Galloping and VIV control of square-section cylinder utilizing direct opposing smart control force

The increasing use of nonlinear loads entails serious electrical energy quality problems in power grids. Active power filter could lead to significant improvement in harmonic compensation performance. In this paper, an adaptive second-order sliding mode fuzzy control based on linear feedback strategy for a three-phase active power filter is proposed to eliminate harmonics. This scheme is constituted by adaptive controller, second-order sliding mode controller, fuzzy controller, and linear feedback controller. The linear feedback controller can compensate the system’s nonlinearities and make the new system have a linear behavior between the input and output. Moreover, the second-order sliding mode controller could transfer the discontinuities in the controller and reduce system chattering. Simulation results illustrate the proposed controller has better performance compared with fuzzy control and adaptive sliding mode fuzzy control.

Sliding mode control has been shown to be a robust and effective control approach for stabilization of nonlinear systems. However the dynamic performance of the controller is a complex function of the system parameters, which is often uncertain or partially known. This paper presents an adaptive fuzzy sliding mode control for a class of underactuated nonlinear mechanical systems. An adaptive fuzzy system is used to approximate the uncertain parts of the underactuated system. The adaptive law is designed based on the Lyapunov method. The proof for the stability and the convergence of the system is presented. Robust performance of the adaptive fuzzy sliding mode control is illustrated using a gantry crane system. Simulation results demonstrate that the system output can track the reference signal in the presence of modelling uncertainties, external disturbances and parameter variation.

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator is proposed. Based on the Udwadia–Kalaba motion constraint equation, the dynamic equation of the constrained manipulator with both ideal and non-ideal constraints is obtained. Considering the uncertainty of the terminal non-ideal constrained force and the chattering phenomenon of sliding mode control, the adaptive fuzzy and the sliding mode control method are combined to control the constrained manipulator. Because the system is constrained, the model order reduction method is innovatively used in the control algorithm. The stability of the system is proved by Lyapunov theorem. For demonstrating the effectiveness of the control algorithm, the 2-degree-of-freedom manipulator is taken as the research object. Finally, the high-precision control of the manipulator is achieved and the chattering phenomenon caused by the sliding mode control is weakened.

This paper intends to design and develop an adaptive fuzzy sliding mode controller (SMC) for robotic manipulator. Since it is not viable to pair the SMC operations with the system model every time, this paper adopts a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. In the second stage, it represents the derived fuzzy rules based on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the noise and validates the effectiveness of the proposed and conventional models. Index Terms : Sliding Mode Control, Robot manipulators, Controller, Noise.

This paper investigates the position regulation problem of permanent magnet synchronous motor (PMSM) servo system based on adaptive fuzzy sliding mode control (AFSMC) method. Adaptive fuzzy sliding mode control is designed by using sliding surface as input of fuzzy controller, and adaptive fuzzy controller to approximate the equivalent control law, thus overcome the impact of model inaccuracy and external disturbances. Then, sliding model controller is designed to compensate the error between the equivalent control law and the fuzzy controller by using adaptive method to estimate the upper bound of the approximation error, meanwhile, adaptive fuzzy sliding mode control can weaken the chattering and improve the robustness of the system, make the motor position reach the reference value in finite time, obtaining a better tracking precision. Simulation results illustrate that the proposed control scheme has much better performance than that of conventional PI.

This paper presents an adaptive Fuzzy Sliding Mode Control approach for an Assist-as-Needed (AAN) strategy to achieve effective human–exoskeleton synergy. The proposed strategy employs an adaptive instance-based learning algorithm to estimate muscle effort, based on surface Electromyography (sEMG) signals. To determine and control the inverse dynamics of a highly nonlinear 4-degrees-of-freedom exoskeleton designed for upper-limb therapeutic exercises, a modified Recursive Newton-Euler Algorithm (RNEA) with Sliding Mode Control (SMC) was used. The exoskeleton position error and raw sEMG signal from the bicep’s brachii muscle were used as inputs for a fuzzy inference system to produce an output to adjust the sliding mode control law parameters. The proposed robust control law was simulated using MATLAB-Simulink, and the results showed that it could instantly adjust the necessary support, based on the combined motion of the human–exoskeleton system’s muscle engagement, while keeping the state trajectory errors and input torque bounded within ±5×10−2 rads and ±5 N.m, respectively.

In this paper, an equivalent multi‐body dynamics model of high‐speed train with uncertain parameter is established. Based on this detailed model and neural network minimum parameter learning algorithm, an adaptive sliding mode control method is designed for speed and position tracking control of high‐speed train, and the stability of the proposed control method is proved strictly. Then an adaptive fuzzy sliding mode control method (AFSMC) is presented to avoid chattering caused by excessive robust switching gain. Finally, numerical tests are carried out on the proposed AFSMC under different scenarios and operation strategies, and model reference adaptive control (MRAC), PID control and sliding mode control are also tested under the same condition for performance comparison. The results show that the proposed control method is superior to the currently used method. More specifically, compared with MRAC and PID, the average speed tracking accuracy of AFSMC is improved by 0.25 km/h and 0.18 km/h, and the average position tracking accuracy is improved by 0.68 m and 0.7 m, respectively.

In this paper, an adaptive fuzzy sliding mode controller is employed for air supply on proton exchange membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given set point in order to prevent oxygen starvation and damage to the fuel-cell stack. The proposed control scheme consists of two parts: a sliding mode controller (SMC) and fuzzy logic controller (FLC) with an adjustable gain factor. The SMC is used to calculate the equivalent control law and the FLC is used to approximate the control hitting law. The performance of the proposed control strategy is analysed through simulations for different load variations. The results indicated that the adaptive fuzzy sliding mode controller (AFSMC) is excellent in terms of stability and several key performance indices such as the integral squared error (ISE), the integral absolute error (IAE) and the integral time-weighted absolute error (ITAE), as well as the settling and rise times for the closed-loop control system.

This paper presents an application of adaptive fuzzy sliding mode control (AFSMC) for a benchmark problem on a seismically excited highway bridge. This approach combines great advantages of sliding mode control (SMC), adaptive control and fuzzy control without compromising the stability or robustness. Switching-type control law of the conventional SMC and uncertainty part of the equivalent control is approximated by fuzzy controller to attenuate the chattering phenomenon of SMC and harmful effects caused by uncertainties. In order to reduce the complexity of the fuzzy rule bases, adaptive law based on Lyapunov function and Barbalat lemma is designed. AFSMC is also integrated with clipped-optimal strategy to illustrate its efficiency for semi-active MR dampers. Robustness of the AFSMC controller has been demonstrated analyzing ±30% perturbation in the stiffness of the bridge. Numerical results indicate that the response quantities of the highway bridge can be reduced to a satisfactory level superior to those of the sample LQG and LQG/clipped-optimal algorithms. Copyright © 2009 John Wiley & Sons, Ltd.

This paper is concerned with the issue of adaptive fuzzy sliding mode control for fuzzy underactuated translational oscillator with rotating actuator (TORA). Since the system considered in this paper is accompanied by strong inputs coupling and nonlinear characteristics, the feedback decoupling and fuzzy technique are introduced to establish a linear fuzzy underactuated TORA system. The two-order fuzzy sliding surface with adaptive parameters is designed, where the adaptive control is introduced to adjust the parameters of sliding surface such that the system performance could be improved. The sliding mode control law is synthesized to guarantee the sliding mode dynamics to be asymptotically stable and robust, and the reachability analysis is presented to ensure that the system trajectories can be driven onto the sliding mode surface and remain there for the subsequent time. Simulation results are provided to compare with the existing results and validate the effectiveness and feasibility of the proposed scheme.

The relationship between the tip clearance flow (TCF) and the blade vibration in the lock-in region was numerically investigated on a transonic rotor. Both the bending (1B) and torsional (8th) modes were analyzed under 0 ND. The time marching method and a single-passage model were used, which were verified by citing and comparing with the results in references. The phase of the TCF (referring to the phase difference between the unsteady pressure caused by the TCF and the blade vibration) changed with the frequency ratio in the lock-in region. The strength of the TCF was influenced slightly by the blade vibration amplitude. A separation method of the unsteady pressure caused by the TCF and the blade vibration was developed and verified at different conditions. The unsteady pressure of nonsynchronous vibration (NSV) was separated into the components of the TCF and the blade vibration under the 1B and 8th modes. The unsteady pressure component of the TCF changed little with the vibration amplitude and mainly existed in the tip area. The unsteady pressure component of the blade vibration was larger at part spans and its distribution depended on the modal shape and the vibration amplitude. The unsteady pressure components of the TCF and the blade vibration determined the aerodynamic work/damping in the lock-in region. The aerodynamic work components of the TCF and the blade vibration increased linearly and at a rate of the square with the vibration amplitude, respectively. TCF was dominant in the initial stage of vibration. With the vibration amplitude increasing, the aerodynamic work done by the unsteady pressure component of the blade vibration gradually caught up. The aerodynamic damping of the TCF changed with the phase of the TCF. TCF provided positive damping at some phases and negative damping at other phases. In the initial stage of vibration, the system was stable at the phases TCF providing positive damping and unstable at the phases of negative damping. NSV occurred only when TCF provided negative damping and the unsteady pressure component of the blade vibration provided positive damping. If the aerodynamic damping of the blade vibration was negative, the vibration would be enlarged until failure. Regardless of other damping forms, the maximum amplitude of NSV can be obtained by calculating the balance of the aerodynamic work. For the 8th mode, the limit amplitude under 0 ND was 0.0926%C, which corresponded to vibration stress of about 60 MPa.

Considering the robust control of stabilized platform of rotary steering drilling system, an adaptive fuzzy sliding mode control strategy based on genetic optimization is presented. Firstly, the universal approximation property of fuzzy system is used to approximate the uncertain external disturbance upper bound of stabilized platform under wording condition. Subsequently, sliding mode controller is designed to guarantee the robustness of the closed-loop system and sign function is replaced by bipolar sigmoid function to weaken chattering. Finally, genetic algorithm (GA) is applied to search the optimal controller parameters, including switching function coefficient, membership function of fuzzy system, adaptive coefficient of fuzzy system and sigmoid function coefficient. Simulation results show that this control strategy can make stabilized platform achieve optimal control performance and robustness.

Direct adaptive fuzzy sliding mode control design for discrete non-affine nonlinear systems is presented to trajectory tracking problems with disturbance. In order to eliminate chattering of sliding mode control, dynamic fuzzy logical system (DFLS) is used for implementing equivalent control, in which parameters are self-tuning online. Stability of the SMC is validated using Lyapunov analysis theory. The over-all system is adaptive and asymptotically stable with chattering free. Simulations of a numerical example verified the performance of the control design.

An indirect adaptive fuzzy sliding mode control scheme for a class of discrete-time systems with model uncertainty and external disturbance is developed. In this approach, a discrete-time exponential reaching law is used to obtain the ideal sliding mode control law. Then, two fuzzy systems and a third one are used to approximate unknown functions of the system and the switch-type control in the ideal control law, respectively, wherein the consequent parameters are adjusted by an on-line adaptation law with parameter projection. Towards to Tank pressurization system, experiment results have shown the effectiveness of the algorithm.

An indirect adaptive fuzzy sliding mode controller is presented for trajectory tracking control of uncertain multi-link robots. This approach combines adaptive fuzzy control and global fast sliding mode control, and adopts a global fast sliding mode manifold which can drive system to reach balance point in finite time. Three fuzzy systems are designed to learn the uncertain robot parameters and external disturbances. So the control system can automatically track the robot parameters and disturbances, and reduces chattering of the controller. Prediction estimation for robot parameters and disturbances is not needed too. The control speed is quickened by simplifying the robot structure and decreasing learning parameters of the uncertain robot. Moreover, the system stability is proved by Lyapunov principle. Simulation results verify the validity of the control scheme.