Adaptive Fuzzy Control Research Articles

A New Event-Triggered Adaptive Fixed-Time Control Design for Uncertain Nonlinear Systems.

This article investigates the problem of dynamic memory event-triggered (DMET) fixed-time tracking control within time-varying asymmetric constraints for nonaffine nonstrict-feedback uncertain nonlinear systems with unmodeled dynamics and unknown disturbances. The existing dynamic event-triggered control methods cannot handle the nonlinear systems with unmodeled dynamics and nonaffine inputs, which greatly limits the applicability of the strategy. To this end, a novel DMET adaptive fuzzy fixed-time control protocol is constructed based on the idea of command filtered backstepping, in which a new dynamic signal function is established to deal with the unmodeled dynamics and an improved DMET mechanism (DMETM) is designed to solve the problem of nonaffine inputs. It is proved that the newly DMET control strategy ensures the tracking error converges to an arbitrarily small compact set in a fixed time and all the signals of the closed-loop systems are bounded. The effectiveness of the proposed approach is demonstrated by two simulation examples.

Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production

Abstract Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

Adaptive Fuzzy Output Feedback Fault-Tolerant Control for Active Suspension Systems

In this paper, a novel adaptive fuzzy output feedback fault-tolerant control (FTC) method is proposed for active suspension systems. The quarter active suspension systems studied in this paper contains electromagnetic actuator faults and unavailable state variables, such as the velocity of active suspensions and the electric current intensity of electromagnetic actuators. First, the complex spring nonlinear dynamics are approximated by fuzzy logic systems (FLSs), and the unmeasurable states are estimated by a fuzzy state observer. Based on an adaptive backstepping technique along with specific Lyapunov functions, a novel adaptive fuzzy output feedback FTC design is presented. To eliminate the need for knowing efficiency indexes, a fault compensation strategy is also developed by utilizing parameter estimation technique. In the presence of electromagnetic actuator failure, all vertical vibration states of the active suspension system remain stabilized. Eventually, the feasibility of the proposed control algorithm is evaluated through random road surface and bump road surface tests.

Robust fuzzy adaptive yaw moment control of humanoid robot with unknown backlash nonlinearity

To achieve an excellent dynamic balance during humanoid robot walks, it is essential to counteract the undesired yaw moment and deal with the nonlinearities existing in the system dynamics. In this paper, the problems of the dynamic balance optimization and yaw moment control for humanoid robot are investigated. First we formulate a constrained dynamic model of compliant joints for robots. Then, a yaw moment control approach based on quadratic objective function is proposed for computing optimal ankle torque to equilibrate the undesired yaw moment. In order to deal with the complicated optimization problem with inequality and equality constraints, a Quadratic Programming (QP) solver based on Linear Variational Inequality (LVI) is adopted. In comparison with the yaw moment control approaches studied previously, the main contributions of this paper are summarized as follows. 1) our newly proposed control scheme is more effective in counteracting the undesired yaw moment for the case of handling object-carrying tasks during walking. 2) a novel adaptive backlash inverse is included in the proposed scheme to cancel the unknown actuator backlash nonlinearity without a prior knowledge of the backlash nonlinearity. By the strict Lyapunov argument, the asymptotic convergence of the system tracking error to arbitrarily small neighborhood of the origin is proved. The simulation examples involving humanoid robot walking are constructed to validate the proposed algorithm.

Observer-Based Fuzzy Adaptive Fault-Tolerant Control for NSC System of USV With Sideslip Angle and Steering Machine Fault

Observer-Based Fuzzy Adaptive Fault-Tolerant Control for NSC System of USV With Sideslip Angle and Steering Machine Fault

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems With Output Constraints and DoS Attacks.

In this article, the fuzzy adaptive event-triggered consensus control issue is addressed for nonlinear multiagent systems (MASs) under output constraints and Denial of Service (DoS) attacks. First of all, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions. Then, a novel switching observer is constructed to observe the leader's state and handle DoS attacks. With the help of the exponent-dependent barrier Lyapunov functions (BLFs), the system output can be constrained within a preset region. Based on dynamic surface control (DSC) technique, the issue of computational complexity can be effectively avoided. Combining the designed switching observer and relative thresholds, a robust fuzzy adaptive event-triggered controller is developed, which ensures that the consensus output tracking errors converge to a small neighborhood of zero, and all signals in the closed-loop system keep bounded. Moreover, Zeno behavior can be avoided. Ultimately, simulation results are given to validate the feasibility and effectiveness of the proposed control strategy and theory.

Fuzzy Observer-Based Finite-time Adaptive Formation Control for Multiple QUAVs with Malicious Attacks

Fuzzy Observer-Based Finite-time Adaptive Formation Control for Multiple QUAVs with Malicious Attacks

Tracking Control of High-Order Nonlinear Systems With Unknown Control Gains and Its Application: An Adaptive Fuzzy Control Method

Tracking Control of High-Order Nonlinear Systems With Unknown Control Gains and Its Application: An Adaptive Fuzzy Control Method

Finite‐Time Adaptive Fuzzy Control for Stochastic Nonlinear Systems with Input Quantization and Actuator Faults

Finite‐Time Adaptive Fuzzy Control for Stochastic Nonlinear Systems with Input Quantization and Actuator Faults

Leader-Following Consensus of Multiple Uncertain Euler-Lagrange Systems via Fully Distributed Event-Triggered Adaptive Fuzzy Control.

This article deals with the leader-following consensus problem of multiple uncertain Euler-Lagrange systems with unknown nonlinear dynamics. By introducing a dynamic compensator for each agent, a fully distributed control strategy is developed based on the fuzzy approximation approach, which is independent of any priori global information associated with the communication topology. Meanwhile, a distributed event-triggering mechanism (ETM) is designed such that each agent broadcasts its states only when an event occurs. It is shown that with the proposed ETM, the leader-following consensus is achieved with aperiodic intermittent communication and Zeno behavior is excluded by contradiction. Moreover, the consensus tracking errors converge to small sets around the origin. Finally, an example is provided to illustrate the effectiveness of the obtained theoretical results.

Adaptive Fuzzy Control for Stochastic Nonlinear Systems With Delayed Input and Delayed State Via Improved Lyapunov-Krasovskii Function

Adaptive Fuzzy Control for Stochastic Nonlinear Systems With Delayed Input and Delayed State Via Improved Lyapunov-Krasovskii Function

Fixed-Time Adaptive Fuzzy Control via Filter and Observer for Uncertain Nonlinear Systems With Disturbances and Its Application in PMSMs

Fixed-Time Adaptive Fuzzy Control via Filter and Observer for Uncertain Nonlinear Systems With Disturbances and Its Application in PMSMs

Robustness of the Fuzzy Adaptive Speed Control of a Multi-Phase Asynchronous Machine

Fuzzy controllers are a powerful tool for controlling complex processes. However, its robustness capacity remains moderately limited because it loses its property for large ranges of parametric variations. In this paper, the proposed control method is designed, based on a fuzzy adaptive controller used as a remedy for this problem. For increase the robustness of the vector control and to maintain the performance of the five-phase asynchronous machine despite the presence of disturbances (variation of rotor resistance, rotor inertia variations, sudden variations in the load etc.), by applying the method of behaviour model control (BMC). The results of simulation show that the fuzzy adaptive control provides best performance and has a more robustness as the fuzzy (FLC) and as a conventional (PI) controller.

Development of a fuzzy controller for adaptive control of the adsorption process

In this paper presents the development of a fuzzy controller for adaptive control of the parameters of the activated carbon adsorption process. The main objective of the developed control system is to increase the efficiency of the control of the technological process of adsorption by using intelligent technologies. It should be noted that the methodological basis for the design of fuzzy controllers is the concept of fuzzy logic. Also, for the development of fuzzy controllers for an adaptive control system, a model of the absorption process in the adsorber is constructed, taking into account multiple relationships between the parameters of the technological process. The conceptual model of the control object was developed taking into account the inherent internal relationships between the parameters of the technological regime and external disturbing factors. That is, fuzzy adaptive control allows you to adapt to changes in the technological parameters of the adsorption process by changing the control algorithm of the technological process. The expediency of using for adaptive control of technological parameters of the adsorption process by using fuzzy controllers is substantiated. The development of an adaptive control system based on fuzzy controllers was carried out using MatLab software, where a control model of the adsorption technological process was built, and the results were obtained and analyzed. Thus, fuzzy controllers evaluate critical situations and also regulate the parameters of the adsorption process based on logical rules. Keywords: adsorption; activated carbon; ethanol; fuzzy controller; system control; process parameters; adaptive control; fuzzy logic.

RHYTHMS: Real-time Data-driven Human-machine Synchronization for Proactive Ergonomic Risk Mitigation in the Context of Industry 4.0 and Beyond

Human-machine work system (HMWS) represents a composite system that integrates machine precision and strength with human cognitive abilities, flexibility, and adaptability, aiming to achieve enhanced performance in various tasks aligned with a common goal, which is getting smarter in the era of Industry 4.0 (I4.0) and has received extensive attention from academia and industry. However, the lack of real-time information sharing during HMWS implementation and operation, coupled with uncertainties stemming from human instability and the complexities introduced by smart networking environments in the context of I4.0, poses challenges to the synchronous coordination of this distributed human-centric manufacturing system. Failure in synchronization not only compromises overall system integrity, leading to unrealistic decision-making and potential harm to human health but also goes against the principles of Industry 5.0 (I5.0), which proposes placing human well-being at the center of the production process for human-centric smart manufacturing in the future. In this context, this research presents real-time data-driven hyper objects orchestrating for the human-machine synchronization (RHYTHMS) based on a service-oriented human-to-machine architecture (SOH2M) and a model reference adaptive fuzzy control for real-time information sharing and synchronous coordination of the smart HMWS. Based on these, a real-life assembly case is carried out by a full-scale HMWS prototype to quantitatively analyze the benefits of the proposed approach in proactive ergonomic risk mitigation (PERM), contributing to the human-centric value-oriented I5.0 era.

A cascade fuzzy adaptive based interaction torque control of a pneumatically actuated forearm rehabilitation robot under disturbance effects

In this study, an intelligent adaptive interaction torque control for a pneumatically actuated forearm rehabilitation robot has been proposed. The main objective is to provide a haptic environment that ensures stable interaction torque fields at changing levels. To achieve this goal, a cascade fuzzy adaptive controller, that is specifically tailored to handle varying levels of interaction torque and ensure stability throughout the rehabilitation process, has been designed. To improve the efficiency of the controller, non-linear friction torque identification of the pneumatic actuator based on changing operating conditions has been conducted. Parallel to this, a user motion intention detection algorithm has been designed to provide compliant, safe and suitable human-robot interactions. The disturbance cases have been considered to make the system robust to unknown conditions. Stability analysis has been performed, specifically focusing on the boundary-input boundary-output (BIBO) stability conditions. In order to demonstrate the superior performance of the proposed cascade fuzzy adaptive algorithm, a cascade PID algorithm has also been meticulously designed for comparison. Numerous experimental validation tests involving a healthy user were conducted in a Hardware-in-the-Loop environment, focusing on torque trajectory tracking performance. The proposed control technique exhibited improved convergence dynamics compared to the cascade PID algorithm, yielding mean absolute error levels of 0.0218 Nm and 0.099 Nm for target interaction torque under disturbance-free and disturbed conditions, respectively.

Predefined Time Fuzzy Adaptive Control of Switched Fractional-Order Nonlinear Systems with Input Saturation

Article Predefined Time Fuzzy Adaptive Control of Switched Fractional-Order Nonlinear Systems with Input Saturation Lusong Ding, and Weiwei Sun * 1 Institute of Automation, Qufu Normal University, Qufu 273165, China * Correspondence: wwsun@hotmail.com Received: 31 August 2023 Accepted: 31 October 2023 Published: 21 December 2023 Abstract: This article investigates the predefined-time fuzzy adaptive tracking control problem for a class of nonlinear switched fractional-order systems with input saturation and external disturbances under a nonstrict feedback structure. By combining the backstepping technique and the common Lyapunov function method, a predefined-time switching control method is constructed based on a novel fractional-order auxiliary function. The fuzzy logic system and the adaptive method are introduced to identify unknown compounded continuous functions. Moreover, the issue of calculating explosion and the problem of singularity are tackled through the newly proposed predefined-time and filter-based dynamic surface control. Especially, the construction of a continuous term in the controller eliminates possible chattering. The developed control strategy achieves that the closed-loop system is practically predefined-time stable under arbitrary switchings, where the upper bound of the settling-time can be defined by users in advance. Finally, two simulation examples are illustrated to prove the feasibility and effectiveness of the presented scheme.

Prescribed performance event-triggered fuzzy optimal tracking control for strict-feedback nonlinear systems

The prescribed performance event-triggered optimal tracking control problem is considered for a class of uncertain strict-feedback nonlinear systems with external disturbances. First, the disturbance observers are constructed to estimate external disturbances. Then, the controller contains an adaptive fuzzy controller and an optimal compensation term, in which the unknown nonlinearities and cost function are approximated by the fuzzy logic systems. An adaptive fuzzy controller is established by the dynamic surface control (DSC) technique, which is addressed to handle “computation complexity” issue occurred in conventional backstepping approach. Based on adaptive dynamic programming (ADP) method, an optimal compensation term is developed by minimizing the cost function. Furthermore, communication load is reduced via embedding event-triggered mechanism. In addition, the tracking error can be restricted in the prescribed region with the aid of the prescribed performance control. Thus, the whole control scheme can not only ensure that the tracking error converges to a boundary but also achieve optimization, reduce the communication burden and avoid “computation complexity” issue. The boundedness of all signals in the closed-loop is proved. Finally, the simulation examples illustrate the validity of the designed control scheme.

Adaptive fuzzy finite-time event-triggered formation control for unmanned surface vehicle systems

This paper studies the finite-time event-triggered fuzzy formation control problem for multiple unmanned surface vehicle (USV) system. The fuzzy state estimator is designed by intermittent output signal to estimate unmeasurable velocity and yaw velocity of the USV. The dual-channel event-triggered mechanisms (ETMs) are designed to reduce the updating times from sensor-to-actuator and controller-to-actuator channels, respectively. Based on the designed fuzzy state estimator and ETMs, a finite-time fuzzy adaptive output feedback formation control is formulated. It is proved that the controlled USV system is semi-global practically finite-time stable. Moreover, the outputs of the followers are able to track the output of the leader. Finally, the simulation and comparison results are provided to demonstrate the feasibility of the presented formation control algorithm.

Adaptive fuzzy iterative learning control based neurostimulation system and in-silico evaluation.

Closed-loop neural stimulation has been an effective treatment for epilepsy patients. Currently, most closed-loop neural stimulation strategies are designed based on accurate neural models. However, the uncertainty and complexity of the neural system make it difficult to build an accurate neural model, which poses a significant challenge to the design of the controller. This paper proposes an Adaptive Fuzzy Iterative Learning Control (AFILC) framework for closed-loop neural stimulation, which can realize neuromodulation with no model or model uncertainty. Recognizing the periodic characteristics of neural stimulation and neuronal firing, Iterative Learning Control (ILC) is employed as the primary controller. Furthermore, a fuzzy optimization module is established to update the internal parameters of the ILC controller in real-time. This module enhances the anti-interference ability of the control system and reduces the influence of initial controller parameters on the control process. The efficacy of this strategy is evaluated using a neural computational model. The simulation results validate the capability of the AFILC strategy to suppress epileptic states. Compared with ILC-based closed-loop neurostimulation schemes, the AFILC-based neurostimulation strategy has faster convergence speed and stronger anti-interference ability. Moreover, the control algorithm is implemented based on a digital signal processor, and the hardware-in-the-loop experimental platform is implemented. The experimental results show that the control method has good control performance and computational efficiency, which provides the possibility for future application in clinical research.