Neural Network Sliding Mode Control Research Articles

Prescribed Performance Tracking Control for 2-D Plane Vehicle Platoons with Actuator Faults and Saturations

This paper studies the multi-lane fusion tracking control problem for the two-dimensional third-order nonlinear vehicle platoons with actuator faults and saturations. A novel class of performance functions is designed to guarantee that the platoon tracking errors under asymmetric constraints satisfy the pre-given performance. To achieve the multi-lane fusion tracking control, new third-order prescribed performance sliding-mode surfaces are proposed for nonlinear vehicle platoons. Further, the saturation steering wheel input and the fault-tolerant throttle/brake input are designed by using the adaptive neural network sliding mode control technique, respectively. They can steer the platoon tracking errors approach the predefined region in the preset finite time while avoiding collisions and maintaining communications. Finally, the effectiveness of the proposed control scheme is verified by using two simulation examples with comparisons.

Control of Quadrotor Based on RBF Neural Network Adaptive Fast Terminal Sliding-Mode Strategy

I The purpose of this paper is to propose a fast adaptive terminal sliding mode control method for quadrotor unmanned aerial vehicles (UAVs) under dynamic uncertainties and external interference. Firstly, a complete mathematical dynamics model of a quadrotor UAV is presented. Secondly, a robust dual-loop nonlinear control law for the quadrotor velocity and attitude tracking is designed. Through the introduction of equivalent control inputs, the attitude control channels can be decoupled and attitude stabilized controllers can be designed separately. For velocity tracking and altitude tracking control, coupled controllers are designed to ensure velocity tracking based on the stability of altitude tracking control. The attitude loop adopts RBF neural network sliding mode control, to compensate for model disturbance uncertainty, adjust its gain in real time, and suppress chattering problems. Finally, the experimental results show that the designed controller can not only suppress external interference, but also achieve accurate tracking of the desired flight command.

Sliding mode control and its application in switched systems: A survey

According to the development and research of sliding mode variable structure control (SMVSC) in recent years. This paper mainly reviews the development and applications of some popular sliding mode control (SMC), such as adaptive SMC, discrete-time system SMC, fuzzy SMC, and neural network SMC. With the research and development of switched system (SS), the research scholars can deal with the complex and changeable system more effectively by combining the SMC with the SS, so as to improve the efficiency and stability of the system. The theoretical research and applications of SMC combined with fuzzy SS, predictive SS and uncertainty SS are also becoming more and more attractive. The purpose of this paper is to analyze and organize the current research status of the popular directions of SMC, and provide the readers with the latest research results on the application directions and new trends in the field of SMC. By considering these theoretical studies and numerous applications, the research and development of intelligent control techniques and switching systems based on sliding mode control are reviewed. Finally, some prospects for related research are presented.

Bluff body vortex-induced vibration control of floating wind turbines based on a novel intelligent robust control algorithm

Given the great potential of the offshore wind power generation in renewable energy sources, it will bring unprecedented significant development opportunities. Meanwhile, the installed capacity of floating wind turbines (FWTs) is huge. However, as one of the important parts of that, FWTs are always subjected to complex environmental loads during operation, which will critically affect the stability of wind power generation. Hence, it is urgent to analyze and control its stability for the safe operation of wind turbines. It is accepted that vortex-induced vibration (VIV) of a bluff body structure is the leading cause of structural damage to FWTs. For this reason, a radial basis function neural network sliding mode control (RBFNNSMC) is proposed to improve the modeling accuracy of bluff body VIV control. Then, the joint numerical analysis system was designed to achieve the completely coupled fluid structure vibration control of bluff body. The numerical results indicate that RBFNNSMC can better control the forward/cross-flow vibration of bluff body. In addition, the controller is not responsive to changes in system parameters and has strong robustness.

Double-Loop PID-Type Neural Network Sliding Mode Control of an Uncertain Autonomous Underwater Vehicle Model Based on a Nonlinear High-Order Observer with Unknown Disturbance

An unknown nonlinear disturbance seriously affects the trajectory tracking of autonomous underwater vehicles (AUVs). Thus, it is critical to eliminate the influence of such disturbances on AUVs. To address this problem, this paper proposes a double-loop proportional–integral–differential (PID) neural network sliding mode control (DLNNSMC). First, a double-loop PID sliding mode surface is proposed, which has a faster convergence speed than other PID sliding mode surfaces. Second, a nonlinear high-order observer and a neural network are combined to observe and compensate for the nonlinear disturbance of the AUV system. Then, the bounded stability of an AUV closed-loop system is analyzed and demonstrated using the Lyapunov method, and the time-domain method is used to verify that the velocity- and position-tracking errors of AUVs converge to zero exponentially. Finally, the radial basis function (RBF) neural network PID sliding mode control (RBFPIDSMC) and the RBF neural network PID sliding mode control (RBFPDSMC) are compared with this method in two trajectory tracking control simulation experiments. In the first experiment, the average Euclidean distance of the position-tracking error for this method was reduced by approximately 73.6% and 75.3%, respectively, compared to those for RBFPDSMC and RBFPIDSMC. In the second experiment, the average Euclidean distance of the position tracking error for this method was reduced by approximately 86.8% and 88.8%, respectively. The two experiments showed that the proposed control method has a strong anti-jamming ability and tracking effect. The simulation results obtained in the Gazebo environment validated the superiority of this method.

The adaptive neural network fuzzy sliding mode control for the 3-RRS parallel manipulator

In order to improve the high-precision tracking control of angle variables and the sliding mode equivalent control part for the 3-RRS parallel manipulator, an adaptive neural network fuzzy sliding mode control algorithm with self-adjusting switching gain is proposed. Firstly, considering the uncertainty of the constrained force between derived links and the moving platform, the complete dynamic model including ideal and non-ideal constrained force is established by combining with the Udwadia-Kalaba(U-K) equation and Lagrange method. Secondly, the neural network sliding mode controller is designed to realize the approximate solution of the sliding mode equivalent control part. At the same time, in order to reduce the chattering phenomenon of the neural network sliding mode controller, a fuzzy adjustment rule of switching gain is designed to better compensate for the uncertain terms. And then the stability of the control system is proved by the Lyapunov method. Finally, the proposed control algorithm is simulated on the 3-RRS parallel manipulator. The simulation results show that the chattering phenomenon is overcome. The high-precision control of angle variables and the sliding mode equivalent control part is realized.

A Novel Robust Neural Network Sliding-Mode Control Method for Synchronizing Fractional Order Chaotic Systems in the Presence of Uncertainty, Disturbance and Time-Varying Delay

A Novel Robust Neural Network Sliding-Mode Control Method for Synchronizing Fractional Order Chaotic Systems in the Presence of Uncertainty, Disturbance and Time-Varying Delay

Wavelet neural network sliding mode control of two rigid joint robot manipulator

To solve the problems of low accuracy and poor stability due to uncertainties, external disturbances and unknown load, which exist in the position control of rigid joint robot manipulator, this article is to propose Non-Singular Fast Terminal Sliding Mode Control strategy with Wavelet neural networks observer (NSFTSMCW). The wavelet observer is designed using the online approximation capability of the neural network, which is used to online estimate the modeling error, external disturbances and uncertainties generated by the dynamic surface control of the joint robot online. Combining the above strategies, the robot manipulators position controller is designed. The stability of this control strategy is demonstrated by stability analysis using the Lyapunov criterion. Simulations on the 2-Link Rigid Joint (2LRJ) robot show that the control strategy can overcome the chattering phenomena ensures the accuracy and stability of the joint robot position control.

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.

Rehabilitation robot following motion control algorithm based on human behavior intention

In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids. This paper proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient’s movement. Firstly, a multi-sensor data acquisition system is designed according to the rehabilitation needs of the patient and the movement characteristics of the human body. A mathematical model of movement behavior is then established. By analyzing and processing motion data, the change in the center of gravity of the human body and the behavior intention signal are derived and used as a control command for the robot to follow the human body’s movement. Secondly, in order to improve the control effect of rehabilitation robot following human motion, an adaptive radial basis function neural network sliding mode controller (ARBFNNSMC) is designed based on the robot dynamic model. The adaptive adjustment of switching gain coefficient is performed by radial basis function neural network. The controller can overcome the influence caused by the change of robot control system parameters due to the fluctuation of the center of gravity of human body, enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human body motion. Finally, the motion following experiment of the rehabilitation robot is performed. The experimental results show that the robot can recognize the motion intention of human body and perform the training goal of following different subjects to complete straight lines and curves. The correctness of human motion behavior model and robot control algorithm is verified, which shows the feasibility of the intelligent control method proposed in this paper.

Adaptive Neural Network Iterative Sliding Mode Course Tracking Control for Unmanned Surface Vessels

In view of the problem of course tracking control of under-driven USV under complex external environment, the adaptive control law is designed by constructing an iterative sliding mode function and using Lyapunov stability theory on the basis of the kinetic model of ship motion. The RBF neural network control technology and adaptive control technology are integrated into the control algorithm, and the iterative sliding mode heading tracking controller of the unmanned surface ship adaptive-neural network is designed. The online reinforcement learning of the RBF neural network is carried out by the reinforcement learning algorithm, which enhances the approximation performance of the network. Besides, the particle swarm optimization with shrinkage factor is applied to the optimization for control parameters to enhance the adaptability and robustness of the control system. The performance experiment verifies the effectiveness and practicability of the adaptive iterative sliding mode control algorithm for unmanned surface ships based on reinforcement learning and particle swarm optimization algorithm; meanwhile, comparative tracking experiments verify that the comprehensive performance of the proposed USV’s course tracking control system is better than that of the genetically optimized neural network sliding mode control system. Therefore, the fusion of multiple algorithms can be applied to improve the performance of the control system.

Adaptive neural network sliding mode control of a nonlinear two‐degrees‐of‐freedom helicopter system

AbstractThe helicopter can play an important role in military and civil applications owing to its super maneuvering ability, which is closely related to its control system. To improve control performance, this study presents an adaptive sliding mode control strategy merging an adaptive neural network for a nonlinear two‐degrees‐of‐freedom (2‐DOF) helicopter system. By setting up the Lyapunov function, the asymptotic stability of the closed‐loop system is guaranteed, the astringency of the neural network weight renewal course is pledged, and the asymptotic attitude adjustment and trajectory tracking for the desired set point are realized. The availability of the adaptive radial basis function sliding mode control is finally verified via the simulation and real implementation on a nonlinear 2‐DOF helicopter platform.

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.

Sliding Mode Control of a 2DOF Robot Manipulator: A Simulation Study Using Artificial Neural Networks with Minimum Parameter Learning

Background: In this paper we have developed an intelligent control law for the control of mobile manipulator robots by investigating the various techniques proposed in the literature. Thus, we have adopted a hybrid approach integrating part of classical and advanced automation in order to create an efficient control structure that can cope with a certain level of complexity. Our research logic is based on the process of keeping in mind that the control system must comply with the constraints imposed during the implementation of the control architecture. Objective: This paper aims to develop a control law in order to guarantee a certain level of performance and, more precisely, during a trajectory tracking application for mobile handling missions. The developed control law guarantees robustness with respect to external disturbances and parametric uncertainties due to the modelling of the system. Methods: In this paper, a study of the basic concepts of robotics and robot modelling is presented in order to set up the dynamic model used for the elaboration of the command. A sliding-mode controller based on a radial base function neural network with minimum parameter learning is developed for the Pelican robot as a two link robot manipulator. This approach, which combines a Radial Base Function Neuronal Network (RBFNN) and a Sliding Mode Control (SMC), is presented for the tracking control of this class of systems with unknown nonlinearities. The centres and outputs weights of the RBFNN are updated via online learning in accordance with the adaptive laws, allowing the control output of the neural network to approach the equivalent control in the sliding mode in the predetermined direction. The Lyapunov function is used to develop the adaptive control algorithm based on the RBFNN model. To reduce computational load and increase real-time arm performance, an RBFNN-based on the SMC with the Minimum Parameter Learning (MPL) method is designed. Results: Neural network sliding mode control is designed to underline the effectiveness of the approach to control the manipulator. This method of control ensure the tracking trajectories. Conclusion: The results of the simulation for the manipulator arm presented demonstrates the effectiveness of the modelling strategy, the correction and the robustness of the control approach.

Intelligent vehicle lateral control based on radial basis function neural network sliding mode controller

Based on the predigestion of the dynamic model of the intelligent firefighting vehicle, a linear 2-DOF lateral dynamic model and a preview error model are established. To solve the problems of a highly non-linear vehicle model, time-varying parameters, output chattering, and poor robustness, the Radial Basis Function neural network sliding mode controller is designed. Then, different driving speeds are used to conduct simulation tests under standard double-shifting and smooth curve road conditions, and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path. The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes.

Iterative learning based neural network sliding mode control for repetitive tasks: With application to a PMLSM with uncertainties and external disturbances

The uncertainties, force ripple, friction and other external disturbances in permanent magnet linear synchronous motors (PMLSMs) pose great challenges to the design of advanced controllers with high speed and high precision. Existing neural network based sliding mode control (NNSMC) methods typically adopt neural network as an online compensator for uncertainties and disturbances, meanwhile stability and robustness are assured owing to sliding mode control. However, PMLSMs usually need to perform repetitive motion tasks within a finite time interval in practical applications, where existing NNSMC methods ignore the previous repetitions’ information, and consequently limit the motion performance improvement for trajectory tracking tasks. This paper proposes an iterative learning based neural network sliding mode control (ILNNSMC) scheme, in which the concept of iterative learning is incorporated into NNSMC methods for the first time. In ILNNSMC, the uncertainties and disturbances can be compensated by NN (updating in the time-domain) and ILC (learning in the iteration-domain) in a complementary way, and the sliding variable is driven to converge to zero for arbitrary time within the predesigned time interval with successive iterations. The motion performance for repetitive tasks can be improved substantively. Moreover, a non-linear dynamic learning gain (DLG) strategy is proposed and adopted in iterative learning law, which guarantees both convergence speed and steady-state accuracy in the learning process. Stability of the scheme is discussed in both time and iteration domains, and the control performance is verified by extensive experiments on a PMLSM.

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.

Comparative Study Between Back-Stepping Control and ANN-Sliding Mode Control of DFIG-Based Wind Turbine System

This paper deals with the control of the rotor-side converter of a doubly fed induction generator integrated in a wind turbine system for regulating independently the active and the reactive power of the DFIG stator by two non-linear control methods: backstepping (BSC) and artificial neural network sliding mode control (ANN-SMC). A comparative study of the two control strategies is performed in order to determine which one is the most efficient and robust in terms of the response time on the one hand and the power ripple minimizing on the other hand, under the effect of the variation of DFIG’s internal parameters. The modeling of the wind turbine system based on DFIG and the controller’s implementation are performed using Matlab/Simulnk environment. Simulation results have proven the robustness of both controllers, although with a slight superiority of the ANN-SMC.

Barrier function-based adaptive neural network sliding mode control of autonomous surface vehicles

In this paper, we consider trajectory tracking control for autonomous surface vehicles (ASVs) with unknown boundary model uncertainties and external disturbances. The neural networks (NNs) and the sliding mode control (SMC) with a switched adaptive law are combined for the first time. The NNs are used to approximate model uncertainties and external disturbances of ASVs. The parameters of the robust SMC term first increase until the sliding variable reaches a quasi sliding mode which bound is associated with the parameter of the barrier function (BF). Then the BF is selected as the parameter of the robust term for the SMC strategy to estimate the NN approximation error and to constrain the sliding variable inside the predefined quasi sliding mode. One salient feature of our approach is that the robust control parameter is no more than approximation error of NNs in sliding and steady phases of SMC. Simulation studies are performed to illustrate the advantage of the proposed control method.