Adaptive Fuzzy Control Algorithm Research Articles

Fuzzy adaptive asynchronous sampled-data consensus control for nonlinear multi-agent systems under DoS attacks

This article investigates the problem of fuzzy adaptive resilient asynchronous sampled-data consensus control (SDCC) for a class of uncertain nonlinear strict-feedback multi-agent systems (MASs) under denial-of-service (DoS) attacks and with a directed graph. Firstly, the upper bounds on the sampling intervals and a distributed sampled-data observer are proposed to estimate the leader's output and its high-order derivatives under DoS attacks. Then, the sufficient conditions to ensure the asymptotic convergence of the designed distributed sampled-data observer are established. Based on the distributed sampled-data observer and backstepping control design technique, a distributed fuzzy adaptive resilient control algorithm is developed. It is proved that the proposed control scheme can guarantee that the controlled MASs are stable, and the consensus errors converge to a small neighbourhood of zero. Finally, a simulation example on heterogeneous vehicular platoons verifies the feasibility of the proposed control scheme.

Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Fuzzy control system design for landscape lamp of environment art based on multi-function

With the material needs that people constantly meet, the people begin to pursue the spiritual abundance, and the environmental art landscape lamp is thus born. At present, most of the landscape lamp controllers are realized through the full hardware circuit, and there are some defects such as troublesome installation, high maintenance cost and poor control effect. In view of this situation, this study uses the fuzzy control algorithm to establish the fuzzy control system of environmental art landscape lamps, aiming to optimize the accuracy and effect of light control. Firstly, the composition structure and lighting scene mode of the environmental art landscape lamp are researched, and then the intelligent light control system is designed by the adaptive fuzzy control algorithm. The results showed that the control accuracy of adaptive fuzzy control algorithm was above 95, and the running time of the system was within 1 minute. In addition, both experts and users scored above 9.5 in terms of satisfaction with the control system. To sum up, the light fuzzy control system designed in this study has good control performance and certain practical application value.

Fixed-time self-triggered fuzzy adaptive control of N-link robotic manipulators

This paper studies the fixed-time fuzzy adaptive self-triggered control of n-link robotic manipulators. To achieve fixed-time stability, a fuzzy adaptive fixed-time control algorithm is proposed by reconstructing the self-triggered controller and using the singularity-free function, which avoids the singularity problem and obtains fixed-time stability. Meanwhile, the self-triggered mechanism reduces the communication resources between the actuator and the controller, improving the system's transmission efficiency. Through the above thoughts, a kind of fixed-time fuzzy adaptive controller based on the self-triggered mechanism is designed, which guarantees that the tracking error of each joint converges to the prescribed range within a fixed time. Finally, the effectiveness of the proposed algorithm is verified by a simulation example of the two-link robotic manipulator.

Adaptive Fuzzy Fixed-Time Control for Uncertain Nonlinear Systems with Mismatched Disturbances

This paper focuses on addressing the adaptive fuzzy fixed-time issue for a class of nonlinear systems with uncertainty functions and mismatched disturbances. Fuzzy logical systems are utilized for identifying unknown functions. Additionally, to tackle challenges posed by mismatched disturbances, disturbance observers are constructed based on the backstepping method. Utilizing the adding one power integrator approach and the fixed-time control method, this paper introduces a fixed-time adaptive fuzzy control algorithm. Notably, this algorithm accommodates the presence of unknown mismatched disturbances and nonlinear functions. The paper establishes, through the application of the Lyapunov stability theory, that the designed adaptive fixed-time fuzzy control algorithm ensures practical fixed-time stability for the resulting closed-loop systems. Finally, the effectiveness of the derived strategy is demonstrated through an illustrative example involving two cases.

Predefined Time and Accuracy Adaptive Fault-Tolerant Control for Nonlinear Systems with Multiple Faults

This work mainly studies the issue of predefined time and accuracy adaptive fault-tolerant control for strict-feedback nonlinear systems with multiple faults. The faults in the controlled system include actuator faults and external system faults. The unknown functions for nonlinear systems are approximated by fuzzy logic systems (FLSs). And then, according to the backstepping technique and the predefined time stability theory, an adaptive fuzzy control algorithm is presented, which can make sure that all closed-loop system signals remain predefined time bound and the tracking error converges to a predefined accuracy within the predefined time. Ultimately, the effectiveness of the presented control algorithm is proved through two simulation examples.

Active disturbance rejection adaptive fuzzy backstepping control of uncertain flexible marine riser system

In this paper, vibration control of a flexible marine riser with uncertain parameters subjected to distributed disturbance and boundary disturbance is studied. The existing vibration control algorithms have the problems of relying on the accurate mathematical model of the system, complex control laws, and need to measure multiple states of the system, which cannot effectively deal with the uncertainty of model parameters and external disturbances. To effectively suppress the vibration of a marine flexible riser system with uncertain parameters, an adaptive fuzzy backstepping control algorithm was designed. First, the backstepping method and Lyapunov function are used to analyze the subsystem and design the virtual control law. On this basis, an adaptive fuzzy system was designed. The fuzzy system was used to approach the unknown nonlinear term in the design process of the control law and the uniform boundedness of the system was proved by the Lyapunov stability theory. The designed control algorithm has a simple structure, and it can effectively restrain the riser vibration and improve the control rate. Finally, the numerical simulation results show that the designed control algorithm is effective in suppressing the vibration of flexible risers and the control effect is better than previous PID control and robust adaptive control.

Fuzzy adaptive control of coal gangue sorting parallel robot with variable load

ABSTRACT Delta parallel robot is widely used in the high-speed sorting industry with a light load and constant load. Its motion control is based on kinematic planning, without considering the influence of load and disturbance. The gangue sorting robot has the characteristics of a large load and random mass change, so traditional kinematics planning can not meet the control requirements. A fuzzy adaptive robust control algorithm is proposed to solve the problems of unstable motion and jitter in the coal gangue sorting robot. By establishing the mathematical model of the fuzzy system, the uncertain terms of the dynamic parameters caused by the random changes in the load are approximated. At the same time, to reduce the number of fuzzy rules and the amount of fuzzy calculation, the uncertain terms are decomposed into velocity terms and acceleration terms and then approximated, respectively. The Lyapunov analysis of the system is carried out, and the convergence of the system is proved. The simulation results show that the fuzzy adaptive robust control algorithm has a smaller error and faster convergence compared with the computational torque control algorithm when the system is sorting gangue within 8 kg, which improves the stability of gangue grasping.

Practically Predefined-Time Adaptive Fuzzy Tracking Control for Nonlinear Stochastic Systems.

This article addresses the practically predefined-time adaptive fuzzy tracking control problem of strict-feedback nonlinear stochastic systems, where the system under consideration includes stochastic disturbances and uncertain parameters. First, in this study, practically predefined-time stochastic stabilization (PPSS) in the p th moment sense is introduced, and a Lyapunov-type criterion for PPSS is proposed to assure the stabilization of the system considered. With these ideas, based on the backstepping design method, a semiglobally practically predefined-time adaptive fuzzy tracking control algorithm is proposed with a fuzzy system used to approximate the unknown part of the system. Moreover, the settling time of the system response can be arbitrarily adjusted in a mean-value sense, and such freedom can be used to improve the stochastic finite-/fixed-time control results. Finally, a practical example and a numerical example of a comparison are provided to validate the effectiveness of the proposed control strategy.

Adaptive fuzzy command–filtered control strategy for electro-hydraulic braking systems of winding hoist with prescribed performance

In the braking control of winding hoisting systems, proper braking strategy is of great significance in reducing the probability of significant hidden dangers and improving the safety and reliability of the system. However, as a typical elastic and variable stiffness system, the winding hoisting systems have large nonlinearity, parameter uncertainties, random disturbances, and so on, which severely restrict the improvement of braking performance. To this end, an adaptive fuzzy command–filtered controller with prescribed performance is developed for electro-hydraulic braking systems of winding hoist in this article. First, the dynamic model of the winding hoisting system is established with consideration of the elasticity of wire rope. Based on the established dynamic model, using the fuzzy logic system, the algebraic loop problem caused by non-strict feed structure is overcome, and the unknown nonlinearities arising from modeling error, parameter uncertainties, and system disturbances are also approximated for the subsequent active unknown nonlinearities compensation. Next, to enhance the convergence rate and guarantee the position tracking error within a prescribed region, a tracking error transformation method based on the prescribed performance function is utilized. Subsequently, the adaptive fuzzy control algorithm is developed with command filters, which can also avoid the complexity explosion issue due to the analytic calculation of the derivatives of stabilizing functions, and the rigorous stability of the close-loop system is proved by the Lyapunov theory. Finally, experimental results are utilized to illustrate the effectiveness of the developed controller.

Non-Invasive Intraoral Stand-Alone Tongue Control System Based on RSIC-V Edge Computing

The intelligent tongue control system is of great significance for assisting the independent life of patients with a limb disability. In order to more accurately control the assisted living equipment of incompetent patients and solve the power-loss problem of the intelligent tongue control system, this research designs a non-invasive pressure sensor array for tongue touch signal detection in the oral cavity and proposes a tongue control system based on RSIC-V edge computing. The system converts the tongue touch pressure data into specific control instructions on the edge of the RSIC-V chip and transmits them to the receiver, thus reducing the transmission of data. This study takes control of the wheelchair motor as the test object. In the experiment, the speed response time test, the center click task, and the power consumption experiment are carried out, whose results show that the adaptive fuzzy PID control algorithm has good robustness in the system; when the DC motor with a given speed of 750 r/min reaches the steady state, its rise time is 0.108 s and the adjustment time is 0.59 s. The dynamic power consumption of the non-intrusive intraoral stand-alone tongue control system proposed in this paper is found to be 3.745 MW, which is 11.5% lower than the total power consumption of the sTD system.

Event-Based Fuzzy Adaptive Consensus Tracking for Stochastic High-Order Nonlinear Multiagent Networks With Specified-Time Convergence

In this paper, a specified-time event-triggered fuzzy adaptive control algorithm is developed to solve the consensus tracking control problem for stochastic high-order nonlinear multi-agent networks, which is intrinsically challenging due to the existence of stochasticity and high-order (positive odd integers greater than one) terms. More precisely, a novel specified-time performance function (STPF) is incorporated into time-varying high-order tan-type barrier Lyapunov function to guarantee that the tracking errors remain under time-varying constraints within specified time. Combining fuzzy logic systems with the adding on power integrator technique, an adaptive approximation policy is introduced to handle the system uncertainties. Moreover, a new switching threshold event-triggered mechanism is devised to determine the control signals updating instants, which reduces the transmission and computation burden, while resizing the triggering threshold in real-time. The Zeno phenomenon is excluded by guaranteeing that the triggering intervals is lower bounded by a positive constant. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.

Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot

To improve environmental quality in enclosed piggeries, a monitoring and control system was designed based on a track inspection robot. The system includes a track mobile monitoring platform, an environmental control system, and a monitor terminal. The track mobile monitoring platform consists of three main components: a single-track motion device, a main box containing electronic components, and an environmental sampling device. It is capable of detecting various environmental parameters such as temperature, humidity, NH3 concentration, CO2 concentration, light intensity, H2S concentration, dust concentration, and wind speed at different heights below the track. Additionally, it can control on-site environmental control equipment such as lighting systems, ventilation systems, temperature control systems, and manure cleaning systems. The networked terminal devices enable real-time monitoring of field equipment operating status. An adaptive fuzzy PID control algorithm is embedded in the system to regulate the temperature of the piggery. Field tests conducted on a closed nursery piggery revealed that the system effectively controlled the maximum temperature range within 2 °C. The concentrations of CO2, NH3, and PM2.5 were maintained at a maximum of 1092 mg∙m−3, 16.8 mg∙m−3, and 35 μg∙m−3, respectively. The light intensity ranged from 51 to 57 Lux, while the wind speed remained stable at approximately 0.35 m∙s−1. The H2S concentration was significantly lower than the standard value, and the lowest relative humidity recorded was 18% RH at high temperatures. Regular humidification is required in closed piggeries and other breeding places when the system does not trigger the wet curtain humidification and cooling function, as the relative humidity is lower than the standard value. By controlling the temperature, the system combined with a humidification device can meet environmental requirements. The control method is simple and effective, with a wide range of applications, and holds great potential in the field of agricultural environmental control.

Establishing a novel adaptive fuzzy control algorithm for an active stabilizer bar with complex automotive dynamics model

Rollovers can cause many hazards to the passengers in the car. The main cause of this problem is the driver's sudden steering at high speed to avoid an obstacle or enter a roundabout. To limit this phenomenon, it is necessary to equip the car with an active stabilizer bar. In this article, we use a new fuzzy control method to direct an active stabilizer bar. This method is established based on five points of view regarding the stability and responsiveness of the system, so it is called the novel adaptive fuzzy algorithm (NAFC). This is a completely new point in the article compared to previous studies. A combined dynamics model that is a fusion of the seven DOFs spatial dynamics model and the three DOFs motion model. The simulation and calculation occur in the Simulink® interface, with two cases representing the steering angles J-turn and Fish-hook. The speed of the car is changed according to three specific values. According to the calculation results, the vehicle body roll angle is greatly reduced when using active stabilizer bars controlled by the novel adaptive fuzzy algorithm. Besides, the minimum dynamic force value at the rear wheel is better guaranteed. The car's roll index is also sharply reduced when applying this algorithm to the hydraulic stabilizer bar model. In addition, rollover occurs when the car steers the J-turn at v3 and the fish hook at v2 and v3 (if stabilizer bars are unavailable). Meanwhile, car’s stability is always guaranteed in the case of a car with a stabilizer bar using the NAFC algorithm. The article's achievements can be used as a basis for developing more complex control algorithms in the future.

A Novel Cooperative Design for USV–UAV Systems: 3-D Mapping Guidance and Adaptive Fuzzy Control

This note investigates a novel cooperative design for path following of the mixed-order underactuated surface vehicle (USV) and unmanned aerial vehicle (UAV) in presence of the structure uncertainties and external disturbances. The designed cooperative scheme is comprised of the 3 dimension (3D) mapping guidance and adaptive fuzzy control algorithm. The 3D mapping guidance is developed to provide the reference signals of the yaw degree of freedom for the USV and UAV by utilizing the equivalent mapping technique. To control the USV-UAV converge to the desired path, the adaptive fuzzy control algorithm is designed for the position and attitude loops by fusing the dynamic surface control (DSC) and the Backstepping techniques. The position loop can generate the thrust inputs for the USV-UAV and provide the desired rolling and pitching angles for the UAV by using nonlinear decoupling method. In the proposed algorithm, the model uncertainties are approximated by the fuzzy logic system and only eight norm-based adaptive parameters are required to be updated online. Based on the Lyapunov theorem, the stability of the position loop and the attitude loop for the USV-UAV systems is achieved and all signals in the cooperative systems are the semi-global uniform ultimate bounded (SGUUB). The superiorities and the effectiveness of the proposed strategy are verified through the numerical experiment on the MATLAB platform under the external disturbances.

An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform.

In the platform gravimeter, the stabilization accuracy of the gravimetric stabilization platform is crucial to improve the accuracy of gravity value measurements due to its uncertainties, such as mechanical friction, inter-device coupling interference, and nonlinear disturbances. These cause fluctuations in the gravimetric stabilization platform system parameters and present nonlinear characteristics. To resolve the impact of the above problems on the control performance of the stabilization platform, an improved differential evolutionary adaptive fuzzy PID control (IDEAFC) algorithm is proposed. The proposed enhanced differential evolution algorithm is used to optimize the initial control parameters of the system adaptive fuzzy PID control algorithm to achieve accurate online adjustments of the gravimetric stabilization platform's control parameters when it is subject to external disturbances or state changes and attain a high level of stabilization accuracy. The results of simulation tests, static stability experiments, and swaying experiments on the platform under laboratory conditions, as well as on-board experiments and shipboard experiments, all show that the improved differential evolution adaptive fuzzy PID control algorithm has a higher stability accuracy compared with the conventional control PID algorithm and traditional fuzzy control algorithm, proving the superiority, availability, and effectiveness of the algorithm.

Research on Retired Battery Equalization System Based on Multi-Objective Adaptive Fuzzy Control Algorithm

Research on Retired Battery Equalization System Based on Multi-Objective Adaptive Fuzzy Control Algorithm

Finite-time adaptive fuzzy output tracking of switched nonlinear systems with ISD-ADT

This article is devoted to the initial state-dependent average dwell time (ISD-ADT) based output feedback control issue for switched systems. First, a switched observer is given to estimate the unmeasurable states. Based on the ISD-ADT method and backstepping technique, a novel switching law is established. Second, based on the smooth switch function (SSF), a novel fuzzy adaptive finite-time control algorithm is recursively fabricated, which availably surmounted the singularity hindrance derived from the differentiating virtual control law. Furthermore, the proposed output feedback control approach can steer the tracking error into an arbitrarily small residual set in a finite time. In addition, different switched filters and controllers are introduced to reduce the conservatism and restriction caused by employing the common filter and controller for different subsystems. Ultimately, the theoretical findings are validated through two simulation examples.

Adaptive Fuzzy Iterative Learning Control for Systems with Saturated Inputs and Unknown Control Directions

An adaptive fuzzy iterative learning control (ILC) algorithm is designed for the iterative variable reference trajectory problem of nonlinear discrete-time systems with input saturations and unknown control directions. Firstly, an adaptive fuzzy iterative learning controller is constructed by combining with the fuzzy logic system (FLS), which can compensate the loss caused by input saturation. Then, the discrete Nussbaum gain technique is adopted along the iteration axis, which can be embedded to the learning control method to identify the control direction of the system. Finally, based on the nonincreasing Lyapunov-like function, it is proven that the adaptive iterative learning controller can converge asymptotically when the number of iterations tends to infinity, and the system signals always remain bounded in the learning process. A simulation example verifies the feasibility and effectiveness of the learning control method.