Adaptive Fuzzy Sliding Mode Control Research Articles

A new control method for leader–follower consensus problem of uncertain constrained nonlinear multi-agent systems

The design of Adaptive Fuzzy Sliding-Mode Control (AFSMC) is discussed in this study for the distributed leader–follower consensus problem of nonlinear uncertain multi-agent systems with input and state constraints. Avoiding the critical circumstances related to the violation of input and state constraints is an important issue in the design of consensus control systems. The limitations of existing adaptive consensus controllers developed for full-state constrained systems motivated the present novel method without restrictive structural assumptions. To convert the original problem to an unconstrained consensus control problem, the proposed control system uses a barrier function-based state transformation method and the input-state linearization methodology. As a result, each agent’s adaptive fuzzy sliding-mode control input, which consists of a fuzzy system and a robust term, is constructed based on the derived representation of the system dynamics. The fuzzy system is used to approximate the dynamic equations of the agent, its neighbors, and maybe the leader in each local feedback control law structure, and the robust term is designed to compensate for the fuzzy approximation error. The magnitudes of the robust terms and the output vectors of the fuzzy systems are also determined using the proposed adaptation laws. The second Lyapunov theorem and Barbalat’s lemma are used to verify the closed-loop system’s asymptotic stability. The effectuality of the proposed methodology is confirmed by numerical simulations for several Autonomous Underwater Vehicles (AUVs). Simulation results show that by means of the proposed AFSMC, the leader–follower consensus is achieved without state constraint violation and performance degradation.

Adaptive Fuzzy Sliding Mode Control and Dynamic Modeling of Flap Wheel Polishing Force Control System

Polishing force is one of the key process parameters in the polishing process of blisk blades, and its control accuracy will affect the surface quality and processing accuracy of the workpiece. The contact mechanism between the polishing surface and flap wheel was analyzed, and the calculation model of the polishing force and nonlinear dynamic model of the polishing force control system was established. Considering the influence of friction characteristics, parameter perturbation, and nonlinear dead zone on the control accuracy of the polishing force system, an adaptive fuzzy sliding mode controller (AFSMC) was designed. AFSMC uses a fuzzy system to adaptively approximate the nonlinear function terms in the sliding mode control law, adopts an exponential approach law in the switching control part of the sliding mode control (SMC), and designs the adaptive law for adjustable parameters in the fuzzy system based on the Lyapunov Theorem. Simulation and experimental results show that the designed AFSMC has a fast dynamic response, strong anti-interference ability, and high control accuracy, and it can reduce SMC high-frequency chatter. Polishing experiments show that compared with traditional PID, AFSMC can improve the form and position accuracy of the blade by 42% and reduce the surface roughness by 50%.

Adaptive fuzzy sliding mode control for underactuated systems with time-varying sensor faults

ABSTRACT The tracking control problem of a class of underactuated systems that have time-varying additive and multiplicative sensor faults is studied in this paper. A composite sliding mode surface is established, and the fuzzy logic system is used to estimate the unknown term in the system. The impact of sensor faults can be counteracted by using the proposed method, which also guarantees the tracking errors converge to zero and the signals in the closed-loop system are bounded. Furthermore, the usefulness of the designed control strategy is certified via a numerical simulation.

Adaptive fuzzy sliding mode control of uncertain nonholonomic wheeled mobile robot with external disturbance and actuator saturation

This paper developed a modified barrier function-based adaptive sliding mode control (MBFASMC) method to improve the tracking precision and robustness performance of the nonholonomic wheeled mobile robot (NWMR), which is subject to actuator saturation and external disturbance. Owing to the modified positive semi-definite barrier function (MPSDBF), the requirement for disturbance bound and the overestimation of control gain are eliminated. The nonsingular terminal sliding manifold and MPSDBF-based adaptive mechanism can guarantee that the sliding variables and the posture errors of NWMR converge to precisely predefined ultimate bounds in a finite time. In order to compensate for the adverse effect of input saturation, an auxiliary dynamics is proposed based on saturation error and used to construct the MBFASMC. Compared to the recent anti-saturation control methods, the demand for saturation level is released in the proposed control method. Moreover, a modified barrier function-based adaptive fuzzy sliding mode control (MBFAFSMC) is provided to relieve the control chattering problem. By Lyapunov analysis, it is validated that the proposed methods can ensure the pre-specified and finite-time convergence performance of the uncertain NWMR. The MBFASMC and MBFAFSMC are also applied to an actual NWMR experimental platform with artificial disturbances to verify the effectiveness of the proposed methods.

Stabilization control of rotary inverted pendulum using a novel EKF-based fuzzy adaptive sliding-mode controller: design and experimental validation

This article methodically develops an improved self-regulating fuzzy-adaptive Sliding Mode Controller (SMC) that strengthens the disturbance compensation capacity of the nonlinear rotary pendulum systems while effectively attenuating the chattering content and curbing the control energy consumption. The article contributes to augmenting the SMC with online adaptation tools to achieve the said objectives. It employs the conventional Gao’s power-rate reaching law as the baseline. The scaling gain and the power rate of the said reaching law are adaptively modulated via a pre-calibrated two-input state-error-driven fuzzy nonlinear function. Additionally, the sign function in the law is also replaced with an odd-symmetric nonlinear fuzzy function to address the hard limits imposed by the former. Finally, the membership functions of the fuzzy function are self-regulated using the Extended-Kalman-Filter to improve the compensator’s adaptability in handling the system’s rapidly changing control requirements under exogenous disturbances. The aforementioned propositions are verified by performing customized and reliable hardware-in-loop experiments on the Quanser single-link rotary pendulum platform. As compared to baseline SMC law, the proposed control procedure contributes a ∼45.2%, ∼48.5%, and ∼34.8% reduction in position-regulation errors, control energy consumption, and peak overshoots, respectively. The experimental assessment validates the proposed control system’s enhanced robustness and chattering-suppression capability.

Simulation Modeling of PMBLDC Motor Speed Control in Electric Propulsion of EV System Using Adaptive Non-Linear Fuzzy Sliding Mode Controller (ANF-SMC)

Simulation Modeling of PMBLDC Motor Speed Control in Electric Propulsion of EV System Using Adaptive Non-Linear Fuzzy Sliding Mode Controller (ANF-SMC)

Adaptive fuzzy sliding-mode control of under-actuated systems with unknown input gain function

This research addresses the design of an Adaptive Fuzzy Sliding-Mode Control (AFSMC) for a group of nonlinear under-actuated systems with unknown input gain functions. In the proposed approach, to provide a structure for controlling the under-actuated subsystem, the sliding manifold corresponding to the whole dynamic system is derived based on the backstepping technique. In addition, two separate fuzzy inference systems are utilized for approximating the system’s internal dynamics and the unknown input gain function. In order to derive the robust part of the controller, a novel method is proposed to define the upper bound of the uncertain term in the form of a state-dependent second-degree polynomial. It should be mentioned that the output vectors of the fuzzy systems and the coefficients of the robust part of the controller are obtained by the designed adaptation laws. The asymptotic stability analysis for the proposed AFSMC structure is provided by the second theorem of Lyapunov and Barbalat’s lemma. In the end, the AFSMC effectuality is confirmed by the depth control of a REMUS Autonomous Underwater Vehicle (AUV).

Slip ratio control based on adaptive fuzzy sliding mode for vehicle with an electromechanical brake system

The development of vehicle intelligence has driven the evolution of brake-by-wire systems, with electromechanical braking (EMB) emerging as a crucial development in intelligent vehicle braking. To enhance the braking safety of EMB-equipped vehicles, this paper proposes a slip ratio control method based on adaptive fuzzy sliding mode control (AFSMC) to more effectively achieve wheel slip ratio tracking during braking. The approach involves establishing the mathematical model of the planetary gear-type EMB system and the vehicle’s longitudinal dynamics model. Additionally, a hierarchical collaborative control strategy is introduced, where the bottom layer employs the EMB clamping force control algorithm based on cascaded proportional-integral (PI), and the top layer integrates AFSMC to regulate the slip ratio. The simulation results, validated using the joint simulation platform of Simulink and Carsim under various conditions, illustrate that the AFSMC, compared to the conventional sliding mode controller (SMC), attains more precise control of wheel slip ratio while mitigating the chattering phenomenon. These findings suggest the potential of AFSMC for practical engineering applications.

Adaptive fuzzy sliding mode control of magnetic levitation system based on Interval Type-2 Fuzzy Neural Network Identification with an Extended Kalman–Bucy filter

The electromagnet levitation control system (EMS) is the core component of the magnetic levitation train. However, its nature is characterized by an inherent instability and strongly nonlinear dynamic. Additionally, modeling uncertainties and numerous exogenous perturbations make the design of the controller even more difficult for this system. To overcome these limitations, a Fuzzy Sliding Mode Control based on Interval Type-2 Fuzzy Neural Network Identification (FSMCF2NN) is designed for the EMS system. The suggested controller has a unique feature that allows the sliding surface and controller effort to be designed by using model-free techniques. As a result, the drawbacks of the conventional SMC schemes, such as the chattering effect, requiring prior knowledge of the system’s uncertainties bounds, and selection of the appropriate control gains, are eliminated. Also, the FSMCF2NN method, since it is designed based on intelligence methods not only minimizes the accumulation of state errors, but also generates the optimum control efforts of the EMS system to ensure stability in different situations. Furthermore, since the measurement of all EMS states is not achievable in reality, a qualified observer is needed to estimate unmeasured states. For this purpose, the Extended Kalman-Bucy filter (EKBF) is selected because of its superiority in managing disturbances for nonlinear systems. Furthermore, to confirm the robustness of the FSMCF2NN scheme in different scenarios, the results of the controller based on Linear Matrix Inequality (LMI), Linear Quadratic Regulation (LQR) controller, and the conventional Sliding Mode Controller (SMC) are compared with the proposed controller. Analyzing the results of simulation demonstrates that maintaining the levitated object is achievable with the proposed control scheme, effectively. At the same time, it attempts to adequately compensate for the uncertainties and disturbances present in the EMS structure.

Adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm

The high-precision trajectory tracking and chatter-free performance of the manipulators are of great significance to the improvement of the automation level. Therefore, in view of the problems of chattering and tracking performance degradation of multi-joint manipulators caused by uncertain factors such as external disturbances and modeling errors, an adaptive fuzzy sliding mode control of the manipulator based on an improved super-twisting algorithm is proposed. First, fuzzy control is chosen to approximate the uncertainty of the manipulator model, which reduces the influence of unknown disturbances on the system, and then the adaptive law is utilized to automatically modify and improve the fuzzy rules to enhance the approximation capability; Second, an improved super-twisting algorithm is used on this basis to put the discontinuous term into the integral term to avoid chattering and eliminate the approximation error; Finally, a comparative simulation experiment is carried out with the 3-DOF manipulator as the research object. The experimental results show that the method in this paper improves the convergence speed by up to 69.74% and the tracking error accuracy by up to 85.11% over the fuzzy control based on adaptive reaching law with no chattering and little difference in control inputs, thus verifying the feasibility and superiority of the method.

Interval type-2 fuzzy sliding mode control for a cable-driven parallel robot with elastic cables using metaheuristic optimization methods

In this paper, a supervisory interval type-2 fuzzy adaptive sliding mode control scheme is addressed for cable robots. In the proposed control scheme, intelligent methods are combined with conventional sliding mode control to achieve optimal adjustment of control parameters. This approach ensures accurate tracking performance, despite the structural constraints of cable robots. These constraints include the production of purely tensile forces and the effects due to inherent elasticity, which will make the design of the controller challenging. For this purpose, the internal force concept is utilized to ensure the tension style of cables. Additionally, considering a compensator part in the proposed controller and applying the singular perturbation theorem, the vibration effects of elastic cables are handled, and stability is proved through the second Lyapunov method. Therefore, the desired performance of the control system will be guaranteed for the movements that stimulate the vibration modes of cables. An interval type-2 fuzzy logic controller is proposed to adjust the control gain, effectively reducing the chattering level. Moreover, a supervisory interval type-2 fuzzy logic control system is introduced to regulate the gains within the sliding surface. The Grasshopper Optimization Algorithm is employed to select the optimal parameters for the membership functions of the fuzzy system. The results of the conducted simulations show that the desired tracking performance is provided, despite different uncertain parameters and the structural constraints of the considered cable robot.

Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method

Carrier-based aircraft landing involves complex system engineering characterised by strong nonlinearity, significant coupling and susceptibility to environmental disturbances, and autonomous landing of carrier-based aircraft under fault states is even more challenging and riskier. To address the control-system problems of loss of efficiency and performance due to actuator faults and performance degradation due to various unknown disturbances, presented here is fault-tolerant control for carrier-based aircraft based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMFTC). First, three models are built (the carrier-based aircraft fault model, the carrier air wake model and the deck motion model), and the control framework of the autonomous landing control system is introduced. Next, a longitudinal and lateral flight channel controller comprising an adaptive fuzzy network, adaptive laws and a sliding-mode controller is designed using the AFSMFTC method. The adaptive fuzzy network implements fuzzy approximation for the sliding-mode switching terms to further offset errors induced by unknown disturbances, the adaptive laws compensate for actuator faults, and the sliding-mode controller ensures tracking of the overall flight path. Furthermore, the stability of the fault-tolerant method is demonstrated using the Lyapunov function. Finally, simulation and comparative experiments show that the proposed fault-tolerant method has outstanding control performance and strong fault-tolerant capability, thereby providing an effective and feasible solution for designing an autonomous landing system for carrier-based aircraft under fault states.

Adaptive hierarchical optimization control for electrohydraulic suspension with resistor-capacitor operator

The controller design for integrated electrohydraulic suspension with leaf spring is a complicated and challenging task, aiming to achieve optimal dynamic performance for heavy vehicles. Considering the hysteresis property of leaf spring, parameter uncertainties and nonlinearity, a novel hierarchical optimization control strategy including upper and bottom controllers is proposed to effectively suppress the vertical vibration of vehicle body. To describe the hysteresis property of leaf spring, a novel resistor-capacitor operator hysteresis model is identified by experiment and optimized by genetic algorithm. Based on hysteresis model, the filter adaptive backstepping control with sprung mass uncertainty is designed as upper controller to generate the desired active force for electrohydraulic actuator. Then, the projection adaptive fuzzy sliding mode control is designed as bottom controller for nonlinear actuator with time-varying fluid parameter to accurately track the required target force. Additionally, a stratified sampling algorithm is investigated to optimize the parameters of hierarchical control strategy for addressing multi-objective optimization issue of electrohydraulic suspension with leaf spring. Finally, the results show that the proposed method has better robustness and effectiveness, and it can ensure ride comfort, safety and optimal performance.

Design of an Adaptive Fuzzy Sliding Mode Control with Neuro-Fuzzy system for control of a differential drive wheeled mobile robot

Design of an Adaptive Fuzzy Sliding Mode Control with Neuro-Fuzzy system for control of a differential drive wheeled mobile robot

Adaptive Super-Twisting Sliding Mode Control of Underwater Mechanical Leg with Extended State Observer

Underwater manipulation is one of the most significant functions of the deep-sea crawling and swimming robot (DCSR), which relies on the high-accuracy control of the body posture. As the actuator of body posture control, the position control performance of the underwater mechanical leg (UWML) thus determines the performance of the underwater manipulation. An adaptive super-twisting sliding mode control method based on the extended state observer (ASTSMC-ESO) is proposed to enhance the position control performance of the UWML by taking into account the system’s inherent nonlinear dynamics, uncertainties, and the external disturbances from hydrodynamics, dynamic seal resistance, and compensation oil viscous resistance. This newly designed controller incorporates sliding mode (SMC) feedback control with feedforward compensation of the system uncertainties estimated by the ESO, and the external disturbances of the hydrodynamics by fitting the parameters, the dynamic seal resistance, and the compensation oil viscous resistance to the tested results. Additionally, an adaptive super-twisting algorithm (AST) with integral action is introduced to eliminate the SMC’s chattering phenomenon and reduce the system’s steady-state error. The stability of the proposed controller is proved via the Lyapunov method, and the effectiveness is verified via simulation and comparative experimental studies with SMC and the adaptive fuzzy sliding mode control method (AFSMC).

Adaptive sliding mode controller based on fuzzy rules for a typical excavator electro-hydraulic position control system

In order to improve the trajectory tracking accuracy and robustness of a typical heavy electro-hydraulic position system, a fuzzy adaptive sliding mode controller is proposed. A potential-like function is introduced to design a new sliding surface with non-linear integral term. An adaptive controller is designed to approximate the equivalent controller in sliding mode control. Stability of the controller is demonstrated by Lyapunov method, and the chattering phenomenon caused by the nonlinear switching term is reduced by using the fuzzy switching method, 25 fuzzy rules are given to adjust the sliding mode switching controller. Simulations with sinusoidal, ramp and step signals, and experiments with leveling operation at different speeds are carried out. The proposed controller can follow the reference trajectory quickly and smoothly, and has certain anti-interference ability. Compared with the traditional sliding mode controller, the proposed controller has small tracking error, fast response and good tracking performances.

Integration of sEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System

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.

Development of an Adaptive Fuzzy Sliding Mode Controller of an Electrohydraulic Actuator Based on a Virtual Prototyping

The EHA (electro hydraulic actuator) has a notable advantage over conventional hydraulic actuators as it uses a closed-loop circuit, reducing the size and volume of oil, and eliminates pressure losses caused by valve orifices. However, accurate control performance of EHA is difficult to achieve using a traditional PID (proportional integral derivative) controller due to the strongly nonlinear, time-varying, and unknown dynamics of the system. Hence this paper seeks to address this problem by proposing a design of an intelligent controller for the EHA. The proposed adaptive fuzzy sliding mode controller (AFSMC) is developed as a hybrid of the adaptive, fuzzy logic, and sliding mode algorithms. To reduce costs and time, a virtual prototype approach is also proposed instead of experimentations to evaluate the performance of the proposed controller. The virtual model of the EHA is built in Amesime software, and then embedded into Matlab/Simulink where the AFSMC is developed and tested to obtain the position responses of the EHA. The results show that the AFSMC is highly successful and more efficient than the traditional PID at controlling the position of the piston accurately.

Adaptive fuzzy sliding mode control of an actuator powered by two opposing pneumatic artificial muscles

Pneumatic artificial muscle (PAM) is a potential actuator in human–robot interaction systems, especially rehabilitation systems. However, PAM is a nonlinear actuator with uncertainty and a considerable delay in characteristics, making control challenging. This study presents a discrete-time sliding mode control approach combined with the adaptive fuzzy algorithm (AFSMC) to deal with the unknown disturbance of the PAM-based actuator. The developed fuzzy logic system has parameter vectors of the component rules that are automatically updated by an adaptive law. Consequently, the developed fuzzy logic system can reasonably approximate the system disturbance. When operating the PAM-based system in multi-scenario studies, experimental results confirm the efficiency of the proposed strategy.

Chattering suppression and hydrodynamic disturbance estimation of underwater manipulators using adaptive fuzzy sliding mode control

Robotic manipulators are nowadays widely used in various underwater scenarios, but their motion control remains a challenging task due to hydrodynamic effects. This article proposes a novel adaptive fuzzy sliding mode control (AFSMC) strategy for precise and robust control of underwater manipulators. To simulate the movement of the robotic manipulators in the underwater environment, the Unified Robot Description Format (URDF) file of a custom-designed electric underwater manipulator is imported into the Simscape Multibody and the hydrodynamic disturbance is modeled according to Morison’s equation. Furthermore, the proposed control strategy takes advantage of the universal approximation capability of fuzzy systems to avoid chattering and observe disturbances by adjusting the control gains of classical sliding mode control (CSMC). And adaptive laws are designed to update the parameters of the fuzzy systems. The strong friction caused by the seal is also compensated by actual test data. In the simulation experiments, a special environment of water flow and variable loads is considered. The results demonstrate that the AFSMC strategy can achieve high precision and strong robustness against disturbances for trajectory tracking. More importantly, the chattering caused by CSMC can be eliminated and the hydrodynamic disturbance can be estimated with high precision through the proposed control strategy.