Adaptive Fuzzy Control Method Research Articles

An adaptive neural fuzzy virtual inertia control method for VSC-HVDC system

Compared to conventional DC transmission, Voltage source converter based high voltage direct current transmission (VSC-HVDC) has the advantages of independently controllable transmission power, no commutation failure, no reactive power compensation and low harmonic levels. However, VSC-HVDC transmission cannot provide effective frequency support and regulation for the system after grid connection. For this reason, an adaptive neural fuzzy virtual inertia control method is proposed to impose a control strategy on the converter at the receiving end of a VSC-HVDC transmission. The method uses the frequency change rate and the amount of change of frequency as constraints to dynamically adjust the virtual inertia and damping coefficients. It provides larger inertial support when the frequency fluctuation is large and smaller inertial support when the frequency fluctuation is small, thus adapting to different operating conditions. Finally, a hardware-in-the-loop simulation platform is built. The superiority and application prospect of the proposed strategy in frequency stability of the system are verified by comparing the proposed strategy with the traditional virtual control strategy.

Nonlinear Adaptive Fuzzy Control Design for Wheeled Mobile Robots with Using the Skew Symmetrical Property

This research presents a nonlinear adaptive fuzzy control method as an analytical design and a simple control structure for the trajectory tracking problem in wheeled mobile robots with skew symmetrical property. For this trajectory tracking problem in wheeled mobile robots, it is not easy to find an analytical adaptive fuzzy control solution due to the complicated error dynamics between the controlled wheeled mobile robots and desired trajectories. For deriving the analytical adaptive fuzzy control law of this trajectory tracking problem, a filter link is firstly adopted to find the solvable error dynamics, then the research is based on the skew symmetrical property of the transformed error dynamics. This proposed nonlinear adaptive fuzzy control solution has the advantages of low computational resource consumption and elimination of modeling uncertainties. From the results for tracking two simulation scenarios (an S type trajectory and a square trajectory), the proposed nonlinear adaptive fuzzy control method demonstrates a satisfactory trajectory tracking performance for the trajectory tracking problem in wheeled mobile robots with huge modeling uncertainties and outperforms the existing H2 control method.

Sliding Mode Based Load Frequency Control and Power Smoothing of Power Systems with Wind and BESS Penetration

This study aims to maintain the frequency stability of the power system penetrated by wind power. Hence, a battery energy storage system (BESS) is applied to smooth the wind power output in power systems and to enhance their load frequency control (LFC) capacity. A novel comprehensive control framework is proposed for power systems integrated with wind farms and BESS based on an adaptive fuzzy super-twisting sliding mode control (AF-SSMC) method. Firstly, the sliding functions and control laws of subsystems are designed according to different relative degrees. Then, the super-twisting algorithm is applied to suppress the chattering of the sliding mode control law. Furthermore, an adaptive fuzzy control method is used to adjust the control gains online for better control performance of the controllers. The Lyapunov stability theory is employed to prove the asymptotic stability of the subsystems. The model of an interconnected thermal power system with wind and BESS penetration is also constructed for simulation analyses. The results indicate that the AF-SSMC method effectively reduces the chattering, and the proposed framework stabilizes the frequency of the power system under system uncertainties and external disturbances. Moreover, the wind farm and BESS combined system accurately tracks a reference power to reduce wind power fluctuations.

Prescribed Performance Adaptive Fuzzy Control for Affine Nonlinear Systems With State Constraints

This article considers the problem of prescribed performance control for a class of affine nonlinear systems with state constraints. Fuzzy logic systems (FLSs) are employed to cope with unknown nonlinear functions. In addition, an additive transformation and one-to-one mapping method are introduced to handle the control problem of nonlinear systems with full-state constraints. Then, based on adaptive backstepping control, we develop a new prescribed performance adaptive fuzzy control method, which not only makes the tracking error be constrained within the prescribed range but also ensures the other state variables do not violate the predefined constraints. The simulation example verify the feasibility of the control method.

Fuzzy Adaptive Finite-Time Consensus Control for High-Order Nonlinear Multiagent Systems Based on Event-Triggered

This article studies the fuzzy adaptive finite-time consensus control problem for high-order nonlinear multiagent systems with unknown nonlinear dynamics. In control design,fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and under the frameworks of adaptive backstepping recursive design and finite-time stability theory, an adaptive fuzzy finite-time consensus control method is developed. To save communication resources and reduce the numbers of controller execution times, a dynamic event-triggered mechanism with a relative threshold is established. Subsequently, an event-triggered-based finite-time fuzzy adaptive control scheme is formulated. Furthermore, by constructing novel integral-type Lyapunov functions and adding a power integrator technique, the finite-time stability of the closed-loop system and the convergence of consensus tracking errors are proved. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed adaptive event-triggered consensus control method.

Fuzzy Adaptive Protective Control for High-Speed Trains: An Outstretched Error Feedback Approach

This paper presents a fuzzy adaptive protective control method for autonomous high-speed trains (HSTs) automatic operation using a new outstretched error feedback design approach. In order to stabilizing the error dynamics with respect to target position and speed profiles of controlled HSTs, nonlinear transformation in prescribed performance control methodology is used to convert running states subject to protective (constrained) information, imposed by automatic train protection (ATP) subsystem, to new coordinates in unconstrained form. By blending a new outstretched error feedback and fuzzy approximation, it is guaranteed above-mentioned errors are kept within regions characterized by error boundary (or prescribed performance) functions and control parameters simultaneously, which can be adjusted to arbitrarily small even without error decreasing boundary functions. Fuzzy approximation is used in compensating unknown running resistances. It is rigorously proved that the resulting closed-loop system is stable in sense of Lyapunov stability in the presence of unknown resistance, containing basis and aerodynamic resistances with uncertain parameters and piecewise continuously slope resistance over varying gradient profile. The proposed control is demonstrated to be effective by comparative simulations of train G1 running on Beijing-Shanghai railway line.

Impact angle constrained fuzzy adaptive fault tolerant IGC method for Ski-to-Turn missiles with unsteady aerodynamics and multiple disturbances

An impact angle constrained fuzzy adaptive fault tolerant integrated guidance and control method for Ski-to-Turn (STT) missiles subject to unsteady aerodynamics and multiple disturbances is proposed. Unsteady aerodynamics appears when flight vehicles are in a transonic state or confronted with unstable airflow. Meanwhile, actuator failures and multisource model uncertainties are introduced. However, the boundaries of these multisource uncertainties are assumed unknown. The target is assumed to execute high maneuver movement which is unknown to the missile. Furthermore, impact angle constraint puts forward higher requirements for the interception accuracy of the integrated guidance and control (IGC) method. The impact angle constraint and the precise interception are established as the object of the IGC method. Then, the boundaries of the lumped disturbances are estimated, and several fuzzy logic systems are introduced to compensate the unknown nonlinearities and uncertainties. Next, a series of adaptive laws are developed so that the undesirable effects arising from unsteady aerodynamics, actuator failures and unknown uncertainties could be suppressed. Consequently, an impact angle constrained fuzzy adaptive fault tolerant IGC method with three loops is constructed and a perfect hit-to-kill interception with specified impact angle can be implemented. Eventually, the numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

Command-Filter-Based Adaptive Fuzzy Finite-Time Output Feedback Control for State-Constrained Nonlinear Systems With Input Saturation

In this article, an adaptive fuzzy finite-time tracking control method is investigated for a class of state-constrained nontriangular nonlinear systems with input saturation. First, the fuzzy logic system is introduced to process the unknown functions, and the immeasurable system states are estimated accurately via the fuzzy state observer. To handle the saturation nonlinearity, the function approximation method is applied for the system transformation. Second, the “explosion of complexity” problem inherent in backstepping is solved through the command filter. Meanwhile, the barrier Lyapunov function is designed to guarantee that the constraints of states are not violated. Third, an adaptive fuzzy finite-time output feedback controller is developed to render the closed-loop system semiglobally practically finite-time stable. Eventually, simulation examples are included to illustrate the effectiveness of the theoretical results.

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Distributed adaptive fuzzy control for multi-agent systems with full state constraints and unmeasured states

This article proposes a fuzzy adaptive output feedback control method for multi-agent systems with uncertainties and nonlinearities, considering both full state constraints and unmeasurable states. Aiming at the problem that the state of followers in nonlinear multi-agent systems is unmeasured, an observer is constructed by applying the characteristics of fuzzy logic systems to achieve the output feedback control scheme. The state constraint of multi-agent systems is also a great challenge. Based on a backstepping technique and integral barrier Lyapunov functions (iBLFs), all states are constrained, and an adaptive controller is designed. Compared with imposing constraints on the error and then constraining the state, the method in this article compensates for defects in the constraint conditions. Furthermore, under the Lyapunov stability theorem, we can prove that all signals of closed-loop systems are cooperative semi-global uniformly ultimately bounded (CSUUB). A simulation validates the effectiveness of the proposed control scheme.

Finite-time command filter-based adaptive fuzzy tracking control for stochastic nonlinear induction motors systems with unknown backlash-like hysteresis

In this article, an adaptive fuzzy control method is proposed for induction motors (IMs) drive systems with unknown backlash-like hysteresis. First, the stochastic nonlinear functions existed in the IMs drive systems are resolved by invoking fuzzy logic systems. Then, a finite-time command filter technique is exploited to overcome the obstacle of “explosion of complexity” emerged in the classical backstepping procedure during the controller design process. Meanwhile, the effect of the filter errors generated by command filters is decreased by utilizing corresponding error compensating mechanism. To cope with the influence of backlash-like hysteresis input, an auxiliary system is constructed, in which the output signal is applied to compensate the effect of the hysteresis. The finite-time control technology is adopted to accelerate the response speed of the system and reduce the tracking error, and the stochastic disturbance and backlash-like hysteresis are considered to improve control accuracy. It’s shown that the tracking error can converge to a small neighborhood around the origin in finite-time under the constructed controller. Finally, the availability of the presented approach is validated through simulation results.

Adaptive fuzzy tracking control for vibration suppression of tower crane with distributed payload mass

As an indispensable transportation tool, tower cranes are widely used in construction sites. However, since the mass and volume of the transported goods become larger, most of the traditional control algorithms designed for Concentrated Payload Mass (CPM) are not sufficient for Distributed Payload Mass (DPM). One of the main differences between DPM and CPM is that the payload swing caused by the moment of inertia of DPM cannot be effectively suppressed, resulting in residual payload swing. Furthermore, due to different working environments, accurate system parameters are difficult to obtain, leading to errors in their positioning. Hence, an adaptive fuzzy control method is proposed, which has good control performance in the presence of external disturbances and parameter uncertainties. The effectiveness of the proposed method was verified through experiments. This improves the efficiency of transportation and is of more practical significance for automation construction. In future research, fault diagnosis and active fault-tolerant control will be considered as the following research directions, taking into account the problem of actuator/sensor failures due to prolonged system operation.

Fuzzy Adaptive Optimal Consensus Fault-Tolerant Control for Stochastic Nonlinear Multiagent Systems

This article investigates the problem of adaptive fuzzy optimal distributed consensus control for stochastic multiagent systems (MASs) with full-state constraints and nonaffine nonlinear faults. Fuzzy logic systems are employed to identify the unknown nonlinearities. To solve the problem of optimal state constraint control, a barrier Lyapunov function based optimal cost function is designed. By introducing Butterworth low-pass filter into control design, the deleterious effects raised by nonlinear fault can be compensated. By utilizing adaptive dynamic programming algorithm in critic–actor construction, a fuzzy adaptive distributed optimal consensus fault-tolerant control method is proposed, which can ensure that all signals of the controlled system are semiglobally uniformly ultimately bounded in probability, and outputs of the follower agents keep consensus with the output of leader. In addition, system states are all not exceeded their constrained bound. Finally, simulation results are provided to illustrate the feasibility of the developed control method and theorem.

Event-Based Adaptive Fixed-Time Fuzzy Control for Active Vehicle Suspension Systems With Time-Varying Displacement Constraint

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

Event-triggered adaptive fuzzy control for stochastic nonlinear time-delay systems

This paper develops an event-triggered adaptive fuzzy control (AFC) scheme for stochastic nonlinear time-delay systems. With the aid of event-triggered mechanism, the salient superiority is to reduce the data transmission and communication burden. By virtue of Lyapunov-Krasovskii function, the time-delay term is effectively addressed. The fuzzy logic systems are exploited to model system uncertainties, meanwhile the norm of fuzzy weight vector is taken as estimation, which simplifies controller complexity. The devised AFC method can not only surmount the adverse influence deriving from time-delay, but also save network and communication resource. Finally, simulation results substantiate the validity of the designed control scheme.

Event-triggered adaptive fuzzy tracking control for high-order stochastic nonlinear systems

This paper is concerned with event-triggered adaptive fuzzy tracking control for high-order stochastic nonlinear systems. The approach of fuzzy logic systems (FLSs) approximation is extended to high-order stochastic nonlinear systems to deal with the unknown nonlinear uncertainties. A novel high-order adaptive fuzzy tracking controller is firstly presented via a backstepping approach and event-triggering mechanism which can mitigate the unnecessary waste of computation and communication resources. Based on the above techniques, frequently-used growth assumptions imposed on unknown system nonlinearities are removed and the influence for the high order is handled. The proposed high-order adaptive fuzzy tracking control method not only deals with the influence of high order, but also ensures that the tracking error converges to a small neighborhood of the origin in probability. Finally, the effectiveness of the proposed control method is illustrated by a numerical example.

Adaptive fuzzy finite-time control with prescribed performance for waverider vehicles

This paper proposes an adaptive fuzzy control method with prescribed performance for Waverider Vehicles (WVs), being able to guarantee finite-time convergence and small overshoot for tracking errors. Firstly, we design a new type of performance function that is independent of the initial error, and possess finite-time convergence and small overshoot. Then, we transform the inequality constraints on tracking errors into an unconstrained equation by introducing a transformed error. On this basis, we design a prescribed performance control (PPC) approach to limit the tracking errors within prescribed funnels utilizing the transformed error and fuzzy approximation, which ensures that satisfactory transient performance and steady-state accuracy can be guaranteed for tracking errors. Compared with the existing PPC, the improvement is to assure finite-time convergence of tracking errors with almost zero overshoot. Finally, compared simulations are given to verify the advantage.

Tracking Control of Uncertain High-Order Nonlinear Systems with Odd Rational Powers and the Dead-Zone Input: A Direct Fuzzy Adaptive Control Method

Tracking Control of Uncertain High-Order Nonlinear Systems with Odd Rational Powers and the Dead-Zone Input: A Direct Fuzzy Adaptive Control Method

Adaptive Fuzzy Tracking Control of Switched MIMO Nonlinear Systems With Full State Constraints and Unknown Control Directions

In this brief, an adaptive fuzzy tracking control method is proposed for switched multi-input multi-output (MIMO) nonlinear systems with time-varying full state constrains (TFSCs) and unknown control directions. First, the fuzzy logic systems are utilized to approximate unknown dynamic functions. A tangent barrier Lyapunov function (BLF-Tan) is used to solve the problem of TFSCs, and the unknown control directions problem is addressed by applying Nussbaum-type function. Then, an adaptive tracking controller is constructed by the backstepping technique. Under the designed control scheme, all the systems signals are derived to be bounded, and the tracking error of the systems is converged to a neighborhood near zero. Finally, the simulation example illustrates the control design programme is reasonable and effective.

Dynamic event‐triggered fixed‐time adaptive fuzzy control of nonstrict‐feedback switched nonlinear systems

AbstractIn this paper, a dynamic event‐triggered fixed‐time adaptive fuzzy control method is set up to solve the tracking problem for a type of nonstrict‐feedback switched nonlinear systems with asymmetric tracking error constraints. The proposed method constructs different event‐triggered adaptive fuzzy controllers for different subsystems together with switched update laws and a switching dynamic event‐triggering mechanism (DETM). Under this switching DETM, the difficulty caused by asynchronous switching between candidate controllers of subsystems and subsystems is effectively overcome, and the known limitations on maximum asynchronous time in the existing literature are not needed. Meanwhile, Zeno behavior is avoided by deriving a strictly positive lower bound of interevent times. It is guaranteed that the asymmetric tracking error constraints are never violated and the tracking error can converge to a small neighborhood of origin in a fixed‐time under arbitrary switchings. Finally, the feasibility of the derived theoretical results is demonstrated by employing a numerical example and a switched Chua's Circuit system.