Sliding Mode Neural Network Research Articles

Nonlinear hysteresis system control based on sliding mode neural network and observer

In this paper, a sliding mode neural network controller with observer is presented and employed in a nonlinear hysteresis system to eliminate the system’s unknown hysteresis and uncertainties. First, a sliding mode controller is proposed for trajectory tracking, which simplifies the computational complexity and ensures robustness. Second, radial basis function neural network is applied for approximating unknown nonlinear function in the control system. Then, an observer is utilized to estimate and observe the hysteresis nonlinearity and cancel the effect of the hysteresis phenomenon. Based on the proposed control scheme, the influence of hysteresis is suppressed, and the system has robust stability and achieves high output tracking performance. Finally, theoretical analysis and numerical simulations confirm the effectiveness of the designed control law.

Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Tracking Control Based on Model Predictive and Adaptive Neural Network Sliding Mode of Tiltrotor UAV

Design and Performance Analysis of a Neural Network Sliding Mode based Hybrid Controlled Islanded Micro-Grid

The micro-grid fed shunt voltage source converter, also known as a smart Artificial neural network and sliding mode controller (ANN-SMC) based hybrid system, is developed during this research for solar system, wind and battery storage connected shunt filter. Here, phased locked loop utilization is avoided by the self-tuning filter (STF). Move over, apart from producing phase synchronization, STF also distinguishes between the fundamental and harmonic components. Total harmonic distortion (THD) reduction and steady voltage across dc link during load variations with short settling times under both grid and island settings are the key goals of the recommended approach. Four scenarios with various combinations of loads, solar radiation, and wind speeds under islanding conditions are used to illustrate the effectiveness of the study.

A robust voltage control of dual active full bridge converter based on RBF neural network sliding mode control with reduced order modeling approach

This paper presents a control method of dual active full bridge (DAB) DC/DC converter based on neural network Sliding mode control under the reduced order modeling method, which is used to enhance the output voltage regulation and improve the system robustness. Sliding mode control (SMC) is famous for its robustness and improving the dynamic performance of nonlinear systems. However, it includes drawbacks such as complex modeling, chattering, and decreased tracking performance. Therefore, through the method of reduced order modeling, the Radial Basis Function neural network algorithm is selected to modify and design the traditional sliding mode variable structure controller, completing the design of neural network approximation terms and adaptive laws. The proposed reduced order modeling and RBF(Radial Basis Function Neural Network)-SMC scheme simplifies the dual active full bridge modeling process, completes the parameter approximation of the sliding mode controller, and eliminates the chattering problem. Firstly, through simulation and comparative analysis of commonly used modeling methods for dual active full bridge, a reduced order modeling method was adopted to simplify the design process. Then, by ingeniously designing the sliding mode surface and Radial Basis Function control law, and using neural network to modify the Sliding mode control technology, the chattering problem, load disturbance and voltage fluctuation influence of the sliding mode controller are improved. The stability of the control method is proved by Lyapunov stability theory. Finally, the proposed RBF-SMC method is compared with PI linear control and classical Sliding mode control methods through simulation and experiments to verify the effectiveness of the proposed control method.

Self-Feedback Neural Network Sliding Mode Control With Extended State Observer for Active Power Filter

In this paper, a fuzzy neural network adaptive sliding mode control with a self-feedback recursion (FNNASMCSFR) based linear extended state observer (LESO) is proposed for a single-phase active power filter (APF), where the adaptive sliding mode controller is designed to improve the response and accuracy of current compensation and reference current tracking. The LESO is designed to estimate the actual APF system dynamics which includes the parameter perturbation and external disturbance. Moreover, the fuzzy neural network with self-feedback recursion is adopted to mimic the switching control gain of ASMC, which combines the output values of neurons at the current time and the previous time, to achieve better dynamic approximation effect and prevent sudden changes. Simulation and hardware experiments verify the introduced method is a viable control solution in harmonics suppression and current control.

Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems

A class of heterogeneous second-order multi-agent consensus problems is studied, in which an event-triggered method is used to improve the feasibility of the control protocol. The sliding mode control method is used to achieve the robustness of the system. A special type of general radial basis function neural network is applied to estimate the uncertainties. The event-triggered mechanism is introduced to reduce the update frequency of the controller and the communication frequency among the agents. Zeno behavior is avoided by ensuring a lower bound between two adjacent trigger instants. Finally, the simulation results are provided to demonstrate that the time evolution of consensus errors eventually approaches zero. The consensus of multi-agent systems is achieved.

Gated Recurrent Fuzzy Neural Network Sliding Mode Control of a Micro Gyroscope

This paper proposes a non-singular fast terminal sliding mode control (NFTSMC) method for micro gyroscopes with unknown uncertainty based on gated recurrent fuzzy neural networks (GRFNNs). First, taking advantage of non-singular fast terminal sliding control, a sliding hyperplane is designed with a nonlinear function to ensure that the tracking error of the system converges to zero within a specified finite time. Then, the unknown model parameters of the micro gyroscope are estimated using a GRFNN. Since the GRFNN can adaptively adjust the base width, center vector, gated recurrent unit parameters, and outer gains, it can achieve accurate approximation to unknown models, enhancing the robustness and accuracy. In addition, due to the introduction of gated recurrent units, The GRFNN can effectively utilize the previous data and avoid the problem of gradient disappearance. The comparison of the simulation results with traditional neural sliding mode control shows that the proposed method can achieve better tracking performance and more accurate estimation of unknown models.

Humanoid control of lower limb exoskeleton robot based on human gait data with sliding mode neural network

Lower limb exoskeleton robots offer an effective treatment for patients with lower extremity dysfunction. In order to improve the rehabilitation training effect based on the human motion mechanism, this paper proposes a humanoid sliding mode neural network controller based on the human gait. A humanoid model is constructed based on the human mechanism, and the parameterised gait trajectory is used as target to design the humanoid control system for robots. Considering the imprecision of the robot dynamics model, the neural network is adopted to compensate for the uncertain part of the model and improve the model accuracy. Moreover, the sliding mode control in the system improves the response speed, tracking performance, and stability of the control system. The Lyapunov stability analysis proves the stability of the control system theoretically. Meanwhile, an evaluation method using the similarity function is improved based on joint angle, velocity, and acceleration to evaluate the comfort of humans in rehabilitation training more reasonably. Finally, to verify the effectiveness of the proposed method, simulations are carried out based on experimental data. The results show that the control system could accurately track the target trajectory, of which the robot is highly similar to the human.

RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to Offshore Cranes.

This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property. Third, based on the RBF neural network approach, an adaptive control law is designed to ensure the finite-time existence of sliding motion in the face of unknown nonlinearity. Fourth, feasible easy-checking linear matrix inequality conditions are developed to analyze passification performance of the resulting sliding motion. Finally, a simulation study is provided to confirm the validity of the proposed method.

Extended State Observer Based Interval Type-2 Fuzzy Neural Network Sliding Mode Control With Its Application in Active Power Filter

Aiming at the difficulties in modeling the active power filter (APF) system and the susceptibility to parameter perturbation and external disturbance, this article proposes a new adaptive sliding mode controller with using a nonlinear extended state observer (NESO) based on an interval type-2 fuzzy neural network (IT2FNN) structure. The IT2FNN is designed to estimate the unknown control coefficient, so that the NESO can estimate the state of the system and the total disturbance including the unmodeled dynamics and external disturbances, and then perform feedforward compensation to achieve active disturbance rejection. Simulation and experimental results show that the proposed control strategy is effective in the harmonic suppression of the APF system and has better steady-state and dynamic performance compared with the existing methods.

Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems

In this article, a sliding mode control (SMC) is proposed on the basis of an adaptive neural network (NN) for a class of Single-Input–Single-Output (SISO) nonlinear systems containing unknown dynamic functions. Since the control objective is to steer the system states to track the given reference signals, the SMC method is considered by employing the adaptive neural network (NN) strategy for dealing with the unknown dynamic problem. In order to compress the impaction coming from chattering phenomenon (which inherently exists in most SMC methods because of the discontinuous switching term), the boundary layer technique is considered. The basic design idea is to introduce a continuous proportional function to replace the discontinuous switching control term inside the boundary layer so that the chattering can be effectively alleviated. Finally, both Lyapunov theoretical analysis and computer numerical simulation are used to verify the effectiveness of the proposed SMC method.

Vibration control of a translational coupled double flexible beam system using sliding mode neural network fuzzy control

A sliding mode neural network fuzzy control (SMNNFC) method is investigated to suppress the vibration of a translational coupled double flexible beam system, equipped with an AC servomotor and several piezoelectric actuators. Adjacent beams and slider moving frame are connected at the tip by elastic springs. Based on the finite element method, the system model is established to recognize the vibration characteristics. Furthermore, two laser displacement sensors are used to decouple the first two bending modes of the double flexible beam system. In the applied SMNNFC strategy, a neural-fuzzy framework is designed to obtain robust control performance and alleviate the chattering phenomenon. Considering unknown and varying system uncertainty, a parameter updating algorithm is adopted. The stability of SMNNFC is analyzed. The experimental setup is constructed and experiments are conducted, including set-point vibration control and simultaneous translation and vibration control under trapezoidal and sinusoidal trajectories. The experimental results demonstrate that the SMNNFC scheme has advantages in suppressing both the large and low amplitude vibrations of the coupling double flexible beam system.

Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

In the process of ship navigation, due to the characteristics of large inertia and large time delay, overshoot can easily occur in the process of path following. Once the ship deviates from the waypoint, it is prone to grounding and collision. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. The desired heading angle is designed according to a backstepping algorithm. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The second-order Taylor expansion is used to predict the future position, the current error and future error functions are constructed, and the total errors are fed back to the desired heading angle. In the sliding mode control system, the RBF neural network is used to approximate the total unknown term, and a velocity observer is introduced to obtain the surge velocity and sway velocity. To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. The simulation results show the effectiveness and superiority of the designed controller. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.

Integrated control of vehicle stability by nonlinear observer-based exponential-like sliding mode neural network system

Dynamic stability is critical to achieve the safety of the cars, particularly during emergency maneuvers. Coordinated control algorithms are suggestive of the enhanced safety and stability of a vehicle. Hence, a novel adaptive robust multi-input control framework is developed using the combination of direct yaw moment (DYC) and active front steering (AFS). The dynamics of the steering system mechanism is included for the reliability of the proposed control scheme. The proposed controller is developed according to the approximation capacity of the radial basis function (RBF) neural network system. The adaptation laws are derived based on the Lyapunov stability theory. Additionally, the proposed integrated control paradigm contains a state observer and the sliding surface of the tracking errors converges to the asymptotic stability condition through the design of a smooth exponential reaching law. The effectiveness of the proposed control scheme is compared to a high-performance optimal robust control technique and open-loop system. In order to assess the robustness of the proposed algorithm, structured and unstructured uncertainties were also incorporated in terms of the parametric uncertainties such as the tire cornering stiffness and cross-wind gust disturbance. The results obtained for different maneuvers reveal that the proposed controller is successful to improve the handling performance and to ensure the stability of the vehicle when compared to the previously reported methods.

Bus ESC Adaptive Neural Network Sliding Mode Control Based on Disturbance Observation

Bus ESC Adaptive Neural Network Sliding Mode Control Based on Disturbance Observation

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

Adaptive Sliding Mode Neural Network Control and Flexible Vibration Suppression of a Flexible Spatial Parallel Robot

With the goal of creating a flexible spatial parallel robot system in which the elastic deformation of the flexible link causes a rigid moving platform to produce small vibrations, we proposed an adaptive sliding mode control algorithm based on a neural network. To improve the calculation efficiency, the finite element method was used to discretize the flexible spatial link, and then the displacement field of the flexible spatial link was described based on floating frame of reference coordinates, and the dynamic differential equation of the flexible spatial link considering high-frequency vibrations was established through the Lagrange equation. This was combined with the dynamic equation of the rigid link and the dynamic equation considering small displacements of the rigid movable platform due to elastic deformation, and a highly nonlinear and accurate dynamic model with a rigid–flexible coupling effect was obtained. Based on the established accurate multi-body dynamics model, the driving torque with coupling effects was calculated in advance for feedforward compensation, and the adaptive sliding mode controller was used to improve the tracking performance of the system. The nonlinear error was examined to determine the performance of the neural network’s approximation of the nonlinear system. The trajectory errors of the moving platform in the X-, Y-, and Z-directions were reduced by 12.1%, 38.8%, and 50.34%, respectively. The results showed that the designed adaptive sliding mode neural network control met the control accuracy requirements, and suppressed the vibrations generated by the deformation of the flexible spatial link.

RBF Neural Network Sliding Mode Control Method Based on Backstepping for an Electro-hydraulic Actuator

Aiming at the interference problem and the difficulty of model parameter determination caused by the nonlinearity of the valve-controlled hydraulic cylinder position servo system, this study proposes a radial basis function (RBF) neural network sliding mode control strategy based on a backstepping strategy for the electro-hydraulic actuator. First, the non-linear system model of the third-order position electro-hydraulic control servo system is established on the basis of the principle analysis. Second, the model function RBF adaptive law and backstepping control law are designed according to Lyapunov’s stability theorem to solve the problem of external load disturbance and modelling uncertainty, combined with sliding mode control strategy and virtual control law. Finally, simulation and experiment on MATLAB Simulink and semi-physical experimental platform are accomplished to show the effectiveness of the proposed method. Moreover, results show that the designed controller has high tracking accuracy to the given signal.

Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties

This paper focuses on the sliding mode control (SMC) problem for a class of uncertain singular fractional order systems (SFOSs). The uncertainties occur in both state and derivative matrices. A radial basis function (RBF) neural network strategy was utilized to estimate the nonlinear terms of SFOSs. Firstly, by expanding the dimension of the SFOS, a novel sliding surface was constructed. A necessary and sufficient condition was given to ensure the admissibility of the SFOS while the system state moves on the sliding surface. The obtained results are linear matrix inequalities (LMIs), which are more general than the existing research. Then, the adaptive control law based on the RBF neural network was organized to guarantee that the SFOS reaches the sliding surface in a finite time. Finally, a simulation example is proposed to verify the validity of the designed procedures.

Adaptive Backstepping Sliding Mode Control of Tractor-trailer System with Input Delay Based on RBF Neural Network

In this paper, an adaptive sliding mode neural network(NN) control method is investigated for input delay tractor-trailer system with two degrees of freedom. An uncertain camera-object kinematic tracking error model of a tractor car with n trailers with input delay is proposed. Radial basis function neural networks(RBFNNs) are applied to approximate the unknown functions in the error model. A sliding mode surface with variable structure control is designed by using backstepping method. Then, an adaptive NN sliding mode control method is thus obtained by combining Lyapunov-Krasovskii functionals. The controller realizes the global asymptotic trajectories tracking of the kinematics system. The stability of the closed-loop system is strictly proved by the Lyapunov theory. Matlab simulation results demonstrate the feasibility of the proposed method.