Adaptive Fuzzy Sliding Mode Control Research Articles

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool.

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non-linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti-angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti-angiogenic therapy is more efficient than single chemotherapy. Two different non-linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti-angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed-loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.

Observer-based Fuzzy Adaptive Sliding Mode Control of IOCV with Actuator Faults and Saturation

Observer-based Fuzzy Adaptive Sliding Mode Control of IOCV with Actuator Faults and Saturation

Adaptive depth tracking of underwater vehicles considering actuator saturation: Theory, simulation and experiment

This paper investigates underwater vehicles' saturated depth tracking problem via an adaptive fuzzy sliding mode controller (AFSMC) with an anti-saturation auxiliary compensator (ASAC). To suppress the chattering effect due to the discontinuity of the sliding mode control, a saturation function boundary layer is first applied in the AFSMC. Subsequently, a model-free fuzzy approximation system with a sliding mode surface as its input is designed for complex uncertainty compensation. Thereafter, a two-layer cascade ASAC is then attached to the controller to reject inherent actuator saturation efficiently. Based on the Lyapunov theorem and Barbalat's lemma, the stability of the entire closed-loop system is proved. Finally, the outperformance of the proposed depth tracking law is verified by numerical simulations and water tank experiments. Namely, there are improvements in tracking accuracy and energy consumption compared to the proportional integral differential algorithm.

Adaptive fuzzy sliding mode control for high‐speed train using multi‐body dynamics model

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.

Adaptive Sliding Mode Controller for Robotic Manipulator Tracking Control with Fuzzy Design

This paper introduces an adaptive sliding mode controller design based on fuzzy compensation for efficient robotic manipulator tracking control. This work introduces design of Adaptive Fuzzy Controller based on sliding control principles for Robotic Manipulators. In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. The fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real-time by adaptive laws, to approximate unknown nonlinearities. This makes it easier to direct the nonlinear system's output to follow a specific trajectory. An adaptive control algorithm based on the adaptive fuzzy model is created using the Lyapunov approach. Both the chattering and the stable performance are assured.

Pareto optimal design of a fuzzy adaptive sliding mode controller for a three-link model of a biped robot via the multi-objective improved team game algorithm

Pareto optimal design of a fuzzy adaptive sliding mode controller for a three-link model of a biped robot via the multi-objective improved team game algorithm

An Adaptive Fuzzy Sliding Mode Control Design For Multiuse Grid Interconnected Inverter to intensify power quality in microgrid

: This paper presents a vigorous control design and a robust controller for a Multifunctional grid interconnected inverter [MFGII] beneath disturbed load conditions. This MFGII controller algorithm uses DQ theory to assess the unacceptable parts of grid current such as harmonics, and required load reactive current. This decoupled method of control design makes normal Grid interconnected inverter to MFGII and also helps in reducing the computational burden which further makes the system more robust. Furthermore, Adaptive Fuzzy sliding mode control (AFSMC) algorithm prepares the system sturdy and effective in improving the dynamic performance during uncertainties. Henceforward, settling time and overshoot are reduced with fast dynamic response. The MFGII infuses the RES to grid at PCC which in addition to Real power injection also provides reactive power compensation, balance the load and mitigate harmonics. The results obtained from FOSMC are compared with AFSMC under the variation of solar irradiation and load.

Supervisory adaptive fuzzy sliding mode control with optimal Jaya based fuzzy PID sliding surface for a planer cable robot

Supervisory adaptive fuzzy sliding mode control with optimal Jaya based fuzzy PID sliding surface for a planer cable robot

Improving control quality of PMSM drive systems based on adaptive fuzzy sliding control method

In this paper, a solution to improve the precision in speed control for permanent magnet synchronous motors (PMSM) based on fuzzy adaptive sliding mode controller (FASMC) is proposed. In order to tackle the nonlinear tracking problem, continuously switching topologies are embedded. The designed algorithm and the closed electric drive system stability is examined by employing corresponding Lyapunov candidate functions. The results are numerically simulated and experimentally verified in the environment of MATLAB-Simulink, control Desk with dSPACE 1104 card, proving the applicability of the control algorithm which not only works well in simulations but also in practice for possible industrial traction drive applications.

A vision-aided fuzzy adaptive sliding mode controller for autonomous landing of a nonlinear model helicopter on a moving marine platform

PurposeA vision-assisted fuzzy adaptive sliding mode controller is presented in this research and implemented on a nonlinear helicopter model, which is about to land on a moving ship. Stabilization of the dynamics and tracking the landing path are required, at the same time. This study aims to take one step closer to fully autonomous landing, which is a growing trend.Design/methodology/approachAn integrated guidance and control is considered for the model helicopter. A fuzzy logic is designed to adaptively choose the best control parameters for the sliding mode controller and solve the challenge of parameter tuning. A self-organizing matrix consisting of fuzzy sliding mode parameters is formed instead of a single parameter with the goal of enhancing controller tracking capability. A simple, precise and fast image recognition system based on OpenCV is used to detect the proper point for descending without getting any special data from the ship and by only using a general “H” sign.FindingsThe problem is simulated under intense disturbances, while the approach and landing performances are acceptable. Controller performance is compared and validated. Simulation results show the robustness, agility, stability and outperformance of the proposed controller.Originality/valueThe novelty of this paper is the designed procedure for using a simple image recognition system in the process of autonomous ship-landing, which does not use any special data sent from the ship. Besides, an improved nonlinear controller is designed for integrated guidance and control in this specific application.

Enhancing Flight Envelope for a Nonlinear Aeroelastic Wing-Section Using Adaptive Fuzzy Sliding Mode Control Law

This paper presents an adaptive fuzzy sliding mode controller (AFSMC) for the control of a nonlinear aeroelastic wing section. The proposed controller estimates both the model dynamics of the system and the switching control of the sliding mode. Furthermore, the designed controller’s strategy is to enhance quality and ensure flight safety by providing additional damping to the already stable system and increasing the flight speed beyond the open-loop flutter speed. The aeroelastic system describes the plunge and pitch motions of the aircraft wing-section equipped with leading and trailing edge control surfaces. Additionally, the selected two-degree-of-freedom model includes structural stiffness nonlinearities and quasi-steady aerodynamics. The simulation results show the effectiveness of the proposed controller toward suppressing flutter and LCOs and reducing the vibrational level in the subcritical flight speed range. Additionally, the designed control strategy effectively controls and derives the state trajectories to the origin despite uncertainties and gust loads.

Observer-based lateral stability adaptive control method for distributed drive electric vehicles

Aiming at the lateral instability of distributed drive electric vehicles (DDEV) under extreme conditions, a nonlinear adaptive lateral stability control strategy is designed based on Cubature Kalman filter (CKF) observer and adaptive fuzzy sliding mode control (FSMC) theory. To solve the problem that it is difficult to measure some parameters of road and vehicle state directly, a vehicle state observer based on a double CKF algorithm is designed. The joint sliding mode surface is designed by the deviation between the ideal and observed values of yaw rate and sideslip angle. The SMC algorithm is combined with the adaptive fuzzy algorithm to design an adaptive FSMC to accurately obtain the additional yaw moment of the vehicle. Simulation verification is carried out based on CarSim and MATLAB/Simulink simulation platforms. The results show that the proposed lateral stability adaptive method can effectively improve the robustness of the DYC system.

Adaptive fuzzy sliding-mode consensus control of nonlinear under-actuated agents in a near-optimal reinforcement learning framework

This study presents a new framework for merging the Adaptive Fuzzy Sliding-Mode Control (AFSMC) with an off-policy Reinforcement Learning (RL) algorithm to control nonlinear under-actuated agents. In particular, a near-optimal leader-follower consensus is considered, and a new method is proposed using the framework of graphical games. In the proposed technique, the sliding variables’ coefficients are considered adaptively tuned policies to achieve an optimal compromise between the satisfactory tracking performance and the allowable control efforts. Contrary to the conventional off-policy RL algorithms for consensus control of multi-agent systems, the proposed method does not require partial knowledge of the system dynamics to initialize the RL process. Furthermore, an actor-critic fuzzy methodology is employed to approximate optimal policies using the measured input/output data. Therefore, using the tuned sliding vector, the control input for each agent is generated which includes a fuzzy term, a robust term, and a saturation compensating term. In particular, the fuzzy system approximates a nonlinear function, and the robust part of the input compensates for any possible mismatches. Furthermore, the saturation compensating gain prevents instability due to any possible actuator saturation. Based on the local sliding variables, the fuzzy singletons, the bounds of the approximation errors, and the compensating gains are adaptively tuned. Closed-loop asymptotic stability is proved using the second Lyapunov theorem and Barbalat's lemma. The method's efficacy is verified by consensus control of multiple REMUS AUVs in the vertical plane.

A new method for control of nonlinear state-dependent switched systems

In this paper, a new method for the control of input-affine nonlinear switched systems is introduced. The system switching conditions are assumed to be state-dependent, rather than the simpler input-dependent case. The main contribution of this research is that the effects of switched dynamics are interpreted as a model uncertainty bounded within a polynomial of states norms, with unknown coefficients. In order to prevent extra conservativeness, coefficients are tuned adaptively, so that a minimal state-varying bound could be achieved. This is unlike the conventional sliding mode control (SMC) scheme, where the existence of a constant and usually large upper bound must be presumed. To address the challenge of coping with such a new concept of uncertainty, an extended form of the original adaptive fuzzy sliding mode control scheme is proposed. Adaptation laws are used to tune a fuzzy controller and also real-time estimation of the instantaneous bound of uncertainties. Closed-loop stability is guaranteed by proposing a group of multiple Lyapunov functions (MLF) with tunable parameters. Except for the mild condition that the largest difference between the magnitudes of the sub-manifolds of the switched system is bounded by a polynomial of states with uncertain coefficients, the proposed method has the distinct advantage that no information about the dynamic equations or switching conditions is required in the control design stage. The proposed method is applied to the two challenging case studies, depicting the outstanding effectiveness of the method.

Yaw Stability Research of the Distributed Drive Electric Bus by Adaptive Fuzzy Sliding Mode Control

The direct yaw moment control can effectively enhance the yaw stability of the vehicle under extreme conditions, which has become one of the essential technologies for the distributed driving electric bus. Due to the features of a large mass and high center of gravity of the bus, lateral instability is more likely to occur under extreme driving conditions. To reduce the uncertainty and interference in the yaw movement process of the bus, this paper targets the instability caused by the coupling problem between the sideslip angle and yaw rate. An adaptive fuzzy sliding mode control is proposed to execute direct yaw moment control. The weight coefficient of the sideslip angle and the yaw rate is adjusted via fuzzy control in real time. The optimal direct yaw moment is finally obtained. A distribution method based on the vertical load proportion is adopted for the allocation of four motors’ torque. Under three typical working conditions, a joint simulation test was carried out. The simulation results demonstrate that the raised method decreases the amplitude of the sideslip angle by 20.90%, 12.75%, and 23.67% and the yaw rate is 8.62%, 6.89%, and 9.28%, respectively. The chattering and sudden changes in the additional yaw moment are also lessened. The control strategy can realize the control target, which effectively strengthens the yaw stability of the bus.

An sEMG based adaptive method for human-exoskeleton collaboration in variable walking environments

In this paper, a novel long short memory network – adaptive robust iterative learning control (LSTM-ARILC) framework is proposed to achieve accurate continuous motion estimation and adaptive following control for the exoskeleton robot, which is applied to offer assistance to human walking in variable environments. The LSTM network is established to estimate the human lower limb motion through the processed surface Electromyography (sEMG) signals, and the human-exoskeleton coupling fuzzy dynamic model is further developed. Then the closed-loop ARILC controller is designed to compensate the estimated errors and realize the adaptive and robust following control, and its asymptotic stability is rigorously proved via Lyapunov theory. The performance of the proposed method is evaluated with numerical experiments and compared with adaptive PID controller and adaptive fuzzy sliding mode controller (SMC). The maximum following errors of ARILC after 6 iterations can be reduced by more than 99% compared to the initial iteration, and the controller is quite less sensitive to the environment changes than the other two controllers, which proves that the proposed control framework is much more robust and effective in variable walking environments.

Disturbance-Observer-Based Adaptive Fuzzy Control for Islanded Distributed Energy Resource Systems

With the aim to improve the antidisturbance ability of the islanded distributed energy resource (DER) systems, a disturbance-observer-based adaptive fuzzy sliding mode control (DAFSC) voltage controller is designed based on indirect vector control, which implements the voltage tracking and improves the self-regulation ability of the islanded DER systems. Firstly, the circuit diagram and the mathematical model of the DER system are presented. Then, the second-order sliding mode differentiator is designed to solve the problem of calculation expansion in the backstepping control method. To solve the influence of lumped disturbance on the system, a disturbance-observer is proposed to observe the unknown disturbance and compensate the controller feed-forward. Moreover, fuzzy control is proposed to reduce the dependence of the control effect on model accuracy. Finally, the stability of the controller is verified by Lyapunov stability theory, and the hardware in the loop results is given to verify that the control effect of the proposed DAFC controller has better dynamic performance compared with proportion-integral (PI) and the backstepping control strategy.

Design of a Fixed-Wing UAV Controller Combined Fuzzy Adaptive Method and Sliding Mode Control

To overcome the complexity of the coupled nonlinear model of a fixed-wing UAV system and the uncertainty caused by a large number of interference factors, a control algorithm combining fuzzy adaptive control and sliding mode variable structure control was proposed. The controller algorithm mainly relies on the sliding mode variable structure control method to solve the control problem of the strongly coupled complex nonlinear system. Based on sliding mode control, a fuzzy adaptive method is introduced to reduce the chattering problem of the traditional sliding mode control, and the uncertain parameters and unknown functions caused by external disturbances are approximated by this method. In this study, two types of fuzzy adaptive sliding mode controller were designed according to the different object ranges of the fuzzy adaptive algorithm. In addition, the stability of the controllers was verified using the Lyapunov method. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed controllers by comparing with the traditional sliding mode controller.

Flutter Suppression of Wind Turbine Blade based on Adaptive Fuzzy Sliding Mode Control

Aiming at the divergent unstable vibration of wind turbine blades, an adaptive fuzzy sliding mode control algorithm based on sliding mode control is proposed to solve the classic flutter fracture failure problem of blades. The structural model is a typical blade section model based on spring-mass-damper, and the aeroelastic equation is obtained by combining the steady aerodynamic model suitable for pure pitch motion. The adaptive fuzzy sliding mode controller gives the pitch signal, and the second-order pitch actuator performs the pitch action to suppress flutter. The saturation function with variable boundary layer thickness is used instead of symbolic function, and the approach rate parameter is adjusted appropriately by fuzzy rules, so as to realize variable-parameter adaptive fuzzy sliding mode control. Simulation results show that, compared with the traditional sliding mode controller, the proposed control algorithm can not only suppress the classical flutter, but also effectively reduce the amplitude and frequency of chattering, and has good robustness.

Dynamic Modeling and Vibration Suppression for Two-Link Underwater Flexible Manipulators

This paper proposes a composite controller (CC) to improve the accuracy of trajectory tracking and suppress the vibration of two-link underwater flexible manipulators. A dynamic model of the flexible manipulators considering hydrodynamic force is established by combining the Lagrange equation and Morison formula. Then, the dynamic model is divided into a flexible dynamic subsystem and rigid dynamic subsystem, and a decomposed dynamic control strategy is presented for the two subsystems. In particular, an adaptive fuzzy sliding mode control scheme (AFSMC) with good robustness to compensate for uncertain factors is designed to track the joint trajectory and suppress vibration. Next, the trajectory tracking control of two-link underwater flexible manipulators is simulated to investigate the performance of the framework. The results show that the hydrodynamic force and flexible deformation markedly affect the input torque of the joint, and the traditional sliding mode controller (SMC) is superior to proportional integral derivative (PID) control in managing hydrodynamic force disturbance and inferior in suppressing flexible vibration. The proposed composite controller based on adaptive fuzzy sliding mode control CC(AFSMC) is more effective in restraining the vibration of flexible manipulators and resisting hydrodynamic force disturbance than PID and CC(SMC).