Adaptive Fuzzy Sliding Mode Control Research Articles

Adaptive Fuzzy Sliding Mode Control of Omnidirectional Mobile Robots with Prescribed Performance

Adaptive fuzzy sliding-mode control design for omnidirectional mobile robots with prescribed performance is presented in this work. First, an error transformation which transforms the constrained variable into an unconstrained one is carried out. Next, a fuzzy logic system (FLS) for approximating the unknown dynamics is constructed. Based on such a model, a nominal adaptive linearizing controller incorporating a serial-parallel model (SPM)-based composite algorithm, which improves the tracking performance of the overall closed-loop system, is synthesized. To solve the so-called “loss of controllability” problem, a smooth-switching algorithm is embedded which hands over the control authority to an auxiliary sliding-mode controller until the danger is safely bypassed. The proposed design ensures the semi-globally uniformly ultimately bounded stability of the closed-loop signals. Simulation works demonstrating the validity of the proposed design are presented in the final.

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.

An adaptive fuzzy sliding mode control under model uncertainties and disturbances: second-order non-linear system

An adaptive fuzzy sliding mode control under model uncertainties and disturbances: second-order non-linear system

Robust multi-input multi-output adaptive fuzzy terminal sliding mode control of deep brain stimulation in Parkinson\u2019s disease: a simulation study

Deep brain stimulation (DBS) has become an effective therapeutic solution for Parkinson’s disease (PD). Adaptive closed-loop DBS can be used to minimize stimulation-induced side effects by automatically determining the stimulation parameters based on the PD dynamics. In this paper, by modeling the interaction between the neurons in populations of the thalamic, the network-level modulation of thalamic is represented in a standard canonical form as a multi-input multi-output (MIMO) nonlinear first-order system with uncertainty and external disturbances. A class of fast and robust MIMO adaptive fuzzy terminal sliding mode control (AFTSMC) has been presented for control of membrane potential of thalamic neuron populations through continuous adaptive DBS current applied to the thalamus. A fuzzy logic system (FLS) is used to estimate the unknown nonlinear dynamics of the model, and the weights of FLS are adjusted online to guarantee the convergence of FLS parameters to optimal values. The simulation results show that the proposed AFTSMC not only significantly produces lower tracking errors in comparison with the classical adaptive fuzzy sliding mode control (AFSMC), but also makes more robust and reliable outputs. The results suggest that the proposed AFTSMC provides a more robust and smooth control input which is highly desirable for hardware design and implementation.

Control synchronization of three eccentric rotors driven by motors in space considering adaptive fuzzy sliding mode control algorithm

In this paper, to obtain ideal dynamic behavior of vibration screen, control synchronization of three eccentric rotors (ERs) driven by motors in space vibration system is investigated by employing the adaptive fuzzy sliding mode control (AFSMC) algorithm. First, the dynamic model of the vibration system is established, and the motion equation of the electromechanical coupling system is derived via Lagrange equation. Then, the synchronization controllers of three ERs are systematically designed with the combination of AFSMC algorithm and master–slave control (MSC) structure, and the stability of the control system is verified by Lyapunov theory. Additionally, in comparison with the conventional sliding mode control (SMC) algorithm, the superiority and feasibility of the proposed AFSMC strategy are confirmed through numerical simulation in MATLAB/Simulink. Moreover, the robustness of the control system is demonstrated in the simulation via changing the motor velocities and structure parameters. The research result shows that the proposed adaptive strategy can effectively control the ERs to implement the desired in-phase synchronization motion, and the control method possesses commendable efficacy in reducing chattering, enhancing control accuracy, eliminating overshoot, and resisting parameter perturbation.

An ABC Optimized Adaptive Fuzzy Sliding Mode Control Strategy for Full Vehicle Active Suspension System

This work presents a Fuzzy based adaptive Sliding Mode Control scheme to deal with the control problem of full vehicle active suspension system and take into consideration the nonlinearities of the spring and damper, unmodeled dynamics as well as external disturbances. The control law of fuzzy-based Adaptive Sliding Mode Control scheme will update the parameters of fuzzy sliding mode control by using the stability analysis of Lyapunov criteria such that the convergence infinite time and the stability of the closed-loop is ensured. The proposed control scheme consists of four similar subsystems used for the four sides of the vehicle. The sub-control scheme contains two loops, the outer loop is built using a sliding mode controller with a fuzzy estimator to approximate and estimate the unknown parameters in the system. In the inner loop, a controller of type Fractional Order PID (FOPID) is utilized to create the required actuator force. All parameters in the four sub-control schemes are optimized utilizing Artificial Bee Colony (ABC) algorithm in order to improve the performance. The results indicate the effectiveness and good achievement of the proposed controller in providing the best ability to limit the vibration with good robustness properties in comparison with passive suspension system and using sliding mode control method. The controlled suspension system shows excellent results when it was tested with and without typical breaking and bending torques.

Adaptive Fuzzy Fault-Tolerant Control against Time-Varying Faults via a New Sliding Mode Observer Method

In this study, the problem of observer-based adaptive sliding mode control is discussed for nonlinear systems with sensor and actuator faults. The time-varying actuator degradation factor and external disturbance are considered in the system simultaneously. In this study, the original system is described as a new normal system by combining the state vector, sensor faults, and external disturbance into a new state vector. For the augmented system, a new sliding mode observer is designed, where a discontinuous term is introduced such that the effects of sensor and actuator faults and external disturbance will be eliminated. In addition, based on a tricky design of the observer, the time-varying actuator degradation factor term is developed in the error system. On the basis of the state estimation, an integral-type adaptive fuzzy sliding mode controller is constructed to ensure the stability of the closed-loop system. Finally, the effectiveness of the proposed control methods can be illustrated with a numerical example.

Investigation on the vortex-induced vibration active control of the riser in the “lock-in” region based on adaptive fuzzy sliding mode theory

A numerical simulation is proposed for the vortex-induced vibration of a riser with two degrees of freedom. The fluid–structure interaction is determined using FLUENT fluid-simulation software with the user-defined function embedded, giving the dynamic response and vortex shedding. The reduced velocity ranges between 1 and 13. An adaptive fuzzy sliding mode controller based on Simulink co-simulation was innovatively applied to the active control of vortex-induced vibration of the riser. The dynamic responses and wake patterns before and after the active control in the “lock-in” region were compared and analyzed, and showed that extremely severe vibration of the riser occurred when the reduced velocity was 5–6, when the vortex shedding frequency was close to the natural frequency and when reached the “lock-in” region. After applying the control, the vibration amplitude of the riser was suppressed with 95% efficiency in the cross-flow direction, and about 89% in the in-line direction. The adaptive fuzzy sliding mode controller demonstrated excellent control effects in both directions and is adaptable to the active control of vortex-induced vibration under different disturbance, mass/stiffness and velocity conditions.

Hybrid force/position control in workspace of robotic manipulator in uncertain environments based on adaptive fuzzy control

In this study, a novel hybrid force/position controller in workspace of robotic manipulator based on adaptive fuzzy control is developed to improve the controlling performance of force and position in contact with uncertain environments. The dynamic model of the robotic manipulator in joint space is converted to a dynamic model in workspace, which is consisted of the model of position-controlled subsystem and model of force-controlled subsystem. Furthermore, in the position-controlled subsystem, an adaptive fuzzy computed torque control is proposed by considering adaptive fuzzy control and conventional computed torque control (AFCTC), for compensating deviations caused by the presence of structured uncertainty and unstructured uncertainty. In the force-controlled subsystem, a fuzzy proportional integral control method (FPI) is proposed by fuzzy logic and conventional proportional integral method to improve the control performance. The asymptotic stability of the developed controller is proved by Lyapunov theorem. The simulation results show the preferable performance of the proposed controller (AFCTC-FPI) by comparison with adaptive fuzzy sliding mode control (AFSMC), adaptive network-based fuzzy inference system with proportion differential and integral (ANFIS-PD+I), computed torque control-proportion integral (CTC-PI), fuzzy proportional integral derivative (FPID), and proportional integral derivative (PID) in uncertain environments. Furthermore, in the experimental study, average position error with AFCTC-FPI is decreased by 87.14 % than that with PID, meanwhile, range of interaction force with AFCTC-FPI is reduced by 70.31% than that with PID. In summary, the experimental results also show the superior control accuracy of the proposed controller in real environment.

Output feedback-based adaptive fuzzy sliding mode control for seismic response reduction of base-isolated buildings

In this research, a model-free control strategy for vibration suppression of base-isolated structures, which are excited by an earthquake, is investigated. For this purpose, the semi-active base isolated system including a magnetorheological (MR) damper is utilized. In this way, the displacement of the base isolator is measured by a linear variable differential transformer (LVDT). Accordingly, an adaptive fuzzy sliding mode observer is implemented to estimate the vector of state tracking error. This estimated vector is then employed by an adaptive fuzzy sliding mode control scheme to calculate the input voltage of the MR damper. The efficiency of the implemented model-free control strategy is studied by some numerical simulations. The advantages of the proposed controller can be summarized as its flexibility and robustness against disturbance rejection and model uncertainty.

Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator.

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.

Active disturbance rejection control for optoelectronic stabilized platform based on adaptive fuzzy sliding mode control

This paper proposes a method that combines active disturbance rejection control (ADRC) and adaptive fuzzy sliding mode control (AFSMC), which is beneficial for the optoelectronic platform to enhance the target tracking capability. A servo system model based on the LuGre friction is first established. The AFSMC controller estimates the unknown part of the platform, and the fuzzy approximator can reduce chattering. Then, an ADRC controller based on Back-Propagation neural network tuning is designed and compared with the empirical method, which makes the system’s tracking accuracy higher. Lyapunov’s theorem and Barbara’s lemma are forceful methods to prove engineering stability. Simulations illustrate that the influence of external disturbances on the optoelectronic platform can be suppressed, thereby enhancing the disturbance isolation of the controller.

Adaptive fuzzy sliding mode controller design for PMLSM position control

We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.

Real-time analysis of adaptive fuzzy predictive controller for chaotification under varying payload and noise conditions

In this paper, an adaptive fuzzy generalized predictive controller is proposed for the anti-chaos control where the system dynamics are assumed to be unknown or uncertain in large. In the proposed mechanism, the control signal is generated by an input constraint generalized predictive controller to track the Duffing oscillator which is used as a chaotic reference system. The adaptive fuzzy system, as a real plant model, is employed to obtain prediction through the receding horizon. In order to show the capability of the proposed mechanism, three different payloads and noise cases are studied in the real-time control experiments of flexible-joint manipulator and tracking results are compared with three more non-model-based controllers which are conventional adaptive fuzzy control, indirect adaptive fuzzy sliding mode controller and proportional–integrative–derivative control. Results show that the proposed mechanism has better control performance than the others.

Fuzzy adaptive sliding mode controller for electrically driven wheeled mobile robot for trajectory tracking task

This paper presents a fuzzy adaptive sliding mode controller (FASMC) for electrically driven wheeled mobile robot for trajectory tracking task in the presence of uncertainties and disturbances. First, a finite-time kinematic controller is developed to compute the auxiliary velocity vector. Second, the FASMC, based on the nonlinear dynamic model of the robot and its actuators, is used to guarantee the stability and the convergence of the closed-loop system. Moreover, by employing the advantages of the fuzzy logic systems, the developed controller ensures the robustness of the system against dynamic disturbances and uncertainties, the smoothness of the computing voltage against the chattering phenomenon, and the optimal convergence of the velocity and posture errors. The Lyapunov theory is used to analyse the stability of this algorithm. In order to evaluate the effectiveness of the developed method, numerical simulations are done in the Mahlab/Simulink environment.

VIRTUAL MODELING AND CONTROLLING OF AN ELECTRO-HYDRAULIC ACTUATOR

Instead of experiment, this paper builds a virtual model of the electro-hydraulic actuator (EHA) thought an Amesim software to evaluate the control response. The main feature of the EHA is to use the closed-loop circuit to reduce the size and oil volume as well as to eliminate the pressure loss caused by the orifice area of the valves. Firstly, the mathematical model of the EHA is established. Secondly, based on this model, an adaptive fuzzy sliding mode controller (AFSMC) is then designed to control the accurate position of the piston. In this control strategy, the system parameters are considered unknown, and they are lumped into two unknown time varying functions. An approximate technique is used to express one of the unknown functions as a finite combination of the basis function. In addition, a fuzzy logic inference mechanism is utilized for realizing a hitting control law to remove completely the chattering problem from the conventional sliding mode control. Then, the Lyapunov stability theorem is utilized to find the adaptive laws for updating the coefficients in the approximate series and turning the fuzzy parameter.

Modelling an Industrial Robot and Its Impact on Productivity

This research aims to design an efficient algorithm leading to an improvement of productivity by posing a multi-objective optimization, in which both the time consumed to carry out scheduled tasks and the associated costs of the autonomous industrial system are minimized. The algorithm proposed models the kinematics and dynamics of the industrial robot, provides collision-free trajectories, allows to constrain the energy consumed and meets the physical characteristics of the robot (i.e., restriction on torque, jerks and power in all driving motors). Additionally, the trajectory tracking accuracy is improved using an adaptive fuzzy sliding mode control (AFSMC), which allows compensating for parametric uncertainties, bounded external disturbances and constraint uncertainties. Therefore, the system stability and robustness are enhanced; thus, overcoming some of the limitations of the traditional proportional-integral-derivative (PID) controllers. The trade-offs among the economic issues related to the assembly line and the optimal time trajectory of the desired motion are analyzed using Pareto fronts. The technique is tested in different examples for a six-degrees-of-freedom (DOF) robot system. Results have proved how the use of this methodology enhances the performance and reliability of assembly lines.

Fuzzy Adaptive Sliding Mode Control of Sandblasting and Rust Removal Parallel Robot

In order to overcome the trajectory tracking distortion caused by the friction mutations of the sandblasting and rust removal parallel robot based on the Stewart parallel mechanism, a fuzzy adaptive sliding mode control method that compensates for the friction mutations is designed. Firstly, the kinematics of the mechanism is analyzed by analytic method and the dynamic model of the Stewart parallel mechanism is established based on Lagrange method. Then, the robust adaptive term of the sliding mode is designed based on the sliding mode variable to estimate the uncertain term in real time, replacing the sliding Switching items of mode control to compensate for the influence of uncertain factors such as unmodeled dynamics, external disturbances and time-varying parameters, and to effectively suppress chattering of sliding mode control; Next, by designing fuzzy control based on sliding mode variable and sliding mode variable derivative, the dynamic adjustment of the sliding mode robust adaptive term gain is realized to compensate for the interference of the frictional force mutation, thereby eliminating the trajectory tracking distortion problem of the Stewart mechanism joint commutation. Finally, using MATLAB control method for numerical simulation and verify the effectiveness of the proposed fuzzy adaptive sliding mode control method to compensate for friction mutations.

Design of a Fuzzy Adaptive Sliding Mode Control System for MEMS Tunable Capacitors in Voltage Reference Applications

Micro electro mechanical system (MEMS) tunable capacitors (TC) are major elements in ac voltage reference sources (VRS). Physical parametric uncertainties, external electrostatic disturbance, and measurement noise malfunction their operation and ruin the preciseness of the VRS output voltage. Our objective problem is the design of a controller to cope with the mentioned parametric uncertainties, noise, and disturbance. Our applied method is the design and employment of a proportional integral fuzzy adaptive sliding mode controller (FASMC). Both terms of matched and unmatched uncertainties as well as external disturbance and measurement noise are all addressed in this article to generate a stable VRS output for the first time. Not only does this article contribute to the employment of a FASMC to enhance robustness in the drive of the capacitor, but also it benefits the reduction of the chattering effect due to fuzzy regulation in the switching term of the control law. Moreover, the automatic fuzzy adjustment in the controller, which is used for estimation of the coefficients in the sliding surface error dynamical equation, facilitates the specification of those coefficients, which can be time consuming in simulation affairs. Clarifying the importance of the proposed fuzzy adaptive sliding mode controller, this article reviews some previous controllers for MEMS TC in comparison with the proposed fuzzy controller, demonstrating its enhancement in comparison with previous schemes.

Disturbance observer based adaptive fuzzy sliding mode control: A dynamic sliding surface approach

In this paper, the disturbance observer based adaptive sliding mode control (SMC) approaches are established for Takagi–Sugeno fuzzy systems with unknown external disturbance. In order to counteract the disturbance actively, a novel memory-based fuzzy disturbance observer is proposed to estimate the unknown disturbance. In the absence of upper bound information of the disturbance estimation errors, by designing novel dynamic sliding surfaces and adaptive laws, both the state- and output-based adaptive fuzzy sliding mode controllers are designed without any constraints on input matrices, and the disturbance estimate is incorporated to achieve active disturbance rejection. It can be shown that, with the proposed control approaches, both the sliding variables and the adaptive parameter estimation errors will converge to the bounded region in fixed-time. Finally, numerical examples are presented to show the effectiveness of the proposed control approaches.