF is superior to the existing models in terms of data utility and efficiency. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive fault-tolerant compensation control problem for a class of Takagi–Sugeno (T–S) fuzzy systems with mismatched parameter uncertainties and actuator faults. A matrix transformation technique is introduced to relax the restriction regarding the input matrices of T–S fuzzy systems considered in the existing results. Moreover, by combining backstepping-like technique and adaptive control method, a new adaptive fault-tolerant control scheme with compensation mechanism is proposed to achieve the desirable control performance. In addition, it is proved that the states of the resulting closed-loop fuzzy system converge to zero asymptotically even in the presence of actuator faults and mismatched parameter uncertainties. Finally, two simulation examples are given to verify the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the stability problem of sampled data Takagi–Sugeno fuzzy control systems with packet dropouts is investigated. A sampling-dependent time-varying Lyapunov function (SDTVLF) is constructed to analyze the stability problem of the system and a switched system approach is proposed to model the packet dropouts phenomenon. On this basis, by dividing the sampling input available interval and unavailable interval into several segments, the matrix functions of the SDTVLF are chosen to be continuous piecewise linear. Then, by using the proposed SDTVLF approach, computable convex conditions are obtained for the sampling input unavailable interval and the sampling input available interval in framework of dwell time. By confining the sampling input unavailable interval with an upper bound and confining the sampling input available interval with a lower bound, the SDTVLF is always decreasing in all sampling input available and unavailable interval, which can make the sampled control system tolerate a larger packet dropout rate. A numerical example is provided to show the efficiency of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the dissipativity-based fuzzy control problem for nonlinear dynamic systems based on the event-triggered mechanism. For the sake of reducing the number of transmissions while maintaining the closed-loop stability of the system, the event-triggered mechanism is considered. Moreover, the dissipativity is also taken into account in designing a controller that ensures the resulting closed-loop system is asymptotically stable and strictly $({X},{Y},{Z})$ - ${\theta }$ -dissipative. In view of the fuzzy model, the stability of the resulting system is analyzed in terms of Lyapunov stability theory. According to the stability conditions, a fuzzy controller is designed. Additionally, the explicit expression of the desired controller is given in view of linear matrix inequalities. Finally, the corresponding simulation results are plotted by applying the standard software, and a practical example on a truck-trailer model is provided to verify and illustrate the effectiveness and applicability of the proposed fuzzy controller design scheme. [ABSTRACT FROM AUTHOR]

The control of mobile robots in an uncertain environment that is densely cluttered with dynamic obstacles is an important problem in robotics. A fuzzy set theory is one of the important tools for describing uncertainty and designing control system of the mobile robot. In this paper, a Z-number-based fuzzy inference system for control of the omnidirectional soccer robot is proposed. To design the fuzzy control system of the robot, at first stage, the modeling of the kinematics and dynamics of the omnidirectional four-wheel-driven soccer robot was carried out. Then the design of control system is implemented for the linear and angular speed. Using Z-number-based fuzzy rule base and interpolative reasoning the Z-number-based fuzzy inference system is proposed. The proposed Z-number-based inference mechanism is then used for control of the omnidirectional robot. The fuzzy controller algorithm has been tested in simulations and provided satisfactory results at runtime. The proposed control algorithm minimizes the deviation between the current plant output signal and the reference signal. The results obtained in this paper demonstrate the suitability of using Z-number-based system in control of soccer robots. [ABSTRACT FROM AUTHOR]

This paper focuses on an observer-based adaptive fuzzy control problem for nonstrict-feedback stochastic nonlinear systems with input saturation and prescribed performance. By introducing a mean-value theorem, the obstacle arising from input saturation is overcome. The transient performance of system output is also achieved by means of designing performance function. On the other hand, a variable separation method is used to deal with the difficulty caused by nonstrict-feedback structure. Additionally, fuzzy logic systems are introduced to approximate unknown nonlinear functions. Moreover, an observer is constructed to estimate unknown state variables in the systems. By combining adaptive backstepping control method, an observer-based adaptive fuzzy controller is designed to ensure all signals of the resulting closed-loop system to be bounded, and the output of the system converge to a prescribed area. Finally, two examples are given to show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of nonfragile guaranteed cost control for discrete-time Takagi–Sugeno fuzzy Markov jump systems with time-varying delays. With the help of the parallel distributed compensation, a nonfragile fuzzy controller is designed. Then via Lyapunov–Krasovskii functional approach, sufficient conditions are obtained ensuring that the resulting closed-loop system is asymptotically stable with an upper bound of the guaranteed cost index. The optimal upper bound of the guaranteed cost index and the controller gain can be achieved via the optimization technique. Finally, an example is presented to show the effectiveness of the proposed new design techniques. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of adaptive fuzzy control for a category of single-input single-output nonlinear networked control systems with network-induced delay and data loss based on adaptive backstepping control approach. Fuzzy logic systems are used to approximate the unknown nonlinear characteristics existing in the system, while Pade approximation is introduced to handle network-induced delay. Data loss occurs intermittently and stochastically in the data transmitting process, which is regarded as the delay in the controller design. In the framework of adaptive fuzzy backstepping technique, a novel state-feedback adaptive controller is constructed to ensure all signals in the resulting closed-loop system to be bounded and the state variables can be regulated to the origin. Finally, two examples are given to show the validity of the proposed results. [ABSTRACT FROM PUBLISHER]

In this paper, a fuzzy approximator based adaptive dynamic surface control scheme is proposed for a class of unknown time delay nonlinear systems preceded by asymmetric hysteresis nonlinearities. The features of the proposed method are: 1) by combining the approximated property of the fuzzy logic systems (FLSs) with the finite covering lemma, the Krasovskii functionals are disposed of, achieving the L{\infty } norm of the tracking error by using the initializing technique; 2) the assumptions on the time-delay functions are removed due to the use of the finite covering lemma and the FLSs; and 3) the proposed adaptive fuzzy dynamic surface control scheme can also compensate the asymmetric shifted Prandtl-Ishlinskii (ASPI) hysteresis without constructing the inverse of the ASPI model with the density function of ASPI model being unknown and estimated online to compensate the hysteresis. It is proved that all the signals in the closed-loop system are ultimately uniformly bounded and can be made arbitrarily small. Simulation results show the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an affine Takagi–Sugeno (TS) fuzzy modeling-based observer and controller are proposed for the estimation and control of a chaotic Hindmarsh–Rose (HR) neuronal model. The main contributions are given as follows. 1) First, an affine TS fuzzy model of the HR chaotic neuronal model is constructed using sector nonlinearity-based approach. 2) Based on the constructed TS fuzzy model, a TS fuzzy observer is designed for simultaneous state and parameter estimation of HR neuronal model for unmeasurable state and parameters. 3) In the same way, a novel affine TS fuzzy model-based output feedback control law is designed with observed state and parameters where the exponential stability of the designs are guaranteed by Lyapunov approach. 4) Finally, numerical simulations are conducted to illustrate the observation and stimulation with regular and fast spiking trains and annihilation of the membrane potential. [ABSTRACT FROM PUBLISHER]

This paper studies an adaptive fuzzy tracking control problem for nonlinear stochastic systems with input saturation and nonstrict-feedback form. We use fuzzy logic systems to approximate unknown nonlinear functions. A novel approach is introduced to tackle unknown functions with nonstrict-feedback structure in the design process. By introducing an auxiliary system, the input saturation problem can be solved. Moreover, based on backstepping control design approach, a novel adaptive fuzzy tracking controller is designed to guarantee all signals in the closed-loop system to be bounded, and the system output can be driven to track the trajectory of a given reference signal. Finally, some simulation results are given to confirm the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

Decomposed fuzzy system (DFS) is a fuzzy system with a novel structure. Due to its excellent learning performance, DFS is originally proposed for an online learning control scheme and is shown to have effective learning performance. This paper is about the use of DFS for modeling dynamic systems. Since the learning mechanism used in online learning control is not suitable for modeling tasks, a commonly used back propagation learning algorithm is adapted for the use of DFS in modeling dynamic systems. The structure of DFS is to decompose each fuzzy variable into fuzzy subsystems that are called component fuzzy systems. Owing to the independency among component fuzzy systems, the learning for those parameters is also independent among different component fuzzy systems and thus, the learning can become more efficient. From the simulation results, it is evident that the proposed DFS can have much faster convergent speed. In addition, the DFS has a smaller testing error than those of other fuzzy systems. [ABSTRACT FROM PUBLISHER]

The final output of modern processes is significantly influenced by the selection of the set points of the process variables, as they fundamentally impact the product quality characteristics and the process performance metrics. This paper proposes a decision support system structure based on models built using fuzzy cognitive maps, an optimization problem that employs these models as constraints, and an observer that augments the optimization problem and allows to implement a run-to-run control approach, enabling the system to correct the suggested set-point values in case of a mismatch between the desired process output and the actual one. An application example related to an industrial process is included to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

Backstepping control is effective for integer-order nonlinear systems with triangular structures. Nevertheless, it is hard to be applied to fractional-order nonlinear systems as the fractional-order derivative of a compound function is very complicated. In this paper, we develop an adaptive fuzzy backstepping control method for a class of uncertain fractional-order nonlinear systems with unknown external disturbances. In each step, a complicated unknown nonlinear function produced by differentiating a compound function with a fractional order is approximated by a fuzzy logic system, and a virtual control law is designed based on the fractional Lyapunov stability criterion. At the last step, an adaptive fuzzy controller that ensures convergence of the tracking error is constructed. The effectiveness of the proposed method has been verified by two simulation examples. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the problem of reliable switched controller design for a class of discrete-time Takagi–Sugeno fuzzy systems with randomly occurring infinite-distributed delays and quantization as well as actuator failures. A random Bernoulli process is used to describe the stochastic infinite-distributed delays. Due to limited communication capacity, the control signal is quantized before being transmitted to the actuator by the logarithmic quantizer. We apply the switching mechanisms to categorize the stochastic behavior of actuator faults. Based on the parallel distributed compensation, the switched feedback controller is designed. By the fuzzy-basis-dependent Lyapunov functional approach, sufficient conditions are obtained to ensure that the resulting closed-loop system is exponentially stable in the mean-square sense with a given l {2} - l {\infty } performance index. Then, a numerical example is presented to demonstrate the effectiveness of the proposed new design method. [ABSTRACT FROM AUTHOR]

This paper introduces a robust H{\infty } adaptive fuzzy controller for a class of unknown nonlinear systems over network. There are two main problems in the networked control systems, the time-varying networked-induced delay and the data packet dropouts. The time-varying networked-induced delays cause degradation for the system performance that controlled over network and also the system can be unstable. Moreover, the delay problem is aggravated when packet losses occur during the transmission of data. The proposed controller has a filtered error to handle the networked-induced delays. Furthermore, it is robust to overcome some packet losses. The unknown nonlinear functions of the system are approximated using fuzzy logic systems. The robustness of the proposed controller is achieved by combining the adaptive fuzzy controller with H{\infty } control technique. The Lyapunov stability analysis is used to prove that the proposed controller is asymptotically stable. Stability of the whole closed loop system is guaranteed via the proposed controller in the presence of bounded external disturbance, data packet dropouts and time-varying networked-induced delays. An extensive example of an inverted pendulum system is studied in detail to verify the effectiveness of the proposed controller based on TrueTime toolbox with comparative results. [ABSTRACT FROM AUTHOR]

In this paper, an active dynamic surface control (DSC) to suppress regenerative chatter in micro-milling with nonlinear piezo-actuators (PZTAs) as active elements has been developed, where the hysteresis effects of two PZTAs are considered. The main features of the proposed approach are: 1) fuzzy neural networks (FNNs) are applied to approximate the unknown functions, including the uncertain dynamics of the high speed cutting system, the unknown bounding functions related to the time-delayed states, and the control errors introduced during the control design procedure; 2) to avoid the development of the adaptation law for each individual weight of the FNNs, the squared 2-norms of the weight vectors are constructed; 3) the utilization of two decreasing smooth performance functions ensures the boundedness of the tracking errors with the prescribed performance; 4) the Prandtl-Ishlinskii (PI) model is employed to depict the hysteresis effect of the active PZTAs and its inverse construction is adopted to mitigate the nonlinear influence; and 5) the Lyapunov-Krasovskii functional is used to cope with the time-delayed effect of chatter in micro-milling, which plays a pivotal role in developing the active control laws. The developed controller with the employments of Lyapunov-Krasovskii functionals, DSC and the adaptive FNN backstepping design can successfully suppress the chatter in micro-milling, guaranteeing all the signals of the closed-loop system are bounded. Simulation results are provided to verify the effectiveness of the proposed control approach. [ABSTRACT FROM PUBLISHER]

In this paper, the output feedback control problem is investigated for general nonlinear industrial processes. At the device layer, the nonlinear industrial processes with disturbances are modeled by utilizing Takagi–Sugeno modeling approach, and the corresponding local controllers are then designed to guarantee that the outputs for the local subsystems can track the decomposed setpoints. At the operation layer, considering the effect of radial basis function performance index and packet dropout phenomenon, a setpoint compensator is constructed to dynamically regulate the setpoints and track the given operation index. Finally, a network-based continuous stirred tank reactor system is considered to verify the validity of the proposed strategy in the simulation part. [ABSTRACT FROM PUBLISHER]

This paper focuses mainly on the approximation-based fuzzy adaptive fault-tolerant control problem for nonlinear systems with unmodeled dynamics and unknown control directions. With the Nussbaum gain technique and a dynamic signal introduced, the difficulties from the unknown control directions and unmodeled dynamics are successfully overcome. Then, by taking advantage of the adaptive fuzzy control method and backstepping technology, we develop a fuzzy adaptive failure compensation control strategy and guarantee the semi-global boundedness for all signals. A simulation example is carried out to demonstrate the validity of the theoretical findings. [ABSTRACT FROM PUBLISHER]

An H_\infty fuzzy control design is presented for the disturbance attenuation of a class of coupled systems described by a set of nonlinear ordinary differential equations (ODEs) and a semi-linear parabolic partial differential equation (PDE). The fuzzy control scheme consists of an ODE state feedback fuzzy subcontroller for the ODE subsystem and a PDE static output feedback fuzzy subcontroller for the PDE subsystem by using piecewise uniform actuators and pointwise sensors. Initially, the original nonlinear system is accurately represented by employing a Takagi–Sugeno fuzzy coupled parabolic PDE–ODE model. Then, an H_\infty fuzzy controller is developed to exponentially stabilize the fuzzy coupled system while satisfying a prescribed H_\infty performance of disturbance attenuation, whose existence condition is given by linear matrix inequalities. Finally, simulation results on a hypersonic rocket car are given to show the effectiveness of the proposed design method. [ABSTRACT FROM PUBLISHER]

This paper investigates the guaranteed-cost finite-time fuzzy control problem subject to a temperature constraint for a class of coupled systems represented by nonlinear ordinary differential equations (ODEs) and a scalar nonlinear heat equation. Initially, a finite-dimensional nonlinear coupled system is derived by combining the slow system of heat equation with the original ODE system, which can be exactly represented by the Takagi–Sugeno fuzzy model. Meanwhile, the temperature constraint is transformed into the state constraint performed on the finite-dimensional coupled system. Then, a guaranteed-cost finite-time constrained fuzzy control design is developed in terms of a set of time-dependent differential linear matrix inequalities (LMIs) to make the closed-loop of the original ODE system finite-time quasi-contractively stable with an upper bound of quadratic cost function, while the temperature constraint is respected. Furthermore, by utilizing the time-convexity and LMI optimization techniques, a suboptimal controller is obtained by means of minimizing the guaranteed-cost bound. Finally, the proposed design method is applied to the control of a temperature constrained hypersonic rocket car. [ABSTRACT FROM PUBLISHER]

This paper investigates the disturbance observer-based composite fuzzy control of a class of uncertain nonlinear systems with unknown dead zone. With fuzzy logic system approximating the unknown nonlinearities, composite learning is constructed on the basis of a serial–parallel identifier. By introducing the intermediate signal, the disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time-varying disturbance, fuzzy approximation error, and unknown dead zone. Based on the disturbance estimation and fuzzy approximation, the adaptive fuzzy controller is synthesized with novel updating law. The stability analysis of the closed-loop system is rigorously established via Lyapunov approach. The performance of the proposed controller is verified via simulation that faster convergence and higher precision are obtained. [ABSTRACT FROM PUBLISHER]

This paper investigates the fuzzy control issue for uncertain active suspension systems via dynamic sliding-mode method. The Takagi–Sugeno fuzzy approach is adopted on the background of the varying masses to describe the prescribed nonlinear system in order to achieve the design targets via the method of sector nonlinearity. This paper employs the dynamic sliding-mode scheme to control nonlinear active suspension systems. In the proposed sliding-mode control scheme, the sliding surface function is formed linearly with the system states and control inputs. Then, a fuzzy dynamic term is utilized to construct the sliding-mode feedback controller. In existing results, the sliding mode is achieved and maintained with no consideration of the system perturbations. Thus, sufficient conditions are proposed to make the sliding surface reachable with the existence of the system perturbations to make the augmented system stable. Finally, simulation results are presented to verify the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the observer-based output feedback control problem for Takagi and Sugeno fuzzy systems with partly immeasurable premise variables. By using measurable premise variables and estimations of immeasurable premise variables of fuzzy models as the premise variables of fuzzy controllers, a new fuzzy control scheme is proposed. Further by constructing a set-theoretic description of fuzzy systems, the equivalence class theory in set theory is applied for designing fuzzy controllers, then the partly measurable premise variables are effectively used in the proposed conditions. In the existing fuzzy control schemes, the fuzzy weight functions are unaccessible on line due to the partly immeasurable premise variables, so that the partly measurable premise variables cannot be directly used, which might introduce some conservatism. Different from the existing results, the new condition can make better use of measurable premise variables via the proposed control scheme. A numerical example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

One of the benefits of using Combs disjunctive control in swarm intelligence is the ability of the swarm to continue to operate even when one or more of its sensors are broken. Instead of failing, the swarm continues to perform based on the inputs of the remaining sensors. In this technical correspondence, a multiobjective scenario is designed for a swarm of agents to complete. Once optimized, the swarm is modified by removing one of its sensors. Maintaining the original objective, the evolution process is continued to allow the swarm to compensate for its handicap. This technical correspondence describes the different emergent behaviors of the swarm to compensate for the loss of function. The solution for the diminished swarm can vary significantly from that of the original. [ABSTRACT FROM AUTHOR]