Adaptive Fuzzy Control Research Articles

A Novel Hyperchaotic Financial System with Sinusoidal Hyperbolic Nonlinearity: From Theoretical Analysis to Adaptive Neural Fuzzy Controller Method

Chaotic systems are known to be extremely sensitive to initial conditions, meaning small changes can have a significant impact on the outcomes. By analyzing the average profit margin in relation to chaotic dynamics, companies can conduct sensitivity analysis to assess the potential impact of various factors on their profitability. This analysis can help identify critical variables or scenarios that may significantly affect profit margins. In this article, we have proposed a hyperchaotic financial system with sinusoidal hyperbolic non-linear variables applied to the average profit margin. Furthermore, we have investigated the stability of the hyperchaotic financial dynamics model to provide information to companies to assess the consistency and reliability of their profitability. In addition, fundamental dynamic behavior like Lyapunov exponents, bifurcation analysis, coexisting attractors have been reported. Finally, a nonlinear feedback control approach is developed to train an adaptive neural fuzzy controller. The application of Lyapunov theory confirms that this nonlinear feedback controller can effectively minimize the synchronization error within a finite duration. The results from simulations establish the effectiveness of the proposed neural fuzzy controller architecture in controlling the synchronization of two hyperchaotic financial models. Additionally, the simulation includes a comparison between the performance of the nonlinear controller and the adaptive neural fuzzy controller.

Predefined‐time adaptive fuzzy control for a class of stochastic nonlinear uncertain systems

SummaryThis article is mainly concentrated on the predefined‐time adaptive fuzzy control of stochastic nonlinear systems. To handle nonlinear functions that are uncertain, fuzzy logic systems (FLSs) are used to approximate them. Compared with the existing studies, a Lyapunov‐type criterion for practically predefined‐time stochastic stabilization (PPSS) is put forward to guarantee the stabilization of the system. The stabilization time is merely dependent on one design parameter, which means the parameter can be adjusted to set the stability time.

Fixed-time adaptive fuzzy control with prescribed tracking performances for flexible-joint manipulators

In this paper, we study the tracking control problem of flexible-joint manipulators with uncertain unstructured dynamics, and develop a prescribed fixed-time solution scheme by combining direct adaptive fuzzy control design with a class of smooth functions. With our scheme, only one adaptive law needs to be calculated online, and also there are no complicated terms, e.g., the partial derivatives of some multivariable functions, involved in virtual controllers and the actual control input, which show that the raised scheme is computationally attractive. Moreover, the boundedness of all closed-loop signals, and the fixed-time convergence of tracking error to a prescribed interval can be established through a rigorous proof, and the obtained results are further confirmed by simulation tests.

A genetic algorithm for rule extraction in fuzzy adaptive learning control networks

A genetic algorithm for rule extraction in fuzzy adaptive learning control networks

Adaptive fuzzy energy management strategy for range‐extended electric vehicles integrated with deep learning

AbstractA novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques.

Adaptive Event-Triggered Fuzzy Control for Unreliable Networked Control Systems with Time-Varying Delay

Adaptive Event-Triggered Fuzzy Control for Unreliable Networked Control Systems with Time-Varying Delay

Adaptive fuzzy control for tendon-sheath actuated bending-tip system with unknown friction for robotic flexible endoscope.

The tendon-sheath actuated bending-tip system (TAB) has been widely applied to long-distance transmission scenes due to its high maneuverability, safety, and compliance, such as in exoskeleton robots, rescue robots, and surgical robots design. Due to the suitability of operation in a narrow or tortuous environment, TAB has demonstrated great application potential in the area of minimally invasive surgery. However, TAB involves highly non-linear behavior due to hysteresis, creepage, and non-linear friction existing on the tendon routing, which is an enormous challenge for accurate control. Considering the difficulties in the precise modeling of non-linearity friction, this paper proposes a novel fuzzy control scheme for the Euler-Lagrange dynamics model of TAB for achieving tracking performance and providing accurate friction compensation. Finally, the asymptotic stability of the closed-loop system is proved theoretically and the effectiveness of the controller is verified by numerical simulation carried out in MATLAB/Simulink. The desired angle can be reached quickly within 3 s by adopting the proposed controller without overshoot or oscillation in Tracking Experiment, demonstrating the regulation performance of the proposed control scheme. The proposed method still achieves the desired trajectory rapidly and accurately without steady-state errors in Varying-friction Experiment. The angle errors generated by external disturbances are < 1 deg under the proposed controller, which returns to zero in 2 s in Anti-disturbance Experiment. In contrast, comparative controllers take more time to be steady and are accompanied by oscillating and residual errors in all experiments. The proposed method is model-free control and has no strict requirement for the dynamics model and friction model. It is proved that advanced tracking performance and real-time response can be guaranteed under the presence of unknown bounded non-linear friction and time-varying non-linear dynamics.

Fuzzy neural network adaptive AUV control based on FTHGO

ABSTRACT A fuzzy radial basis function neural network (Fuzzy RBFNN) adaptive control scheme, based on fixed-time high gain state observer (FTHGO), is proposed to address the unpredictability of real-time state and composite interference in the trajectory tracking of the fully driven Autonomous Underwater Vehicle (AUV), ensuring fixed-time system convergence regardless of initial conditions. Firstly, a fixed-time backstepping controller is designed and a first-order fixed-time filter is introduced to tackle the differential explosion issue. Secondly, an FTHGO is developed to observe the real-time states of the AUV without assuming global known state signal. Then, the composite interference in the AUV system is effectively compensated by integrating the Fuzzy RBFNN technique. Finally, the fixed-time stability of the entire closed-loop system is proven utilising the Lyapunov stability theory. The effectiveness of the proposed algorithm is proved by the simulation experiment.

Adaptive fuzzy prescribed-time control of high-order nonlinear systems with actuator faults

This study concentrates on the adaptive prescribed-time control of high-order nonlinear systems with actuator faults. Since the actual control signal of high-order nonlinear systems has a non-affine form, the existing results require that the bounds of time-varying actuator fault parameters be known. To break through the above limitation, the non-affine form of the actual control signal is solved in a linear manner, and novel adaptive estimations are designed. Moreover, fuzzy logic systems are adopted to approximate uncertain terms in control design. It is proved theoretically that the system states converge to zero within the prescribed time which can be set in advance, and all signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed control scheme is certificated by a numerical simulation and a practical simulation of the electromechanical system.

Adaptive fuzzy finite-time output feedback fault-tolerant control for MIMO stochastic nonlinear systems with non-affine nonlinear faults

This paper investigates adaptive fuzzy finite-time output feedback fault-tolerant tracking control problem for multiple-input multiple-output (MIMO) stochastic non-strict feedback nonlinear systems with non-affine nonlinear faults and states unmeasured. Firstly, high-gain fuzzy state observers are constructed to overcome the state unmeasured problem in the system. Secondly, the mean-value theorem and input compensation techniques are employed to decouple non-affine nonlinear faults, and the resulting unknown control coefficients problem is handled using Nussbaum functions. In addition, dynamic surface control techniques and error compensation mechanisms are considered in the controller design, which effectively decreases the computational complexity of the control scheme. By combining the adaptive backstepping technique with finite-time control, a novel adaptive fuzzy dynamic surface finite-time output feedback fault-tolerant tracking control algorithm is proposed. The designed controller guarantees that all signals in the stochastic systems are semi-global finite-time stable in probability (SGFSP) as well as the tracking errors can converge to a small neighborhood within the origin in finite-time. Finally, two simulation examples are introduced to verify the effectiveness of the suggested control strategy.

Fuzzy adaptive event-triggered synchronization control mechanism for T–S fuzzy RDNNs under deception attacks

In this paper, a fuzzy-dependent adaptive event-triggered mechanism (FAETM) for synchronizing Takagi–Sugeno (T–S) fuzzy reaction–diffusion neural networks (RDNNs) is developed while considering deception attacks. Firstly, a general neural network model considering both fuzzy logic rules and reaction–diffusion terms is established. Secondly, a FAETM based on an aperiodic sampling period is presented under deception attacks to alleviate the communication burden, wherein the adaptive threshold function is dependent on membership functions. Moreover, a membership-function-dependent asymmetric Lyapunov functional (LF) is constructed, and some positive-definite and symmetric constraints of matrices are removed as a result. Based on the LF method and integral inequality techniques, the H∞ synchronization criteria for the T–S fuzzy RDNNs are presented by linear matrix inequalities (LMIs). Finally, one simulation example is exploited to demonstrate the feasibility and validity of the established method, and the outcome is applied to image secure communication.

Variable Power Tracking Control Strategy of Doubly Fed Induction Generators for Fast Frequency Responses

Frequency regulation and droop control of doubly fed induction generators (DFIGs) can quickly respond to frequency changes and reduce the maximum rate of frequency (MROFF) in power systems. However, due to real-time dynamic changes in the MPPT control loop, the ability to improve the lowest frequency point is limited. Therefore, this article first describes an in-depth analysis of the dynamic characteristics of the incremental power of frequency regulation with droop control using an equivalent linear model. The limitations of improving the lowest frequency point under the influence of dynamic changes in the MPPT control loop are revealed. Secondly, to address the impact of these dynamics, an improved decoupling frequency regulation (IDFR) strategy based on power tracking is proposed, aiming to increase the maximum frequency deviation (MFD) and MROCOF. Then, in order to overcome the difficulty of adjusting control coefficients in the IDFR strategy, an adaptive control coefficient tuning fuzzy control method based on frequency deviation and ROCOF was proposed to flexibly adjust control requirements under various working conditions, thereby improving the control stability and performance of the system and effectively solving the problem of control coefficient allocation. Finally, to verify the frequency regulation performance of the proposed IDFR strategy under various operating conditions, simulations were conducted based on different disturbances and wind conditions. The results show that the proposed IDFR strategy significantly improves the system MFD and MROCOF improvement ability under various conditions.

Event-Triggered Adaptive Fuzzy Control for Strict-Feedback Nonlinear FOSs Subjected to Finite-Time Full-State Constraints

In this article, an event-triggered adaptive fuzzy finite-time dynamic surface control (DSC) is presented for a class of strict-feedback nonlinear fractional-order systems (FOSs) with full-state constraints. The fuzzy logic systems (FLSs) are employed to approximate uncertain nonlinear functions in the backstepping process, the dynamic surface method is applied to overcome the inherent computational complexity from the virtual controller and its fractional-order derivative, and the barrier Lyapunov function (BLF) is used to handle the full-state constraints. By introducing the finite-time stability criteria from fractional-order Lyapunov method, it is verified that the tracking error converges to a small neighborhood near the zero and the full-state constraints are satisfied within a predetermined finite time. Moreover, reducing the communication burden can be guaranteed without the occurrence of Zeno behavior, and the example is given to demonstrate the effectiveness of the proposed controller.

Trajectory tracking control of autonomous space-based simulators for the on-orbit assembly of large space optical telescopes

To enhance the precision of on-orbit assembly for large space optical telescopes, the control of attitude motion in the Autonomous Space-based Simulator (ASS) is imperative. This paper presents a robust finite-time trajectory tracking control method aimed at improving the position and attitude tracking performance of ASS. Considering the dynamic characteristics inherent in ASS, we introduce an improved non-singular fast terminal sliding mode controller (INFTSMC) incorporating an adaptive fuzzy mechanism to attain finite-time error convergence and robust control. The adaptive fuzzy logic control (AFLC) method yields an equivalent gain, eliminating discontinuous switching in terminal sliding mode control (TSMC). This reduction minimizes chattering in control inputs and compensates for uncertainties and time-varying disturbances within the dynamic model. The proposed method’s effectiveness is validated through comprehensive simulation and experimental studies.

Adaptive finite‐time fuzzy secure control for nonlinear MASs under deception attacks

SummaryIn this paper, the problem of adaptive security control for a class of nonlinear multi‐agent systems (MASs) under deception attacks is studied. This scheme aims to design a new adaptive fuzzy control scheme for each subsystem of MASs to reduce the impact of network attacks (from sensor to controller) on state signals, and proposes Nussbaum function to deal with unknown time‐varying control directions. In addition, for multi‐agent systems, a finite‐time convergent security control scheme is designed to ensure the convergence of tracking error and the boundedness of signals in the closed‐loop system. Finally, the effectiveness of the proposed method is verified by a simulation example.

Experimental validation of double-fed induction generator in oscillating water column using circulatory system-based optimization

Abstract This paper validates the optimal operation for a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). This study presents a novel optimization technique called the circulatory system-based optimization (CSBO) approach to develop six adaptive fuzzy logic controllers (AFLCs) with 30 parameters and compare them to chaotic-billiards optimization (C-BO) and genetic algorithm (GA). The proposed controller is also compared with a proportional–integral differential (PID) controller based on a self-adaptive global-best harmony search (SGHS). CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104. The results of using CSBO–AFLCs revealed a fast time response, fast convergence, less overshoot and minimal error compared with those achieved with C-BO–AFLC, SGHS–PID and GA–AFLC during different case studies. The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors. The results show that the CSBO shows better power tracking by 25% as compared with C-BO, by 45% when compared with the GA and by 56% when compared with PID. Moreover, the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO–AFLC. The root mean square of the errors of six controllers using CSBO is improved by 68.27% when compared with GA, by 22.57% when compared with C-BO and by 38.42% when compared with PID. These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.

Design and Simulation of a Multi-Channel Biomass Hot Air Furnace with an Intelligent Temperature Control System

Timely and effective drying of agricultural products is crucial for ensuring the quality and yield of grains. Biomass drying enhances energy utilization and reduces energy pressure. To this end, a novel multi-channel circulating biomass hot air furnace was designed to provide precise control of the heat source for grain drying, thereby improving the efficiency and quality of the drying process. The combustion process utilizes a multi-channel combined air supply to ensure complete combustion of biomass pellet fuel. During the heat exchange process, heat exchange plates isolate hot and cold areas, discharging combustion exhaust, while ensuring a pure air output. Using rapeseed as the drying subject, a temperature controller based on adaptive fuzzy PID was designed, targeting the biomass hot air furnace’s heat exchange system for modeling and verifying the model with the step response method. Model simulations were conducted in Matlab’s Simulink module using both adaptive fuzzy PID and traditional PID controllers, for a given signal. The settling times for the conventional PID and fuzzy PID were 445 s and 364 s, respectively, with overshoots of 20.1% and 6.3%, showing that the fuzzy PID controller performed better in terms of control performance. The validation tests showed that both control methods could maintain the temperature within ±5 °C. Compared to traditional PID control, the adaptive fuzzy PID control achieved a precision of ±3 °C. At the target temperature of 90 °C, the error was reduced to 3.7%, with a stabilization time of 1014 s. The use of fuzzy PID control exhibited better dynamic response characteristics, meeting the drying needs of rapeseed. This study provides a theoretical basis for the structural design and control system design of biomass hot air furnaces.

Fuzzy adaptive PID control for path tracking of field intelligent weeding machine

In order to reduce the influence of external interference on the intelligent weeder, improve the stability of its path tracking system, enhance its weeding effect, and reduce the rate of seedling injury, the motion steering control of the four-wheeled weeder is studied. The control strategy of fuzzy adaptive proportional integral differential (PID) algorithm is determined by establishing the path tracking mathematical model of the field intelligent weeder; the fuzzy adaptive PID controller is designed with the input average steering angle deviation of the front wheels and the rate of change of the deviation so as to realize the automatic adjustment and optimization of the parameters. We use Simulink to build the control system model and to compare and analyze with the PID controller. The results show that under the action of the step signal, the rise time of the fuzzy adaptive PID and PID control system responses is 2.596 and 4.209 s, respectively; under the action of the impulse, the fuzzy adaptive PID has a significant advantage over the traditional PID control system in terms of the rise time and response time. In addition, this control system has a fast response speed, high adaptability, high anti-interference ability, and superior path tracking ability, which are necessary for improving the accuracy of the field intelligent weeder and reducing the rate of seedling injury.

Adaptive fuzzy echo state network optimal synchronization control of hybrid–order chaotic systems via reinforcement learning

In this paper, a novel optimal synchronization control scheme for fractional–integer hybrid–order chaotic systems is formulated. To deal with the fractional–order (FO) constraint, a transformation programme is developed and then the master system considered as an FO chaotic system is transformed into an integer–order one. A fuzzy echo state network (FESN) with the advantages of both fuzzy logic system and echo state network is introduced to approximate system uncertainty. Simultaneously, to alleviate the resource pressure, an optimal synchronization control is proposed in the light of the reinforcement learning mechanism, in which critic–actor update laws are constructed through the negative gradient of a positive function with regard to Bellman residual. Compared to typical fuzzy synchronization control, the devised FESN synchronization scheme with the same computational complexity possess superior approximation ability and synchronization performance. Ultimately, three simulation cases are exhibited to check the validity of the proposed approach.

Adaptive fuzzy tracking control for a class of time-varying output constrained nonlinear systems with non-affine nonlinear faults

This paper investigates a tracking control issue for a class of time-varying output constrained nonlinear systems subject to non-affine nonlinear faults and virtual control coefficients. In the controller design process, the nonlinear function is approximated by fuzzy logic systems. Utilizing a feasible function to convert the system, a new transformation strategy is proposed to handle the system with time-varying output constraints or without output constraints. The mean value theorem is applied to split non-affine faults, and the Nussbaum-type function is used to eliminate the effect of the unknown directional affine variable gain of input resulted from non-affine faults. Combined with adaptive fuzzy backstepping technology and the error transformation function, a new control strategy is presented, which not only deals with the time-varying output constraint but also handles non-affine faults, effectively. Finally, all closed-loop system signals are bounded, and the tracking error can converge to a small preset set. Two simulations are performed to confirm the validity of the presented strategy.