Adaptive Fuzzy Sliding Mode Control Research Articles

Nonlinear energy sinks with nonlinear control strategies in fluid-structure simulations framework for passive and active FIV control of sprung cylinders

This article investigates the vortex-induced vibration reduction of a circular cylinder as well as the flow-induced vibration suppression of an elastically-mounted square cylinder freely oscillating in the transverse and streamwise directions with the aid of passive and active nonlinear energy sinks that benefit from nonlinear intelligent control strategies. To this end, firstly, the optimized parameters of the passive nonlinear energy sink including mass, damping and stiffness ratios are tuned. Next, the passive control method is turned into an active nonlinear energy sink by employing an adaptive fuzzy sliding-mode controller. Owing to the computational complexity of the employed controller, the obtained results are used to design a fuzzy controller based on the adaptive neuro-fuzzy inference system. The results of collaborative fluid-structure interaction (FSI) simulations indicate that the nonlinear energy sink equipped with the fuzzy controller based on the active fuzzy sliding-mode controller successfully decreases the maximum transverse and streamwise displacements of circular cylinder by, respectively, 95.56% and 98.53% at Re = 85, 96.26% and 97.64% at Re = 90, 95.80% and 97.64% at Re = 95, and 93.82% and 97.05% at Re = 100. Also, for the square cylinder, the maximum transverse and streamwise displacements decrease by 95.06% and 90.9% at Re =87.5, 99.94% and 86.66% at Re = 90, 95.80% and 96.33% at Re = 220, and 72.41% and 99.21% at Re = 250 compared to the uncontrolled case. All these cases demonstrate the superiority of active nonlinear energy sink over its passive counterpart.

A predictive approach to adaptive fuzzy sliding-mode control of under-actuated nonlinear systems with input saturation

In this paper, a computationally efficient robust predictive control method is proposed for continuous-time under-actuated SISO systems in the presence of actuator saturation and state-dependent uncertainties. The proposition of this research is to employ the idea of model prediction together with the Adaptive Fuzzy Sliding-Mode Control (AFSMC) for tuning the sliding surface parameters by predicting the anticipated effects of uncertainties. In the proposed scheme, only after the trigger conditions are met, the coefficients of the sliding surface are updated and the AFSMC is applied. Hence, computational complexity can be controlled by adjusting the switching rule. In the AFSMC, a fuzzy system is used to approximate a nonlinear function, and a robust term to compensate for any possible mismatches. An adaptively tuned gain is also applied to the control signal to prevent instability caused by the actuator saturation. Based on the updating sliding surface, fuzzy singletons, the upper bound of the fuzzy approximation error, and the saturation gain are adaptively tuned. Closed-loop stability is shown to be guaranteed using the multiple Lyapunov functions theorem and the Barbalat’s lemma. Finally, the method is applied for the depth control of an Autonomous Underwater Vehicle (AUV), depicting the excellent performance of the proposed method.

Fuzzy Adaptive Sliding Mode Impedance Control of Fracture Reduction Robot

With the development of robotics, it will be possible for fracture reduction robots to replace doctors in performing surgery operations. The surgery accuracy and safety by the reduction robot is important. In order to compensate various uncertainties and nonlinear problems in the reduction robot impedance control system, this paper proposes a fuzzy adaptive sliding mode impedance control of the reduction robot, which includes force residual observer (FRO) and fuzzy adaptive sliding mode controller (FASMC). The FRO is used to estimate the external force between the reduction robot and the fractured musculoskeletal tissue in real-time, no force sensor is installed at the end-effector of reduction robot. The FASMC mainly includes three parts: linearizing the system by inverse dynamics and PD control, improving the system's robustness by the sliding mode control and fuzzy adaptive switching gain to reduce chattering, eliminating the nonlinear disturbance term of the system, and the normalized factor linear combination fuzzy system is used to compensate. The co-simulation of the fracture reduction robot shows that the FASMC impedance control method has a better steady-state position and force tracking accuracy, the steady-state position accuracy is -0.733 mm, and the steady-state force accuracy is -2.12 N. This paper provides an effective method for impedance control of the fracture reduction robot.

Secondary control of islanded microgrid using robust finite time decentralized method based on adaptive fuzzy sliding mode controller

Secondary voltage control of islanded microgrid is one of the main needs of microgrid hierarchical control from one perspective and the realization of stable smart grids from another aspect. This article aims to achieve several goals simultaneously: First, it provides a decentralized structure for controlling and regulating the voltage of DGs, in which the effects of nonlinear terms on the dynamics of the microgrid system are considered without elimination or linearization. The fuzzy method has been used to approximate the uncertainties, the interactions of agents and nonlinear terms, and the adaptive method has been used to obtain the optimal fuzzy approximation gains. Sliding mode rules are also used to obtain a control signal and using Lyapunov's theory, the finite time and robust stability of the closed-loop control system is guaranteed. The finite time approach ensures the achievement of convergence in a finite and definite time, and the robust approach realizes the dynamic stability of the system against uncertainties and disturbances. The results of the microgrid system simulation show well the proposed controller capability in different operating conditions.

Design of Adaptive Fuzzy Sliding Mode Controller for Mobile Robot

Design of Adaptive Fuzzy Sliding Mode Controller for Mobile Robot

Modeling and control of probe distance, substrate and probe of AFM using AFSMC in the presence of disturbances

Controlling the motion and its associated parameters is among the most important topics in the field of manipulation and displacement of nanoparticles. Accordingly, this article considers the control of nanomanipulation process in three general areas: 1) the distance between probe and surface, 2) the substrate, and 3) the probe of atomic force microscope (AFM). This study benefits from the adaptive fuzzy sliding-mode (AFSMC) control technique. Moreover, elliptical and cubic geometries are utilized. The results for the control of probe distance from substrate show that the system attains the acceptable value of 50 nm after 0.8 s. The desired output for the derivative of distance is fulfilled after 0.07 s. The results for the control of substrate in the horizontal direction show that for different values of λ, the desired value is obtained in different manners. The comparison between the results of this study and previous researches using the sliding mode control and proportional-integral-derivative method indicates the accuracy of the proposed method. The simulation results of the control of atomic force microscope probe for elliptical and cubic geometries demonstrate that the deviation of probe for these two geometries reaches its desired value in less than 0.04 s. However, the main difference is observed when investigating the position of atomic force microscope probe; the elliptical nanoparticle with an average radius of 75 nm has a delay of around 0.02 s for reaching the desired value compared with the cubic nanoparticle with a side length of 75 nm. In addition, the overshoot of elliptical geometry is more than that of cubic case. Finally, motion tracking along a specific path is simulated. In addition, trajectory assessment is realized on an actual experimental surface.

Study on the control strategy of chaotic oscillation of synchronous generator based on the linear feedback adaptive fuzzy sliding mode control

In the case of periodic load disturbance, the chaos oscillation phenomena may be caused by synchronous generator to threaten the safety operation of the network. In order to solve this problem, this paper presents the strategy on the control of linear feedback adaptive fuzzy siding mode. Under the action of the controller, the system output track the desired trajectory, and the fuzzy control rules are used to suppress the high frequency chattering in conventional sliding mode controller. The theoretical analysis and simulation results show that the designed controller can effectively control the power system oscillation, and it has certain robustness.

Adaptive Fuzzy Sliding Mode Control for a Micro Gyroscope with Backstepping Controller.

This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation.

Spacecraft attitude tracking for space debris removal using adaptive fuzzy sliding mode control

In this paper, the problem of attitude tracking control for clean platform, with no prior knowledge about the bounds of the parametric uncertainties and external disturbances, in the process of space debris removal is investigated. We proposed a robust adaptive control strategy, which is based on the coupling of the fuzzy logic system (FLS) with sliding mode control (SMC). First, a desired attitude solution model for the space debris removal is developed, and the error attitude dynamics equation of clean platform is formulated. Second, a sliding mode controller is designed due to its appreciable features to the uncertainties and disturbances. On this basis, a FLS is employed to approximate the unknown system functions, and the approximation errors are eliminated using adaptive algorithm. Meanwhile, the control gains of the switching control, being constant in the conventional SMC approach, are adjusted adaptively by fuzzy logic inference techniques in order to reduce the chattering phenomenon. The global stability of the closed-loop system is proven by the Lyapunov stability theorem. Finally, detailed numerical simulations are carried out to verify the robustness and effectiveness of the proposed adaptive fuzzy sliding mode control (AFSMC) approach. The simulation results show that the proposed AFSMC approach can ensure that the spacecraft exactly tracking the desired attitude in presence of inertial uncertainties and external disturbances and effectively reduce the chattering against to the conventional SMC approach.

Hybrid Control Using Adaptive Fuzzy Sliding Mode Control of Doubly Fed Induction Generator for Wind Energy Conversion System

In this paper, we are interested in the development of a robust control of active and reactive power for a Doubly Fed Induction Generator for variable speed wind energy using hybrid control by Adaptive Fuzzy logic and Sliding Mode Controller (AFSMC). This type of control is introduced to avoid the major disadvantage of variable structures systems which is the chattering phenomenon. Using the variable structure is to ensure the high dynamic of convergence and the robustness towards parametric variations and disturbances. Whereas the fuzzy control is introduced here in order to remove residual vibrations in high frequencies. Simulation results show that the proposed control strategy gives better results.

The position control of the ball and beam system using state-disturbance observe-based adaptive fuzzy sliding mode control in presence of matched and mismatched uncertainties

This paper presents a state-disturbance (S-D) observer-based adaptive fuzzy sliding mode control (AFSMC) to control the ball and beam system (BBS) in the presence of matched and mismatched uncertainties. First, an SMC scheme is designed to an error-based state-space representation of the BBS involving uncertainties. Stability analysis shows that the closed-loop system with the proposed SMC in the presence of uncertainties has global asymptotic stability. Second, a finite-time S-D observer is used to estimate uncertainties, the speed of the ball moving on the beam, and the angular velocity of the beam rotation. In this case, not only does the implementation of the S-D observer-based SMC eliminate the difficulty of access to information of state variables but also it can reach a suitable control input range by selecting smaller control input coefficients. Mathematical proof proves that the closed-loop system reaches the global asymptotic stability using the proposed S-D observer-based SMC in the presence of uncertainties. Finally, an adaptive fuzzy approximator is designed such that if the correct estimation of the boundary of uncertainties is not available, the closed-loop system with the S-D observer-based AFSMC achieves at least the global uniform bounded stability. The simulation and experimental results verify the efficiency of the suggested method compared to similar methods.

Adaptive fuzzy sliding mode formation controller for autonomous underwater vehicles with variable payload

PurposeIn this paper, an adaptive fuzzy sliding mode controller (AFSMC) is developed for the formation control of a team of autonomous underwater vehicles (AUVs) subjected to unknown payload mass variations during their mission.Design/methodology/approachA sliding mode controller (SMC) is designed to drive the state trajectories of the AUVs to a switching surface in the state space. The payload mass variation results in parameter variation in AUV dynamics leading to actuator failure. This further leads to loss of communication among the members of the team. Hence, an adaptive SMC based on fuzzy logic is developed to maintain the coordinated motion of AUVs with payload mass variation.FindingsThe results are obtained by employing adaptive SMC for AUVs with and without payload variations and are compared. It is observed that the proposed adaptive SMC exhibits improved performance and tracks the desired trajectory in less time even with variation in the payload. The adaptive fuzzy control algorithm is developed to handle variation in payload mass variation. Lyapunov theory is used to establish stability of AFSMC controller.Research limitations/implicationsPerfect alignment is assumed between centres of gravity (OG) and buoyancy (OB), thus AUVs maintaining horizontal stability during motion. The AUVs’ body centres are aligned with centres of gravity (OG), thus the distance vector being rg = [0,0,0]T. As it is a tracking problem, sway motion cannot be neglected as the AUVs are travelling in a curved locus, hence susceptible to Coriolis and centripetal forces. The AUV is underactuated as only two thrusters at the stern plate that are employed for the surge and yaw controls and error in Y- direction are controlled by adjusting control input in surge and heave direction. Control inputs to the thruster are constants, and depth control is achieved by adjusting the rudder angle.Practical implicationsAUVs are employed in military mission or surveys, and they carry heavy weapons or instrument to be deployed at or picked from specific locations. Such tasks lead to variation in payload, causing overall mass variation during an AUV’s motion. A sudden change in the mass after an AUV release or pick load results in variation in depth and average velocity.Social implicationsThe proposed controller can be useful for military missions for carrying warfare and hydrographic surveys for deploying instruments.Originality/valueA proposed non-linear SMC has been designed, and its performances have been verified in terms of tracking error in X, Y and Z directions. An adaptive fuzzy SMC has been modelled using quantized state information to compensate payload variation. The stability of AFSMC controller is established by using Lyapunov theorem, and reachability of the sliding surface is ensured.

Adaptive Fuzzy Sliding Mode and Robust Tracking Control for Manipulators with Uncertain Dynamics

In response to the issue of the trajectory tracking control problem of manipulators with uncertain parameters and external disturbance, an adaptive fuzzy sliding mode robust control algorithm is proposed. Sliding mode control (SMC) is adopted to perform robotic manipulator trajectory tracking control. Then, a fuzzy logic system is used for adaptive adjustment of switching gain of the SMC and to reduce the buffeting problem. Next, compensation is made by using the robust controller in consideration of the impacts of unmodeled dynamics and external disturbance. The simulation experiment on a two axes robotic manipulator shows that, with the proposed control method, the sliding mode control input signal is kept smooth, and the manipulator has high trajectory tracking precision.

Fuzzy adaptive sliding mode control of PMSM based on extended state observer

Fuzzy adaptive sliding mode control of PMSM based on extended state observer

Motion control of a space manipulator using fuzzy sliding mode control with reinforcement learning

The free-flying space manipulators present challenges in controlling the motions of both the spacecraft bus and the manipulator, because of the highly-coupling system dynamics and the unknown space environment disturbances. Although the sliding mode controllers are robust to the unknown disturbances and system uncertainties, the chattering effect could affect the pointing accuracy and the lifetime of the actuators. This paper first introduces the dynamics of a CuBot, which is a 3-rigid-link manipulator based on the CubeSat platform. To maintain the robustness while decreasing the chattering effect, an innovative reinforcement learning based fuzzy adaptive sliding mode controller is proposed. To maintain the robustness while reducing the chattering effect, an innovative reinforcement learning based fuzzy adaptive sliding mode controller is proposed. The switching gain is updated to estimate the lumped upper bound of the system uncertainties and the unknown disturbances, and then a new fuzzy logic adaptive law is applied on the switching gain to decrease the chattering effects. On top of that, the fuzzy logic rules are tuned by an innovative modified reinforcement learning mechanism to achieve the better tracking performance. The uniformly ultimately bounded tracking errors are guaranteed by the proposed control scheme, and the effectiveness is validated by the simulation results.

Adaptive Fuzzy Sliding Mode Control for High-Precision Motion Tracking of a Multi-DOF Micro/Nano Manipulator

This letter introduces a robust adaptive control methodology for piezo-actuated flexure-based micro/nano manipulation mechanisms. This special control approach is established for tracking of the desired complex motion trajectories with system uncertainties. Further, the control methodology is formulated to accommodate not only the system uncertainties but also nonlinearities including the hysteresis effect and external disturbances in the motion of a 3-degrees-of-freedom (3-DOF) XYZ micro/nano manipulator. Adaptive fuzzy sliding mode control (AFSMC) is developed for the formulation of the control methodology. The fuzzy logic component and adaptation law employed in the controller are used to approximate the system uncertainties and compensate for the effect of nonlinear terms in the system. The resulting control strategy is devised using a sliding mode control scheme. The asymptotic tracking ability of the designed controller is proved by using Lyapunov-based stability analysis. Furthermore, an experimental study is performed on the 3-DOF compliant monolithic parallel micro/nano manipulator and the results confirm that the designed control approach produces high-precision motion tracking and smaller tracking errors compared to a model-free control technique, i.e. Proportional-Integral-Derivative (PID) controller.

Interval type-2 adaptive fuzzy sliding mode control design of reentry attitude for reusable launch vehicles

Interval type-2 adaptive fuzzy sliding mode control design of reentry attitude for reusable launch vehicles

FUZZY-SLIDING-MODE-BASED ROBUST TRACKING CONTROL OF AUTONOMOUS UNDERWATER VEHICLES

Control of an autonomous underwater vehicle (AUV) is difficult due to its nonlinear and coupled characteristics, parameter uncertainties, and unknown disturbances. This paper presents a motion control system based on an adaptive fuzzy sliding mode control (FSMC) method for trajectory tracking of an AUV. The proposed FSMC method is primarily based on a combination of sliding mode control (SMC) strategy with an adaptive fuzzy algorithm to estimate external disturbances and weaken the chattering effect. A waypoint guidance law based on a line-of-sight (LOS) algorithm is applied to the guidance system. Simulation studies confirm a good performance of the proposed controller. The results are compared with those obtained with a sliding mode controller in terms of robustness and accuracy.

Adaptive Fuzzy Sliding Mode Control for a 3-DOF Parallel Manipulator with Parameters Uncertainties

Parallel manipulators possess the advantages of being compact structure, high stiffness, stability and high accuracy, so such parallel manipulators have been widely employed in application fields as diverse as parallel kinematic machine, motion simulator platform, medical rehabilitation device, and so on. Due to the complexity of the closed-loop structural system, an accurate dynamic model is very difficult to be derived in the absence of some uncertainties parameters and external disturbances. In order to improve the trajectory tracking accuracy with time-varying and nonlinear parameters, this paper addresses the design and implement of adaptive fuzzy sliding mode control (AFSMC) for a three-degree-of-freedom (DOF) parallel manipulator, where internal force term can be linearly separated into a regression matrix and a parameter vector that contains the estimated errors. Furthermore, fuzzy inference unit is utilized to modify the gain parameters in real-time by using the state feedback from the task space and the adaptive law is performed to update uncertainties in dynamic parameters. The proposed controller is deduced in the sense of Lyapunov theory to guarantee the stability while improving the trajectory tracking performance. Finally, simulation experiment results demonstrate that the proposed control method is insensitive to uncertainties and disturbances and permits to decrease the requirement for the bound of these uncertainties, which validate the effectiveness of the developed control method and exhibit good trajectory tracking performance compared with sliding mode control (SMC) and fuzzy sliding model control (FSMC).

A modified extreme seeking-based adaptive fuzzy sliding mode control scheme for vehicle anti-lock braking

Vehicle Anti-lock Braking Systems (ABS) are designed to optimise vehicle braking performance but there are some challenges for their control. One of the challenges is that the optimal slip ratio is difficult to obtain in real time and it can differ under different road conditions. Another challenge is that ABS are non-linear and many of their parameters are difficult to identify in advance. To solve these problems, a new extreme seeking-based adaptive fuzzy sliding mode control strategy is proposed. The proposed Modified Sliding Mode-based Extreme Seeking (MSMES) algorithm is able to avoid the large oscillation and automatically search for the optimal slip ratio. An Adaptive Fuzzy Sliding Mode Controller (AFSMC) is designed to mimic an ideal controller. The simulation results show that the integrated control scheme MSMES-AFSMC is able not only to search for the optimal slip ratio under different road conditions but also has a better braking performance than the conventional COSR for ABS.