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The enhanced model reference adaptive control (EMRAC) algorithm is an effective full‐state adaptive solution to control plants affected by nonlinear unmodelled dynamics and persistent disturbances. The EMRAC strategy improves the tracking performance by equipping the MRAC algorithm with adaptive switching and adaptive integral control actions. However, the need for the plant state prevents the applicability of the EMRAC algorithm to engineering control problems where only the plant output is measurable. To cover this gap and extend the range of plants controllable by EMRAC solutions, this article presents an output‐based EMRAC algorithm leveraging the closed‐loop (CL) MRAC formulation. The robustness of the closed‐loop control system is analytically analysed, not only with respect to plant parameter uncertainties and square measurable disturbances, but also to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unmodelled terms and disturbances. The ultimate boundedness of the closed‐loop control system is assessed with respect to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unknown nonlinear terms and disturbances, by using Lyapunov theory for Filippov systems, as the adaptive switching control action makes the closed‐loop system discontinuous. To assess the effectiveness of the CL‐EMRAC strategy to impose reference trajectories despite the unmodelled plant dynamics and persistent bounded disturbances, the problem of vehicles' direct yaw moment control is used as an engineering case study. The closed‐loop tracking performance is also quantitatively evaluated through a set of key performance indicators and compared to those provided by four benchmark controllers, that is, two LQ‐based strategies and two MRAC‐based control solutions. The CL‐EMRAC and benchmark controllers are implemented and tested in a co‐simulation environment based on a high‐fidelity IPG CarMaker vehicle model. [ABSTRACT FROM AUTHOR]

This paper investigates a finite-time adaptive fuzzy prescribed performance fault-tolerant control (FTC) issue for the steer-by-wire vehicle (SBWV) systems with intermittent actuator faults. Different from the steer-by-wire (SBW) system studied by the previous literatures, the SBWV system involved in this study consists of a vehicle dynamics model and an SBW system, including unmeasurable states and unknown nonlinear dynamics. Fuzzy logic systems (FLSs) are first used to identify the unknown model dynamics, and a fuzzy state observer is constructed to estimate the unmeasured states. Then, to compensate for the influence of intermittent actuator faults, a novel finite-time output-feedback prescribed performance adaptive FTC scheme is developed by using the adaptive backstepping control methodology and co-designing the last virtual controller. The presented control scheme not only guarantees that all signals of the closed-loop system are bounded in the presence of actuator faults, but also ensures that the tracking error converges to a small neighborhood of the zero within the prescribed performance bounded. The computer simulation and comparison results demonstrate the effectiveness of the proposed fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

This article deals with the security sliding mode control (SMC) issue for T‐S fuzzy systems under random injection attacks. As a way to increase the communication efficiency of network control systems, the weighted try‐once‐discard (WTOD) protocol is utilized to decide whether the sensor nodes transmit or not. A compensating strategy is introduced in this controller side, based on which a fuzzy sliding mode controller depending on the scheduling signal is constructed. Then, the reachability of the given sliding surface together with the stochastic stability of the closed‐loop system are analyzed by incorporating the membership function, and the relaxed stability conditions are derived. Moreover, a GA‐based solving algorithm is proposed to search an optimized sliding matrix and improve the SMC performance. Finally, numerical simulation results are provided to verify the proposed SMC strategy. [ABSTRACT FROM AUTHOR]

A neural network adaptive integral terminal sliding mode control method with input saturation is proposed for the horizontal vibration problem of high‐speed elevator car systems caused by uncertainties such as guideway excitation and shaft piston wind change. First, considering the input saturation problem existing in the elevator control actuator, a class of smooth functions is introduced to approximate the nonlinearity of switching saturation, and an eight‐degree‐of‐freedom asymmetric anti‐saturation nonlinear system model of the high‐speed elevator car is established; second, in order to solve the singularity problem existing in the terminal sliding‐mode control, a nonlinear term is introduced into the sliding‐mode design, and a neural network is utilized for the fitting of the complex unknown function, and the design of the an adaptive integral terminal sliding mode controller (AITSMC), which enables the state variables of the system to achieve finite time convergence and proves the stability of the system by using Lyapunov theory; finally, under the action of two typical guide excitations, the proposed controller is compared with the passive control and adaptive control (AC), and the results show that, after adopting the proposed control method, the vibration acceleration eigenvalue is reduced by more than 60%, which effectively suppresses the horizontal vibration of the car system and verifies the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

As for a class of strict‐feedback nonlinear systems with simultaneous sensor and actuator faults, the article focuses on the problem of fixed‐time fault‐tolerant control. Both fixed‐time command filters and a compensation mechanism are utilized to cope with these issues of differential explosion and filter errors. Meanwhile, through the fusion of fixed‐time control, fuzzy logic systems, adaptive control and the backstepping technology, a n$$ n $$‐step design scheme is presented. Our proposed controller ensures the fixed‐time convergence of all the signals in the system even with sensor/actuator faults, and allows the designers to acquire the upper limit value of the fixed convergence time only by tuning the design parameters. And this scheme takes into account rapidity, adaptability and fault tolerance of the system simultaneously. Based on the numerical simulation analysis, it is shown that the better tracking performance can be achieved. So it is concluded that our strategy is effective. [ABSTRACT FROM AUTHOR]

Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations. [ABSTRACT FROM AUTHOR]

This article describes a research study on an electromechanical system with saturation, where a fuzzy hybrid controller with integral action and anti-windup is applied. The study focuses on implementing this Integral Fuzzy Logic Controller (IFLC) on a Field-Programmable Gate Array (FPGA) board. The fuzzy controllers, known for their effectiveness in handling disturbances and saturations, are used in a parallel structure. To optimize the performance of the controller, the Particle Swarm Optimization (PSO) technique is employed to tune the membership functions and feedback loop gains. The complex algebraic concepts and Type 1 fuzzy logic algorithms are transformed into mathematical equations suitable for VHSIC Hardware Description Language (VHDL). The proposed controller is co-simulated using Vivado and Xilinx® System Generator (XSG) tools on both software and hardware platforms. The utilization of fixed-point data propagation in the controller's structure ensures optimized implementation methods. The performance index of our controller surpasses that of a conventional Proportional-Integral-Derivative (PID) controller, demonstrating superior efficacy in regulating the system dynamics. To verify the efficacy of the proposed control strategy, a thorough comparison is done using control simulations between it and previous PID systems. The results show a 31% decrease in speed overshoot. [ABSTRACT FROM AUTHOR]

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good. [ABSTRACT FROM AUTHOR]

Summary: As the nonlinearities of many real physical controlled systems become stronger and stronger, they are difficult to be described by accurate mathematical models. For a class of nonlinear single‐input single‐output systems with all‐state constraints and actuator faults, an event‐triggered adaptive fuzzy ABLF function fixed‐time tracking control method is proposed. The asymmetric barrier Lyapunov function (ABLF) combined with backward step technique is used to ensure the boundedness of the closed‐loop system output. In order to solve the problem of limited bandwidth resources, this paper adopts the event trigger mechanism and fuzzy control technology to approximate the unknown function to ensure the convergence of the system in a fixed time. After theoretical analysis, it is proved that the tracking error of the system converges in the small neighborhood of the origin, and it still has good tracking performance when the actuator faults. Finally, by comparing the proposed method with the asymmetric barrier Lyapunov function, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study focuses on addressing the challenge of adaptive fuzzy tracking control for stochastic nonlinear systems while considering full-state constraints. To tackle this issue, we introduce a fuzzy logic system to approximate the unknown nonlinear terms in the system. Additionally, a barrier Lyapunov function is utilized to confine the system state within a specific range. In order to design a novel tracking controller, the backstepping design method and the fuzzy control approach are combined. To optimize system performance, a hysteresis quantizer is integrated. To evaluate the effectiveness of the proposed control strategy, two simulation examples are presented. The simulation results demonstrate that the proposed control strategy effectively limits all state variables to a predefined range. Additionally, the tracking error of the system steadily converges to a negligible range near zero. [ABSTRACT FROM AUTHOR]

This research focuses on the issue of adaptive fuzzy fault-tolerant position tracking for permanent magnet synchronous motors (PMSMs) subject to finite-time prescribed performance. An improved finite-time prescribed performance control strategy, in which unknown nonlinear functions can be tackled via fuzzy logic systems (FLSs), is presented via incorporating the approach of prescribed performance control with the technique of command filter. In addition, the command filtered method is utilized to conquer the 'explosion of complexity' emerged in the classic backstepping method and the error compensation mechanism is adopted to diminish the error generated by filtering process. Further, the impact of actuator failures is dealt with based on fault-tolerant control. It is proven that the designed controllers not only assure the semi-global boundedness of all the controlled system signals, but also make the output tracking error is preserved in a specified prescribed performance within a finite-time interval. Finally, simulation results are supplied to display the significance and potential of the proposed control technique. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties. • We propose an adaptive probabilistic recurrent TSK fuzzy neural PID controller. • The Lyapunov stability criterion is used to update the proposed controller parameters. • The proposed controller is applied practically for controlling a servo motor. [ABSTRACT FROM AUTHOR]

The traditional Nussbaum‐type functions encounter challenges when extended to systems with multiple unknown control directions, as the coupling between the Nussbaum‐type function and Nussbaum‐type gain may lead to mutual elimination. To address this issue, a novel Nussbaum‐type function is introduced to handle multiple unknown control directions for stochastic systems. The stability of the proposed Nussbaum‐based design can be guaranteed using the Lyapunov analysis. To conserve computational resources, a static composite event‐triggered mechanism is integrated with intermittent feedback in the Nussbaum‐based design. In contrast to conventional event‐triggering mechanisms that depend solely on control signals, the presented design incorporates tracking errors into the event‐triggering conditions. Moreover, to further alleviate computational burdens, we further develop a dynamic composite event‐triggered mechanism. With the overall design scheme, bounded tracking errors and reduced computational burden can be ensured. Simulation examples validate the efficacy of the proposed adaptive Nussbaum composite event‐based robust control design. [ABSTRACT FROM AUTHOR]

The work is devoted to the helicopter turboshaft engines' gas generator rotor R.P.M. neuro-fuzzy controller development, which improves control accuracy and increases the system's stability to external disturbances and adaptability to changing operating conditions. Methods have been developed, including improvements to the automatic control system structural diagram which made it possible to obtain the system transfer function in the bandpass filter transfer function form. The work also improved the fuzzy rules base and the neuron activation function mathematical model, which significantly accelerated the neuro-fuzzy controller training process. The transfer function frequency and time characteristics analysis showed that the system effectively controlled the engine and reduced vibration. Methods for ensuring a guaranteed stability margin and the synthesis of an adaptive filter were studied, which made it possible to achieve the system's high stability and reliability. The results showed that the developed controller provided high stability with amplitude and phase margins, effectively compensating for changes in external conditions. Experimental studies have demonstrated that the control quality improved by 2.31–2.42 times compared to previous neuro-fuzzy controllers and by 5.13–5.65 times compared to classic PID controllers. Control errors were reduced by 1.84–2.0 times and 5.28–5.97 times, respectively, confirming the developed neuro-fuzzy controller's high efficiency and adaptability. [ABSTRACT FROM AUTHOR]

This article concentrates on the problem of adaptive event-triggered control with double-side event-triggering mechanisms for a family of switched nonlinear systems with measurement sensitivity. In order to circumvent the negative effect of intermittent transmission and sensor measurement sensitivity on the output, an extended system is constructed by introducing a first-order output filter. Then, an input-driven neural switched observer is designed to estimate unmeasurable system states. To deal with the mutual interaction between the triggering and the switching mechanisms and their negative effect on the positive lower bound of inter-execution intervals, an incremental compensation is incorporated in the event-triggering mechanism (ETM) at the output sensor side. At last, a novel event-triggered control scheme built on both sides of the controller and output is proposed, which exhibits superior efficacy compared to the one-side event-triggered control scheme. By using the methods of the average dwell time (ADT) and the backstepping design, it is guaranteed that all signals of the switched closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon is excluded. The efficacy of the proposed methodology is validated by using a numerical simulation and a one-link robot system. [ABSTRACT FROM AUTHOR]

Summary This paper investigates the adaptive fuzzy backstepping control problem for uncertain discrete‐time nonlinear systems with mismatched disturbances. A novel adaptive fuzzy command filtered backstepping approach is proposed, which can overcome the noncausal problem by using the command filter. The filter errors generated by filter are removed by constructing error compensation mechanism. The fuzzy logic systems are used to approximate the uncertain nonlinear dynamics, and the unknown mismatched disturbances are actively attenuated by using adaptive control technique. Based on the proposed new weighed Lyapunov functions, all the signals in the close‐loop system are proved to be uniformly ultimately bounded. The viability of proposed method is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach that simultaneously enables photovoltaic (PV) inversion and flexible arc suppression during single-phase grounding faults. Inverters compensate for ground currents through an arc-elimination function, while outputting a PV direct current (DC) power supply. This method effectively reduces the residual grounding current. To reduce the dependence of the arc-suppression performance on accurate compensation current-injection models, an adaptive fuzzy neural network imitating a sliding mode controller was designed. An online adaptive adjustment law for network parameters was developed, based on the Lyapunov stability theorem, to improve the robustness of the inverter to fault and connection locations. Furthermore, a new arc-suppression control exit strategy is proposed to allow a zerosequence voltage amplitude to quickly and smoothly track a target value by controlling the nonlinear decrease in current and reducing the regulation time. Simulation results showed that the proposed method can effectively achieve fast arc suppression and reduce the fault impact current in single-phase grounding faults. Compared to other methods, the proposed method can generate a lower residual grounding current and maintain good arc-suppression performance under different transition resistances and fault locations. [ABSTRACT FROM AUTHOR]

This paper presents the decentralized sampled‐data fuzzy observer design technique for nonlinear interconnected systems based on the Takagi–Sugeno fuzzy model. The interconnected system is considered that the subsystems include uncertain interconnections and are not needed to satisfy stable conditions. Based on the continuous‐time Lyapunov functional approach, the observer design technique is proposed to minimize the interconnection bound to attenuation degree ratio and formulated into the linear matrix inequality formats. Finally, by simulation example, it shows the validity and superiority of the proposed observer design technique. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an agent rescue scheduling approach grounded in proximal policy optimization, coupled with a singularity-free predefined-time control strategy. The primary objective of this methodology is to bolster the efficiency and precision of rescue missions. Firstly, we have designed an evaluation function closely related to the average flying distance of agents, which provides a quantitative benchmark for assessing different scheduling schemes and assists in optimizing the allocation of rescue resources. Secondly, we have developed a scheduling strategy optimization method using the Proximal Policy Optimization (PPO) algorithm. This method can automatically learn and adjust scheduling strategies to adapt to complex rescue environments and varying task demands. The evaluation function provides crucial feedback signals for the PPO algorithm, ensuring that the algorithm can precisely adjust the scheduling strategies to achieve optimal results. Thirdly, aiming to attain stability and precision in agent navigation to designated positions, we formulate a singularity-free predefined-time fuzzy adaptive tracking control strategy. This approach dynamically modulates control parameters in reaction to external disturbances and uncertainties, thus ensuring the precise arrival of agents at their destinations within the predefined time. Finally, to substantiate the validity of our proposed approach, we crafted a simulation environment in Python 3.7, engaging in a comparative analysis between the PPO and the other optimization method, Deep Q-network (DQN), utilizing the variation in reward values as the benchmark for evaluation. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained. [ABSTRACT FROM AUTHOR]

For a class of full‐states constraints nonlinear system with actuator failure, an adaptive fuzzy disturbance observer tracking design is carried out by using Lyapunov function and the dynamic surface method. The integral explosion problem is avoided by using the dynamic surface design method. Because the disturbance of the system is unknown, a nonlinear fuzzy disturbance observer is designed to estimate the unknown disturbance. And the unknown nonlinear function is approximated fuzzy logic systems. Based on the Lyapunov function method and backstepping approach, the stability of considered systems are analysed. And the proposed method can guarantee that all the signals in the closed‐loop system are bounded and the system output can track the desired signal to the small neighbourhood range. In addition, the fuzzy disturbance observer can estimate the unknown disturbance state well. Finally, several sets of simulation examples are given and compared with other method, and the experimental results are quantitatively analysed. Then, the robustness and superiority of the proposed control scheme are verified and demonstrated. [ABSTRACT FROM AUTHOR]

In order to enhance the support capability of photovoltaic inverters for new energy microgrid systems, grid-forming control technology has attracted widespread attention, with Virtual Synchronous Generator (VSG) emerging as a research frontier. This paper integrates hybrid energy storage systems with photovoltaic generation to provide stable voltage support and power compensation for the system. In addition, leveraging the variability of the virtual parameters J and D in traditional VSGs, an adaptive grid-forming photovoltaic inverter control strategy based on fuzzy algorithm is proposed. Furthermore, to reduce the frequency deviation caused by load transients during islanding operation, an adaptive droop coefficient based on frequency power limits is introduced. Finally, simulations are conducted under grid-connected and islanded conditions to compare three control methods: fixed parameter control, traditional adaptive control, and the proposed adaptive with fuzzy control. The results demonstrate that integrating adaptive droop coefficients with fuzzy control can effectively improve the frequency stability and dynamic response capability of microgrids. [ABSTRACT FROM AUTHOR]

This paper is devoted to adaptive trajectory tracking of quadrotor systems with full-state constraints and random disturbances. The command-filtering technology combined with error compensation mechanisms is proposed to deal with the "explosion of complexity" problem in traditional backstepping design and reduction of the filtering errors. The Barrier Lyapunov functions (BLFs) are constructed to ensure that all system states do not violate the boundary of its constraints. The command-filtered adaptive fuzzy controller is designed such that the state constraints are not breached almost surely, all signals in the closed-loop system are bounded almost surely, and the first-order moment of the tracking error converges to an arbitrarily small neighborhood of zero by parameters tuning. Finally, the effectiveness of the proposed control method is illustrated by simulation results. [ABSTRACT FROM AUTHOR]

This paper studies the distributed output feedback consensus control for a class of uncertain nonlinear multi-agent systems (MASs). The dynamics of subsystems are heterogeneous and contain unknown control gains. A novel distributed fuzzy consensus control protocol is constructed by employing variable separation technology and adaptive backstepping technique. The main advantage of the protocol is that it is distributed only depending on the relative output information. Moreover, it does not require the observer information exchange, which can avoid network attacks. A numerical example is given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The problem of adaptive event‐triggered security tracking controller design is studied for interval type‐2 (IT2) Takagi‐Sugeno (T‐S) fuzzy‐approximation‐based nonlinear networked systems with deception attacks, privacy protection, and dynamic quantization in this paper. To reduce the network burden, the adaptive event‐triggered schemes (AETSs) and dynamic quantization are introduced to reduce unnecessary transmission and the number of network packets, respectively. Under the framework of network security, a novel security co‐design scheme with the tracking controller, AETSs, and the quantizers is developed, in which the privacy protection of the reference model and the deception attacks of the controller are taken into consideration simultaneously. Sufficient conditions for the results system to be mean‐square asymptotically stable with a H∞$$ {H}_{\infty } $$ tracking performance are established. Finally, two practical simulation examples demonstrate the effectiveness and novelty of the proposed design strategy. [ABSTRACT FROM AUTHOR]

This article examines a conical tank nonlinear system’s level control. A crucial component of facilities like milk processing plants and sugarcane mills is the conical tank. The fluid drains entirely due to the conical shape of the tank. The conical form of the system causes nonlinear dynamics. The conical tank nonlinear system transfer mechanism is not easily accessible, and different operating heights require different transfer mechanisms, making controller design difficult. Developing the Fractional-Order Integral Terminal Sliding-Mode (FO-ITSM) Controller system for Advanced Golden Jackal Optimisation (GJO) based conical tank systems is the main objective of this paper. This is utilised to deliver real-time liquid level management in non-linear conical systems. The dynamics of these models make it possible to identify the conical tank system that produces control signals from liquid samples taken at reference levels with greater accuracy. This system, which is employed on multiple platforms, maintains and controls the liquid level while meeting all design specifications, including time constant, no overshoot, less settling, and rise time. For research simulation work, MATLAB software’s FOMCON toolbox is utilised. [ABSTRACT FROM AUTHOR]

This paper presents a new method for regional stabilization of discrete-time nonlinear systems by static output feedback based on polynomial Lyapunov functions. The considered class of nonlinear systems subject to time-varying parameters can be described by difference-algebraic representation and an equivalent polytopic model is obtained, making it possible to apply Lyapunov theory and linear matrix inequality-based tool. In this sense, a convex optimization problem in terms of LMI is provided to guarantee the closed-loop system's robust stabilization and to enlarge the estimated domain of attraction (DoA). The proposed control design is a one-step approach that can be applied to systems with nonlinear output matrices. It requires no iterative algorithms or congruence transformation, and auxiliary decision variables are introduced only aiming at less conservative results. Besides that, the use of polynomial Lyapunov functions allows us to obtain asymmetric and non-convex estimated DoA, reducing the conservativeness. Numerical examples are provided to illustrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison. [ABSTRACT FROM AUTHOR]

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Different agronomic requirements, production conditions, and crop species result in varying row spacings. To address the issue of seedling damage caused by pressure when a fixed track width sprayer operates in different row spacings and enhance the accuracy of track width adjustment, this study designed a track width adjustment system for a sprayer based on the agronomic requirements for field management during the early and mid-stages of corn growth and the entire growth period of wheat in Henan Province, China. The designed track width adjustment system for the sprayer comprised transmission mechanisms, telescopic track width adjustment mechanisms, and an electro-hydraulic control system. The control system achieved a precise track width adjustment by controlling the movement of the hydraulic cylinders through electrical signals, forming a closed-loop adjustment system with the aid of sensors. Four control schemes are proposed: classical PID, integral separated PID, fuzzy adaptive PID, and integral separated fuzzy PID. Simulation experiments were conducted using MATLAB to compare these schemes. The results indicated that the integral separated fuzzy PID exhibited the fastest response and highest steady-state accuracy. The performance of the track width adjustment system was validated through field experiments. The results demonstrate that the stability coefficient of variation for the track width adjustment was 3.04%, which is below the 10% threshold required by agricultural machinery standards. Additionally, the average error of the track width adjustment was 13.42 mm, indicating high precision and effectively reducing seedling compression damage during plant protection operations. [ABSTRACT FROM AUTHOR]

The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match. [ABSTRACT FROM AUTHOR]

In several applications, the accuracy and robust performance of the control method for the speed of permanent magnet synchronous motors (PMSMs) is critical. Model uncertainties, caused by inaccurate model identification, decrease the accuracy of PMSM control. To solve this problem, this paper presents a super robust control structure for the speed control of PMSMs. In the proposed method, the model uncertainties with Lipschitz condition together with disturbances are considered during the PMSM modeling, and their effects are handled using a robust state feedback control. To be more specific, the Lyapunov stability proof is performed in such a way that the model uncertainty effects are eliminated. Before that, the Lyapunov stability criteria have been selected in such a way that the Hꝏ conditions are considered and guaranteed. This issue helps to eliminate the effects of the disturbances. In addition, this paper considers another option to make the whole control structure robust against sudden load changes. To solve this problem, a fuzzy adaptive sliding mode observer (FASMO) is presented to determine the load torque and use it in the control signal generation. In this observer, the switched gain of the sliding mode observer (SMO) is adapted using a fuzzy system to eliminate the chattering phenomena and increase the estimation accuracy. In fact, the proposed method is called super robust because it resists model uncertainties, disturbances, and sudden load changes during three stages by robust state feedback control, Hꝏ criterion, and load estimator, respectively. The performance of the proposed approach is validated through a set of laboratory tests, and its superiority is shown compared to other methods. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

This article addresses the global regulation by periodic event‐triggered output feedback control for a class of feedforward nonlinear systems. Particularly, the considered systems suffer from parameter uncertainties and unknown measurement sensitivity, which entail integrating advanced compensation strategies within the event‐triggered framework. To this end, a time‐varying gain is devised to tackle uncertain nonlinearities as well as the sampling error. Subsequently, a novel periodic event‐triggered output feedback controller is constructed based on the delicate choice of design parameters. Notably, the proposed control scheme is able to avoid the continuous monitoring of the system behavior, and guarantee that all closed‐loop system states are globally bounded and ultimately converge to zero. Finally, a practical example is given to illustrate the main results. [ABSTRACT FROM AUTHOR]

This article addresses the problem of adaptive fault‐tolerant event‐triggered control (ETC) for time‐varying delay switched nonlinear systems (SNSs) with actuator and sensor failures. First, in order to address the unknown actuator failures, a fault compensation coefficient is introduced. Then, a mode‐dependent state observer is established to estimate the unknown states. The unknown nonlinear function is approximated by the usage of fuzzy logic systems (FLS). In addition, the Lyapunov–Krasovskii functions are utilized to discuss the influence of time‐varying delays. Then, an adaptive fuzzy fault‐tolerant ETC and a novel event‐triggering mechanism (ETM) are derived. It is proved that the proposed fault‐tolerant controller can ensure that all states of the closed‐loop system (CLS) are semi‐globally uniformly ultimately bounded (SGUUB) by average dwell time (ADT) constraints and the Zeno phenomenon can be excluded. Results of two simulation examples verify that the proposed control method is effective. [ABSTRACT FROM AUTHOR]

In this article, predictive cloud control is investigated for nonlinear networked multiagent system (NMAS) with quantizers via extended state observer (ESO). Considering the network‐induced delays in NMAS, the cloud control scheme is designed. Then, arbitrary region quantizers are used to decrease the quantity of data packets. Moreover, the ESO that can estimate the state and uncertainty of nonlinear NMAS is used to obtain the prediction of data packets. Input‐to‐state stability (ISS) can guarantee the validity of ESO. Sufficient conditions that can achieve stability and consistency for NMAS are given by the zoom strategy. The significance of the scheme is verified by simulation results. [ABSTRACT FROM AUTHOR]

Quantized signal control has been widely recognized as an effective means for conserving communication resources. However, ensuring stability of quantization‐driven nonlinear systems becomes particularly challenging in the presence of intermittent denial‐of‐service (DoS) attacks and sensor failures. This article explores the stable control problem of quantized nonlinear systems subject to DoS attacks. When an attack exists, all signals are unmeasurable. During the dormant phase of a DoS attack, only the quantized output signal is measurable, while there is a deviation between the received signal and the ideal signal due to sensor failure. To address these challenges, a switch‐type quantized observer is designed in this article to estimate unmeasurable state signals. In addition, we integrate the first‐order dynamic filter into the back‐stepping design process, which not only avoid the obstacles arisen from non‐differentiable output signals, but also simplifies the design process. Furthermore, this article adopts a more flexible sector quantizer and derives a set of adaptive laws to compensate for unknown quantization parameters. It is shown that, by utilizing the estimated signal, the proposed quantized adaptive control is able to effectively fight against DoS attacks and also is fault‐tolerant to sensor failures, guaranteeing semi‐globally uniformly ultimately bounded for all closed‐loop signals. Finally, simulation results validate the effectiveness of the proposed theory. [ABSTRACT FROM AUTHOR]

Power quality is a crucial determinant for integrating wind energy into the electrical grid. This integration necessitates compliance with certain standards and levels. This study presents cascadedfuzzy power control (CFPC) for a variable-speed multi-rotor wind turbine (MRWT) system. Fuzzy logic is a type of smart control system already recognized for its robustness, making it highly suited and reliable for generating electrical energy from the wind. Therefore, the CFPC technique is proposed in this work to control the doubly-fed induction generator (DFIG)-based MRWT system. This proposed strategy is applied to the rotor side converter of a DFIG to improve the current/power quality. The proposed control has the advantage of being model-independent, as it relies on empirical knowledge rather than the specific characteristics of the DFIG or turbine. Moreover, the proposed control system is characterized by its simplicity, high performance, robustness, and ease of application. The implementation of CFPC management for 1.5 MW DFIG-MRWT was carried out in MATLAB environment considering a variable wind speed. The obtained results were compared with the direct power control (DPC) technique based on proportional-integral (PI) controllers (DPC-PI), highlighting that the CFPC technique reduced total harmonic distortion by high ratios in the three tests performed (25%, 30.18%, and 47.22%). The proposed CFPC technique reduced the response time of reactive power in all tests by ratios estimated at 83.76%, 65.02%, and 91.42% compared to the DPC-PI strategy. Also, the active power ripples were reduced by satisfactory proportions (37.50%, 32.20%, and 38.46%) compared to the DPC-PI strategy. The steady-state error value of reactive power in the tests was low when using the CFPC technique by 86.60%, 57.33%, and 72.26%, which indicates the effectiveness and efficiency of the proposed CFPC technique in improving the characteristics of the system. Thus this control can be relied upon in the future. [ABSTRACT FROM AUTHOR]

This paper studies the problem of adaptive fuzzy control based on command filtering for a class of nonlinear systems characterized by an input dead zone, input saturation, and unknown control direction. First, this paper proposes a novel equivalent transformation technique that simplifies the design complexity of multiple input constraints by converting the input dead zone and saturation nonlinearities into a unified functional form. Subsequently, a fuzzy logic system is utilized to handle the unknown nonlinear functions, and the command-filtering method is employed to address the issue of complexity explosion, while the Nussbaum function is utilized to resolve the challenge of an unknown control direction. Based on Lyapunov stability, it is proven that the tracking error converges to a small neighborhood around the origin, and all closed-loop signals are bounded. Finally, a numerical simulation result and an actual simulation result of a pendulum are presented to verify the feasibility and effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The predefined performance tracking control issue of a vertical take-off and landing (VTOL) aircraft system is discussed in this work. Employing the presented control performance function, the constrained error signal of the aircraft is switched into an equivalent unconstrained signal. Moreover, fuzzy logic systems are introduced to address unknown nonlinear dynamics problems in aircraft system. Furthermore, in order to address the approximation errors and the external disturbances of aircraft system, parameter-adaptive update laws are proposed in the analysis process. Subsequently, adaptive fuzzy sliding-mode control laws are designed by combining fuzzy control method with sliding-mode control technique. By applying the redesigned sliding-mode functions and the Lyapunov stability method, the output of the VTOL aircraft system can track the desired trajectory, and the tracking errors are constrained in a predefined range. Finally, the availability of the proposed control laws is testified through simulation examples. [ABSTRACT FROM AUTHOR]

This paper proposes a fixed-time fuzzy adaptive output feedback control scheme for the robotic arm model (RAM) of steel structures. Firstly, the process of transforming RAM into a nonlinear system is elaborated. Secondly, a fuzzy observer is designed to approximate the nonlinear function and estimate the observed state of the system. Subsequently, a fixed-time adaptive controller is constructed, ensuring the system's stability within a fixed time, with the convergence time unaffected by the initial state. Finally, the effectiveness of the strategy is verified through simulation examples. [ABSTRACT FROM AUTHOR]

As a typical nonlinear control method, backstepping control can decouple the mathematical model of permanent magnet synchronous motors. In addition, permanent magnet synchronous motor control systems based on the backstepping method can enhance the control performance of the control system to a certain extent. Furthermore, the design step is easy and simple to implement in engineering practice. However, the long-term wear and tear, aging, high temperature caused by changes in the basic parameters of motor and external load sudden changes as well as other factors will bring interference to the control system, leading to reduced-control accuracy and control performance degradation. To solve this problem, this paper suggests a control strategy combining backstepping control and fuzzy control based on backstepping control. It sets the load adaptive law and utilizes fuzzy control to make online real-time adjustments to the control parameters in the backstepping control. This is done to improve the immunity of interference and stability of the control system in response to the changes in the parameters of the body of the motor and sudden changes of the load. The effectiveness and feasibility of this system is verified by MATLAB simulation and experimental results, which provides a feasible solution for permanent magnet synchronous motor immunity and high-precision control occasions. [ABSTRACT FROM AUTHOR]

Categorical features appear in datasets from almost every practice area, including real estate datasets. One of the most critical handicaps of machine learning algorithms is that they are not designed to capture the qualitative nature of the categorical features, leading to sub-optimal predictions for the datasets with categorical observations. This study focuses on a new fuzzy regression functions framework, namely hierarchical fuzzy regression functions, that can handle categorical features properly for the regression task. The proposed framework is benchmarked with linear regression, support vector machines, deep neural networks, and adaptive neuro-fuzzy inference systems with real estate data having categorical features from six markets. It is observed that the proposed method produces better prediction performance for real estate price prediction than the benchmark methods in a wide variety of real estate markets. Since we provide all the required software codes to implement the proposed hierarchical fuzzy regression functions framework, our approach offers practitioners a readily applicable, high-performing tool for real estate price prediction and other regression problems involving categorical independent features. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the fuzzy finite‐time command filtering tracking control problem for switched nonlinear systems with input nonlinearities via using backstepping technique. An equivalent transformation method is proposed for the purpose of handling the impediment problem caused by the nonlinearity of control input. Finite‐time command filtering control technology can solve the "explosion of complexity" issue, which is caused by the multiple derivation of virtual controllers appeared in the classic backstepping control. A filtering compensation scheme is developed to reduce the filtering error. By combining the fuzzy logic system's approximation ability and the finite‐time theory, a new fast convergence adaptive control scheme is proposed so that the output of the system can converge to a small area near the desired signal and the boundedness of all signals of the controlled system can be assured in finite time. Finally, a simulation example successfully verifies the feasibility of the developed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible sensor made of fiber Bragg gratings (FBGs) package that can effectively recognize the direction and velocity of relative sliding on its surface. The measurement principle and structural design of the sensor are introduced, and the sensor is analyzed by finite element simulation software to verify that the sensor can detect the shear deformation of relative sliding on its surface. On this basis, an experimental platform is built to test the relative sliding direction and velocity, the feature values are extracted after the initial signal is processed with noise reduction, and the recognition prediction is carried out by using back-propagation neural network (BPNN) and GA-BP neural network optimized by Genetic Algorithm (GA). The experimental results show that GA-BP neural network has better recognition accuracy, and the correlation coefficients R2 in the recognition of the sliding direction and velocity parameters are 0.9851 and 0.9819, respectively, which are able to recognize the sliding sensation parameters effectively. [ABSTRACT FROM AUTHOR]

In this article, a fuzzy adaptive finite time (FT) output feedback tracking fault-tolerant control (FTC) scheme is designed for stochastic non-strict feedback nonlinear systems (NSFNSs) with actuator failure, input saturation, and unknown control direction. The system studied in this article is more complex than previous results and more in line with the actual situation. The advantages of this study are as follows: (i) A fuzzy state observer of high gain is established to deal with the states unmeasured problem of the system. (ii) The Nussbaum function is employed to address unknown control direction and actuator failure problems, and the Smooth non-affine function is utilized to dispose of the input saturation. (iii) The dynamic surface technique introduces errors that are eliminated by using the error compensation technology, and fuzzy logic systems (FLSs) are applied to appropriate unknown nonlinear functions in the system. The controller developed in this study guarantees that tracking errors can converge to a small neighborhood around the origin within FT and overall signals in the stochastic system are semi-global finite-time stable in probability (SGFSP). Finally, three simulation examples are introduced to verify the effectiveness of the suggested control strategy. [ABSTRACT FROM AUTHOR]

This work researches the issue of adaptive fault-tolerant fuzzy tracking control for a class of nonlinear systems in strict-feedback form with quantized inputs. The fuzzy logic systems are utilized to approximate unknown functions, and a fuzzy state observer is built to estimate the unavailable states. Meanwhile, an improved hysteresis quantizer is introduced to achieve the quantized inputs for saving communication resources. To improve the approximation capacities of fuzzy logic systems, the compensated tracking errors and the prediction errors are used to construct the adaptive laws parameters. Furthermore, a composite adaptive fault-tolerant fuzzy control strategy is developed, which can guarantee proper operations of the systems when encountering actuator faults, and overcome the issue of "explosion of complexity" in the backstepping approach. It is strictly demonstrated that the system output can follow a desired signal within a small error zone and all signals of the closed-loop system are bounded. Finally, the simulation results are given to confirm the validity of the presented control strategy. [ABSTRACT FROM AUTHOR]

In order to solve the problem of optimal prescribed performance control for unknown dynamic nonlinear systems, an adaptive dynamic programming method based on dynamic event-triggered control strategy is designed. By using Lyapunov stability theory, it is proved that all signals in nonlinear systems are uniformly and ultimately bounded. First, the system under consideration is transformed into an unconstrained system with the prescribed performance by the variable transformation method. Then, the integral reinforcement learning method is used to solve the optimal control problem when the system drift dynamic is unknown. In addition, a dynamic event-triggered control strategy is constructed, which can update the weight estimation and control strategy irregularly, so as to alleviate the problem of excessive data transmission burden when the designed critic neural network approximates the value function. At the same time, Zeno's behavior in the communication process is avoided. Finally, a numerical example is given to verify the validity of the proposed theory. [ABSTRACT FROM AUTHOR]

This paper studies the mixed H 2 / H ∞ control for Takagi–Sugeno (T–S) fuzzy Markovian jump systems (MJSs) subject to random delays and multiple uncertain transition probabilities. In contrast to existing research, this study presents uncertainty parameters, external disturbance, random delays, and uncertain transition probabilities simultaneously in a unified T–S fuzzy model. Specifically, this study examines multiple Markov chains with partially unknown transition probabilities. These complex imperfections have a substantial adverse impact on system performance and the associated challenge of mixed H 2 / H ∞ control remains unresolved. Our innovative contributions are described as follows. The proposed approach utilizes free-weighting matrix technique and Lyapunov–Krasovskii functional to get the H 2 / H ∞ controller, which ensures that the stochastic T–S fuzzy systems exhibit stochastic stability and comply with the H ∞ performance index. [ABSTRACT FROM AUTHOR]

This paper aims to investigate an adaptive event-triggered control scheme for unreliable networked control systems with data loss, transmission delay, and time-varying delay described by Takagi–Sugeno (T–S) fuzzy model. In order to achieve higher computational efficiency, better robustness, and higher control accuracy, an adaptive event-triggered controller based a sampling-state error mechanism is proposed. The proposal of this scheme extends the adaptive range of the event-triggered controller. On the basis of linear matrix inequality (LMIs), a new stability criterion is attained under the Lyapunov–Krasovskii functional (LKF) theory. Compared with the previous results, the scheme is less conservative. By using a numerical system and a continuous stirred tank reactor (CSTR) system, the quantitative experimental results indicate that the number of sampling in the controller is reduced, thus verifying the effectiveness and superiority of this control method. [ABSTRACT FROM AUTHOR]