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Showing papers on "Fuzzy control system published in 2021"


Journal ArticleDOI
TL;DR: An improved error conversion mechanism based on performance functions is presented such that the converted error is limited to an interval greater than zero, and an appropriate barrier Lyapunov function (BLF) is constructed to avoid the breach of position tracking error constraint.
Abstract: In this article, the singularity-free adaptive fuzzy fixed-time control problem is studied for an uncertain n -link robotic system with the position tracking error constraint. The controlled robotic system can be described as a multiple-input–multiple-output system.To implement the user-defined performance, an improved error conversion mechanism based on performance functions is presented such that the converted error is limited to an interval greater than zero, and an appropriate barrier Lyapunov function (BLF) is constructed to avoid the breach of position tracking error constraint. The fuzzy approximator is utilized to estimate the unknown functions. The significance and challenges of this article are to establish a new error conversion mechanism and design corresponding BLF that can be integrated into fixed-time control design to present a singularity-free adaptive fuzzy fixed-time control scheme. Benefits of the proposed adaptive fixed-time controller in comparison to the current approaches are that it cannot cause the singularity issue appearing in backstepping-based fixed-time control design and ensures quick transient response. Combining with Lyapunov stability theory, the boundedness of the closed-loop signals is ensured, and the position tracking error can be constrained in the user-defined performance boundaries. Finally, simulation results demonstrate the feasibility of the proposed control strategy.

209 citations


Journal ArticleDOI
TL;DR: In this article, an event-triggered robust fuzzy adaptive prescribed performance finite-time control strategy is presented for a class of strict-feedback nonlinear systems with external disturbances, and the dynamic surface control technique is applied to address the computational complexity problem.
Abstract: In this article, an event-triggered robust fuzzy adaptive prescribed performance finite-time control strategy is presented for a class of strict-feedback nonlinear systems with external disturbances. The relative-threshold-based event-triggered signal is introduced to reduce communication burden, and the dynamic surface control technique is applied to address the computational complexity problem. A disturbance observer is designed to estimate the compounded disturbances, which are composed of external disturbances and fuzzy approximation errors. The proposed control strategy can guarantee that the closed-loop system is semiglobally practically finite-time stable, and the tracking error converges to a small residual set by incorporating the prescribed performance bound in finite-time. Finally, simulation results are provided to verify the effectiveness of the proposed robust fuzzy control strategy.

198 citations


Journal ArticleDOI
TL;DR: To process the measurement output and schedule the transmission sequence for eliminating the communication burden, a logarithmic quantizer and a weighted try-once-discard protocol are synthesized, which can further improve the network bandwidth utilization in networked control systems.
Abstract: In this paper, the sliding mode control issue is investigated for a class of discrete-time Takagi-Sugeno fuzzy networked singularly perturbed systems via an observer-based technique. Moreover, to process the measurement output and schedule the transmission sequence for eliminating the communication burden, a logarithmic quantizer and a weighted try-once-discard protocol are synthesized, which can further improve the network bandwidth utilization in networked control systems. Based on the fuzzy observer states, a novel fuzzy sliding surface is established with taking the singularly perturbed parameter into consideration properly, and we endeavor to synthesize a fuzzy observer-based sliding mode control law such that the reachability of the prescribed sliding surface could be guaranteed. In addition, by virtue of the convex optimization theory and Lyapunov approach, sufficient conditions are developed to guarantee the asymptotic stability of the sliding mode dynamics as well as the error system with an expected $H_{\infty }$ performance. Finally, a verification example is presented to illustrate the feasibility and effectiveness of the proposed method.

194 citations


Journal ArticleDOI
TL;DR: The least-squares algorithm specific to Virtual Reference Feedback Tuning is replaced with a metaheuristic optimization algorithm, i.e. Grey Wolf Optimizer, to exploit the advantages of data-driven control and fuzzy control.

186 citations


Journal ArticleDOI
TL;DR: In this paper, the authors explored the performance of fuzzy system-based medical image processing for brain disease prediction, and designed a brain image processing and brain disease diagnosis prediction model based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation).
Abstract: The present work aims to explore the performance of fuzzy system-based medical image processing for brain disease prediction. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. While ensuring the model safety performance, a brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation). Brain MRI images collected from the Department of Brain Oncology, XX Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrated that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 seconds on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under the DSC coefficient, reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. To sum up, the proposed algorithm can provide higher accuracy while ensuring energy consumption, a more apparent denoising effect, and the best segmentation and recognition effect than other models, which can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

179 citations


Journal ArticleDOI
TL;DR: A novel control scheme is constructed to ensure that tracking error is within a very small range of the origin almost surely, meanwhile, the constraints on the system states are not breached almost surely during the operation.
Abstract: This paper focuses on the design of a reduced adaptive fuzzy tracking controller for a class of high-order stochastic nonstrict feedback nonlinear systems with full-state constraints. In the proposed approach, reduced fuzzy systems are used to approximate uncertain functions which involve all state variables and a high-order tan-type barrier Lyapunov function (BLF) is considered to deal with full-state constraints of the controlled system. With this BLF and a combination of the reduced fuzzy control and adding a power integrator, a novel control scheme is constructed to ensure that tracking error is within a very small range of the origin almost surely, meanwhile, the constraints on the system states are not breached almost surely during the operation. Two examples are proposed to show the effectiveness of the design scheme.

133 citations


Journal ArticleDOI
TL;DR: This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi–Sugeno fuzzy model method, and derives a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite- time bounded and positive, and fulfill the given $L_{2}$ performance index.
Abstract: This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi–Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given $L_{2}$ performance index. Applying the stochastic Lyapunov–Krasovskii functional (SLKF) methods, we establish sufficient conditions to obtain the finite-time state-feedback controller. Finally, a Lotka–Volterra population model is used to show the feasibility and validity of the main results.

131 citations


Journal ArticleDOI
TL;DR: Finite-time adaptive fuzzy output-feedback control for a class of nontriangular nonlinear systems with full-state constraints and unmeasurable states with finite-time stability theory is focused on.
Abstract: This article focuses on finite-time adaptive fuzzy output-feedback control for a class of nontriangular nonlinear systems with full-state constraints and unmeasurable states. Fuzzy-logic systems and the fuzzy state observer are employed to approximate uncertain nonlinear functions and estimate the unmeasured states, respectively. In order to solve the algebraic loop problem generated by the nontriangular structure, a variable separation approach based on the property of the fuzzy basis function is utilized. The barrier Lyapunov function is incorporated into each step of backstepping, and the condition of the state constraint is satisfied. The dynamic surface technique with an auxiliary first-order linear filter is applied to avoid the problem of an “explosion of complexity.” Based on the finite-time stability theory, an adaptive fuzzy controller is constructed to guarantee that all signals in the closed-loop system are bounded, the tracking error converges to a small neighborhood of the origin in a finite time, and all states are ensured to remain in the predefined sets. Finally, the simulation results reveal the effectiveness of the proposed control design.

130 citations


Journal ArticleDOI
TL;DR: It is proven that all states of the closed-loop system are bounded in finite time under the proposed fuzzy finite-time control scheme and the proposed control method is extended to a class of more general switched large-scale nonlinear systems.
Abstract: The adaptive fuzzy finite-time tracking control problem of a class of switched nonlinear systems is investigated in this study. Fuzzy logic systems are introduced to handle the unknown nonlinear terms in the considered system. To overcome the drawback in the recursive design method, a finite-time command filter is employed. By constructing a new state-dependent switching law and adaptive fuzzy control signal, the existing restrictions on subsystems of switched systems are relaxed, all subsystems of the considered system are allowed to be unstabilizable. To avoid the Zeno behavior, a new hysteresis switching law is derived. It is proven that all states of the closed-loop system are bounded in finite time under the proposed fuzzy finite-time control scheme. Additionally, the proposed control method is extended to a class of more general switched large-scale nonlinear systems. Finally, two examples are provided to verify the developed method's effectiveness.

128 citations


Journal ArticleDOI
TL;DR: A novel adaptive fuzzy event-triggered control method for stochastic nonlinear systems with unmeasured states and unknown backlash-like hysteresis is constructed and it is shown that whole signals in the closed-loop systems are, ultimately, semiglobally and uniformly bounded in probability.
Abstract: This article investigates the event-triggered control problem for stochastic nonlinear systems with unmeasured states and unknown backlash-like hysteresis. Based on the fuzzy logic systems, the unknown nonlinear functions can be identified. Then, by utilizing a fuzzy state observer, the unmeasured states of the considered system can be estimated. Moreover, by introducing an event-triggered mechanism, the communication load can be largely reduced. By employing the backstepping control strategy and the adaptive control method, a novel adaptive fuzzy event-triggered control method is constructed. It is shown that whole signals in the closed-loop systems are, ultimately, semiglobally and uniformly bounded in probability. Moreover, the tracking errors and the observer errors are located in a small neighborhood around the origin. Finally, a numerical example is given to confirm the effectiveness of the design scheme.

126 citations


Journal ArticleDOI
TL;DR: Simulation results substantiate the effectiveness of the event-triggered dynamic surface control method for circumnavigating a maneuvering target.
Abstract: This article addresses the event-triggered dynamic surface control of an underactuated autonomous surface vehicle with unknown kinetics for circumnavigating a dynamic target with unknown velocity. A modular design approach to the event-triggered dynamic surface control is proposed for target enclosing. In the estimator module, an extended state observer is employed for estimating the relative motion between the target and the surface vehicle. A fuzzy system is used for online modeling the unknown vehicle kinetics. In the controller module, an event-triggered dynamic surface control law is constructed by using the estimated relative velocities and vehicle kinetics in the estimator module. In the control law, a triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. Besides, the control inputs are bounded with the aid of a projection operator and saturated functions. The input-to-state stability of the closed-loop target enclosing system is proven through Lyapunov analysis. Simulation results substantiate the effectiveness of the event-triggered dynamic surface control method for circumnavigating a maneuvering target.

Journal ArticleDOI
TL;DR: In this paper, a sliding-mode surface analysis for MIMO underactuated systems is presented, and the asymptotic stability of the system equilibrium point is strictly proven based on the composite surfaces.
Abstract: In the field of modern industrial engineering, many mechanical systems are underactuated, exhibiting strong nonlinear characteristics and high flexibility However, the lack of control inputs brings about many difficulties for controller design and stability/convergence analysis Additionally, some unavoidable practical issues, eg, plant uncertainties and actuator deadzones, make the control of underactuated systems even more challenging Hence, with the aid of elaborately constructed finite-time convergent surfaces, this article provides the first solution to address the control problem for a class of multi-input-multi-output (MIMO) underactuated systems subject to plant uncertainties and actuator deadzones Specifically, this article overcomes the main obstacle in sliding-mode surface analysis for MIMO underactuated systems, that is, by the presented analysis method, the asymptotic stability of the system equilibrium point is strictly proven based on the composite surfaces In addition, the unknown parts of the actuated/unactuated dynamic equations and actuator deadzones can be simultaneously handled, which is important for real applications Furthermore, we apply the proposed method to two kinds of typical underactuated systems, that is: 1) tower cranes and 2) double-pendulum cranes, and implement a series of hardware experiments to verify its effectiveness and robustness

Journal ArticleDOI
TL;DR: A fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique is presented.
Abstract: 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.

Journal ArticleDOI
TL;DR: To deal with a class of nonlinear systems with unknown control directions, a command filter-based adaptive tracking controller is designed and guarantees that error signals converge into bounded compact sets around the origin and all closed-loop signals are bounded.
Abstract: 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.

Journal ArticleDOI
TL;DR: In this article, an adaptive fuzzy fault-tolerant control strategy is introduced to deal with the difficulties associated with the actuator faults and external disturbance, and a modified performance function, which is called the finite-time performance function (FTPF), is presented.
Abstract: In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results.

Journal ArticleDOI
TL;DR: This paper proposes an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation and shows that the proposed fuzzy system has smaller drowsness estimation errors and higher interpretability than introduced benchmarking models.
Abstract: In the field of intelligent transportation, transfer learning (TL) is often used to recognize EEG-based drowsy driving for a new subject with few subject-specific calibration data. However, most of existing TL-based models are offline, non-transparent, and in which features are only represented from one view (usually only one algorithm is used to extract features). In this paper, we consider an online multi-view regression model with high interpretability. By taking the 1-order TSK fuzzy system as the basic regression component and injecting the nature of the multi-view settings into the existing transfer learning framework and enforcing the consistencies across different views, we propose an online multi-view & transfer TSK fuzzy system for driver drowsiness estimation. In this novel model, features in both the source domain and the target domain are represented from multi-view perspectives such that more pattern information can be utilized during model training. Also, comparing with offline training, the proposed online fuzzy system meets the practical requirements more competently. An experiment on a driving dataset demonstrates that the proposed fuzzy system has smaller drowsiness estimation errors and higher interpretability than introduced benchmarking models.

Journal ArticleDOI
TL;DR: The closed-loop system is transformed into a fuzzy piecewise-homogeneous Markov jump singularly perturbed descriptor system (MJSPDS) by descriptor representation and a rigorous proof of mean-square exponential admissibility for the resulting fuzzy MJSPDS is presented.
Abstract: This article addresses the $H_{\infty } $ control and filtering problems for Markov jump singularly perturbed systems approximated by Takagi–Sugeno fuzzy models. The underlying transition probabilities (TPs) are assumed to vary randomly in a finite set, which is characterized by a higher level TP matrix. The mode- and variation-dependent fuzzy static output-feedback controller (SOFC) and filter are designed, respectively, to fulfill the control and filtering purposes. To facilitate the fuzzy SOFC synthesis, the closed-loop system is transformed into a fuzzy piecewise-homogeneous Markov jump singularly perturbed descriptor system (MJSPDS) by descriptor representation. A rigorous proof of mean-square exponential admissibility for the resulting fuzzy MJSPDS is presented. The criterion ensuring the mean-square exponential stability of the fuzzy filtering error system is further formed based on similar procedures. By setting the specific forms of the related matrix variables, the solutions for the predesigned fuzzy SOFC and filter are furnished, respectively. Finally, feasibility and validities of the developed fuzzy control and filtering results are verified by two practical examples.

Journal ArticleDOI
TL;DR: The presented scheme can realized better tracking property and estimate the unknown model more accurately, thus obtaining better control effects than that without adding fractional order control.
Abstract: This article proposes a fractional order nonsingular terminal super-twisting sliding mode control (FONT-STSMC) method for a micro gyroscope with unknown uncertainty based on the double-loop fuzzy neural network (DLFNN). First, the advantages of nonsingular terminal sliding control are adopted, a nonlinear function is used to design the sliding hyper plane, then the tracking error in the system could converge to zero in a specified finite time. Second, fractional order control can increase the order of differential and integral, which greatly improves the flexibility of control method. The fractional-order controller has some advantages that integer-order systems cannot achieve, thus obtaining better control effects than that without adding fractional order control. Furthermore, the chattering problem of control input can be effectively solved by using the super-twisting algorithm, which makes the control input smoother. Finally, the unknown model of the micro gyroscope is estimated by using the DLFNN. Because the DLFNN can adjust the base width, the center vector and the feedback gain of the inner and outer layers adaptively, the accurate approximation of the unknown model can be achieved, and the robustness and accuracy can be enhanced. The simulation results and the comparisons with conventional neural sliding mode control prove the presented scheme can realized better tracking property and estimate the unknown model more accurately.

Journal ArticleDOI
TL;DR: By using the finite-time stability criterion, it is proven that the proposed control strategy can ensure the boundedness of the whole system variables and achieve all the state tracking errors evolve within the predesigned performance regions in finite time.
Abstract: This article investigates the problem of event-based decentralized adaptive fuzzy output-feedback finite-time control for the large-scale nonlinear systems. The full-state tracking error constraints, unmeasured states, and external disturbances are simultaneously considered in the controlled systems. The unknown auxiliary functions are modeled by using fuzzy logic systems, and a state observer is established to estimate unmeasured states. By taking a new error transformation method based on prescribed performance functions and constructing corresponding barrier Lyapunov functions, the predefined system error dynamic performance is ensured. Then, on the basis of the event-triggered control technique and the backstepping recursive design technique, a new event-based adaptive fuzzy nonsingular finite-time control strategy is proposed, and the “singularity” problem existing in backstepping design procedure is avoided. Moreover, by using the finite-time stability criterion, it is proven that the proposed control strategy can ensure the boundedness of the whole system variables and achieve all the state tracking errors evolve within the predesigned performance regions in finite time. Finally, the effectiveness of the proposed control strategy is verified by using some simulation results.

Journal ArticleDOI
TL;DR: In the light of fixed time theory, it is proved that both the stability and tracking performance of the closed-loop system can be obtained in fixed time.
Abstract: This paper addresses the fixed-time control problem for the constrained quarter active vehicle suspension systems (AVSSs) via an event-triggered-based adaptive fuzzy fixed-time control method. The benefit of the usage of the time-varying barrier Lyapunov function (BLF) is to avoid the violation of the time-varying displacement constraint so that the stability and safety of AVSSs can be guaranteed. The relative-thresholdbased event-triggered controller is devised so as to reduce the communication burden from the controller to the actuator. In the light of fixed time theory, it is proved that both the stability and tracking performance of the closed-loop system can be obtained in fixed time. The fixed-time based eventtriggered control strategy is independent of initial states of AVSSs in comparison with the existing finite-time results. Some simulation results and comparisons on a quarter-car AVSS indicate better performance in terms of feasible fixed-time control and exact trajectory tracking.

Journal ArticleDOI
TL;DR: In this article, a fault tolerant control strategy for dynamic positioning of unmanned marine vehicles using the quantized feedback sliding mode control technique is proposed, which is based on switching mechanism to compensate for thruster faults effects.
Abstract: This paper proposes a novel fault tolerant control strategy for dynamic positioning of unmanned marine vehicles using the quantized feedback sliding mode control technique. Due to the complex ocean environment, the unmanned marine vehicles are modeled as the Takagi-Sugeno fuzzy system with unknown membership functions. When the membership functions are not available, traditional sliding mode control technique becomes infeasible. To tackle this difficulty, a novel quantized sliding mode control strategy based on switching mechanism is designed to compensate for thruster faults effects. In addition, the phenomenon of time-varying delay leads to conservativeness of the existing dynamic quantization parameter adjustment strategy. Then a larger quantization parameter adjustment range, by taking time delay and fault factor into account, is given. Combining the novel sliding mode controller design and the improved dynamic quantization parameter adjustment strategy, the dynamic positioning of unmanned marine vehicles with thruster faults and quantization can be maintained. Finally, the effectiveness of the proposed method is verified through the simulation comparison results.

Journal ArticleDOI
TL;DR: Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance and it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.
Abstract: This article reports our study on a reduced adaptive fuzzy decoupling control for our lower limb exoskeleton system which typically is a multi-input–multi-output (MIMO) uncertain nonlinear system. To show the applicability and generality of the proposed control methods, a more general MIMO uncertain nonlinear system model is considered. By decoupling control, the entire MIMO system is separated into several MISO subsystems. In our experiments, such a system may have problems (even unstable) if a traditional fuzzy approximator is used to estimate the complicated coupling terms. In this article, to overcome this problem, a reduced adaptive fuzzy system together with a compensation term is proposed. Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance. By employing the proposed control scheme to an actual 2-DOF lower limb exoskeleton rehabilitation robot system, it can be seen from the experimental results that, as expected, it has good performance to track the model trajectory of a human walking gait. Therefore, it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.

Journal ArticleDOI
TL;DR: F fuzzy asynchronous filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi–Sugeno fuzzy control technique contains synchronous and mode-independent filtering as special cases.
Abstract: This article reports our study on asynchronous ${H_{\infty } }$ filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi–Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the ${H_{\infty } }$ performance level. Simulation examples including a single-link robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.

Journal ArticleDOI
TL;DR: By integrating the prescribed performance control and command filter technique into backstepping recursive design, a finite-time adaptive output-feedback controller is constructed, and the stability of closed-loop system is strictly proved.
Abstract: This paper considers the problem of finite-time adaptive fuzzy prescribed performance control via output-feedback for nonstrict-feedback nonlinear systems. The fuzzy state observer is designed to estimate the unmeasured system states. To rapidly approximate the derivative of virtual signal, a novel finite-time command filter is proposed. The fractional power error compensation mechanism is established to remove filtered error. By integrating the prescribed performance control and command filter technique into backstepping recursive design, a finite-time adaptive output-feedback controller is constructed, and the stability of closed-loop system is strictly proved. The designed control strategy shows that the closed-loop system is practical finite-time stable, and the output tracking error converges to a residual set within prescribed performance bound in finite time. Finally, a numerical comparison and a practical examples are provided to demonstrate the validity of the developed finite-time control algorithm.

Journal ArticleDOI
TL;DR: In this article, an interval type-3 fuzzy logic system (IT3-FLS) and an online learning approach are designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems.
Abstract: In this article, a novel method based on interval type-3 fuzzy logic systems (IT3-FLSs) and an online learning approach is designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems. Unlike the other methods, the dynamics of battery, PV and boost converters are considered to be fully unknown. Also, the effects of variation of temperature, radiation, and output load are taken into account. The robustness and the asymptotic stability of the proposed method is analyzed by the Lyapunov/LaSalle’s invariant set theorems, and the tuning rules are extracted for IT3-FLS. Also, the upper bound of approximation error (AE) is approximated, and then a new compensator is designed to deal with the effects of dynamic AEs. The superiority of the proposed method is examined in several conditions and is compared with some other well-known methods. It is shown that the schemed method results in high performance under difficult conditions such as variation of temperature and radiation and abruptly changing in the output load.

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

Journal ArticleDOI
TL;DR: In this paper, an adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system.
Abstract: An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator (SFRM) with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system. By designing a performance function, the transient performance of the control system is guaranteed. To stabilize the SFRM, a dynamic signal is applied to handle the unmodeled dynamics. To cut down the communication load of the channel, the event-triggered control law is developed based on the switching threshold strategy. The Lyapunov stability theory and backstepping technique are applied coordinately to design the control strategy. The semiglobally ultimately uniformly boundedness can be ensured for all signals in the closed-loop system. The designed control method can also guarantee that the tracking error can converge to a small neighborhood of zero within the prescribed performance boundaries. At the end of the article, two illustrative examples are shown to validate the designed event-triggered controller.

Journal ArticleDOI
TL;DR: A backstepping-based quantized control algorithm is extended to nonlinear systems with unmodeled dynamics and non-strict-feedback structure and the semi-globally asymptotic tracking control scheme is independent of the quantized parameter.

Journal ArticleDOI
TL;DR: A novel control strategy based on a broad fuzzy neural network (BFNN) which is subjected to contact with the unknown environment and an adaptive impedance learning is developed to establish the optimal interaction between the robot and the environment.
Abstract: This article proposes a novel control strategy based on a broad fuzzy neural network (BFNN) which is subjected to contact with the unknown environment. Compared with the conventional fuzzy neural network (NN), a prominent feature can be achieved by taking the advantage of the broad learning system (BLS) to explicitly tackle the problem of how to choose a sufficient number of NN units to approximate the unknown dynamic model. Aiming at providing a soft compliant contact scheme without the requirement of the environment model, an adaptive impedance learning is developed to establish the optimal interaction between the robot and the environment. Meanwhile, the problems related to the state constraints are addressed by incorporating a barrier Lyapunov function (BLF) into the design of a trajectory tracking controller. The proposed method can achieve desired tracking and interaction performance while guaranteeing the stability of the closed-loop system. In addition, simulation and experimental studies are performed to verify the effectiveness of BFNN under optimal impedance control with a two degree-of-freedom (DOF) manipulator and a Baxter robot, respectively.

Journal ArticleDOI
01 Aug 2021
TL;DR: In this study, a hybrid power system with the application of the Tidal Power Unit (TPU) and Vehicle-to-Grid (V2G) is effectively planed as an isolated MG and a new fractional gradient descent based on a single-input interval type-2 fuzzy logic controller (SIT2-FLC) is suggested as the main LFC controller.
Abstract: The high-penetration of distributed generation technologies in the form of MicroGrids (MGs), in recent years, has increased the risk of frequency instability since their energy is supplied by renewable energy resources (RESs) with uncertain nature. Under such circumstances, providing an MG model with an efficient load frequency control (LFC) has a fundamental role in restoring the stability of the unstructured power system. In this study, a hybrid power system with the application of the Tidal Power Unit (TPU) and Vehicle-to-Grid (V2G) is effectively planed as an isolated MG. A new fractional gradient descent (FGD) based on a single-input interval type-2 fuzzy logic controller (SIT2-FLC) is suggested as the main LFC controller, where the footprint of uncertainty (FOU) coefficient of the SIT2-FLC is specifically adjusted to enhance the LFC performance. Additionally, a deep deterministic policy gradient (DDPG) with the actor-critic framework is considered to generate the supplementary control action, which is useful for the frequency stabilization by adapting to the randomness of load disturbances and RESs. Lastly, a model-in-the-loop (MiL) simulation is conducted to appraise the feasibility and applicability of the suggested design method from a systemic perspective.