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

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC).

In this paper, a neural network sliding mode control method based on genetic algorithm (GA) is proposed, considering the control accuracy reduction problem of the manipulators caused by the external interference and modeling error. The system is divided into the nominal part and the uncertain part, using state feedback control and improved neural network sliding mode control respectively. Neural network used to approximate the upper bound of uncertainty and serves as an input for the dynamic compensation of the sliding mode controller. At the same time, genetic algorithm is used to optimize the structural parameters of the neural network to ensure effective mapping. The global stability of the system is proved by Lyapunov function. The simulation results show that the proposed method can effectively track the desired trajectory with small error and has good robustness to the external uncertainty.

The 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.

Due to its complex nonlinear, underdriven, and strongly coupled characteristics, the tower crane will cause the load to swing violently when working, which will cause the tower crane to tip over in serious cases, and there exists a huge safety hazard. In this article, a new artificial neural network sliding mode control method is designed for the control problems of tower crane lifting in position and load swing prevention, which has strong robustness to disturbances and unmodeled dynamics, and ensures that the tower crane lifting is accurately tracked in position and suppresses the load swing at the same time. First, a nonlinear dynamics model of a five-degree-of-freedom tower crane considering the actual working conditions is established. Aiming at the problem that it is difficult to effectively control the nonlinear model of the tower crane system, a new neural sliding mode controller and compensation controller are designed based on the sliding mode control theory and using radial basis function neural network. The neural sliding mode controller is used to approximate the sliding mode equivalent controller with uncertainty and strong nonlinearity, and the compensation controller realizes the compensation of the neural sliding mode controller for the difference between the system control inputs and the uncertainty of the system. The convergence and stability of the proposed control system is rigorously demonstrated using the Lyapunov stability theory. Simulation studies have been carried out to verify the correctness of the model established in this article, as well as the excellent control performance of the control system and the ability to deal with system uncertainty, proving its strong robustness.

SummaryRock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro‐hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model‐based high‐performance control algorithms. To solve these challenges, a parameter adaptive based neural network sliding mode control algorithm is proposed to enhance control performance of the electro‐hydraulic system. The parameter adaptive law is developed to estimate unknown parameters of the system, the neural network is applied for compensating unmodeled dynamics, and then the final control law is designed by sliding mode control method, and the stability demonstration of the closed‐loop system is given. In the simulations, the effectiveness of the designed parameter adaptive law is verified. Extensive comparison experiments are performed on a real rock drilling jumbo driven by proportional valves, the experimental results demonstrate that the developed control algorithm obviously improves the control precision of hydraulic cylinder of the rock drilling jumbo compared with the traditional sliding mode and PID control algorithm, thus the designed control algorithm can be expanded and applied for general hydraulic servo control mechanism.

A new approach for induction motor drive control is presented in this paper. The new scheme is based on the direct application of an artificial neural network, trained with sliding mode control, into the feedback control system. Neural network learning is implemented with an on‐line adaptation algorithm that inherits robustness and high speed of learning from Sliding Mode Control. The results showed that proportional and integral or proportional, integral and differential controllers used in classical motor drives can be replaced with a neural network with on‐line learning. Copyright © 2003 John Wiley & Sons, Ltd.

The yarn vibration causes the yarn tension value to fluctuate, causing a change in the amount of yarn feed, thus causing a deviation of the carpet pile height from the predetermined value. To solve this problem, the sliding mode control algorithm is used to design the sliding mode function and the sliding mode control law. And four variables in the yarn vibration system are controlled by the MATLAB software. For solving the chattering problem of the control law, the sliding mode control law is improved. The fuzzy sliding mode control algorithm based on the quasisliding mode is adopted. The results show that the sliding mode control algorithm is effective, but the sliding mode control force needs to be switched at high frequency and there is severe chattering. The fuzzy sliding mode control algorithm based on quasisliding mode is adopted to achieve better control effect with a smaller force. In addition, the control force does not have high‐frequency switching, and the change is relatively stable, which reduces the chattering phenomenon of sliding mode control.

In order to improve the ability of magnetic levitation feed platform of controllable excitation linear synchronous motor to resist external disturbance, a sliding mode control method based on fuzzy switching gain adjustment is proposed. According to the operation mechanism of controllable excitation linear synchronous motor, the voltage equation, flux equation, magnetic levitation force equation and state equation of controllable excitation linear synchronous motor are established. The sliding mode surface is designed, and the integral term is introduced to eliminate the steady-state error of the system. The reaching law is designed to reduce chattering by using saturation function instead of sign function. Fuzzy logic reasoning is used to estimate the switching gain, to cancel the unknown disturbance and further weaken the chattering of the system. The sliding mode controller based on fuzzy switching gain adjustment is designed, and the system is simulated in MATLAB environment. The simulation results show that the fuzzy sliding mode control algorithm can improve the dynamic performance of the system and weaken chattering. The feasibility of this algorithm is proved.

Aiming at the path tracking problem of AUV (autonomous underwater vehicle) in the process of docking, a fuzzy sliding mode control algorithm based on RBF (radial basis function) neural network is proposed. Firstly, the sliding mode control is used to track the trajectory, the fuzzy control is used to continuously correct the parameters of the exponential reaching rate in the sliding mode control to alleviate the shaking problem. Then the RBF neural network is used to compensate uncertainty in the AUV motion model and the external unknown interference. Finally, the stability of the control system is proved by Lyapunov stability theory. The simulation result shows that the designed control algorithm can track the trajectory of AUV effectively. By comparing tracking effects with traditional sliding mode control and fuzzy sliding mode control, it is proved that the proposed control method has faster tracking speed, better stability and better tracking performance.

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.

Sliding Mode Control (SMC) has a good effect on solving typical nonlinear robot control systems. But it also has defects such as chattering, which limits its application. With the rapid development of artificial intelligence technology, researchers have proposed a hybrid controller based on Deep Neural Network (DNN) and SMC to improve SMC s control quality. This article introduces the latest research results in this field in recent years and conducts an in-depth analysis of these control algorithms fundamental principles. Finally, the future research directions in this field have prospected.

An induction generator (IG) speed drive with the application of a sliding mode controller and a proposed fuzzy neural network (FNN) controller is introduced in this paper. Grid connected wind energy conversion system (WECS) present interesting control demands, due to the intrinsic nonlinear characteristic of wind mills and electric generators. The FNN torque compensation is feedforward to increase the robustness of the wind driven induction generator system. A multivariable controller is designed to drive the turbine speed to extract maximum power from the wind and adjust to the power regulation. Moreover, a sliding mode speed controller is designed based on an integral-proportional (IP) sliding surface. When sliding mode occurs on the sliding surface, the control system acts as a robust state feedback system.

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator is proposed. Based on the Udwadia–Kalaba motion constraint equation, the dynamic equation of the constrained manipulator with both ideal and non-ideal constraints is obtained. Considering the uncertainty of the terminal non-ideal constrained force and the chattering phenomenon of sliding mode control, the adaptive fuzzy and the sliding mode control method are combined to control the constrained manipulator. Because the system is constrained, the model order reduction method is innovatively used in the control algorithm. The stability of the system is proved by Lyapunov theorem. For demonstrating the effectiveness of the control algorithm, the 2-degree-of-freedom manipulator is taken as the research object. Finally, the high-precision control of the manipulator is achieved and the chattering phenomenon caused by the sliding mode control is weakened.

This paper presents a novel guidance law to intercept non-maneuvering targets with impact angle and lateral acceleration command constraints. Firstly, we formulate the impact angle control to track the desired line-of-sight (LOS) angle, which is achieved by selecting the missile's lateral acceleration to enforce the sliding mode on a sliding surface at impact time. Secondly, we use the Lyapunov stability theory to prove the stability and finite time convergence of the proposed nonlinear sliding surface. Thirdly, we introduce the wavelet neural network (WNN) to adaptively update the additional control command and reduce the high-frequency chattering of sliding mode control (SMC). The proposed guidance law, denoted WNNSMC guidance law with impact angle constraint, combines the SMC methodology with WNN to improve the robustness and reduce the chattering of the system. Finally, numerical simulations are performed to demonstrate the validity and effectiveness of the WNNSMC guidance law.

This paper addresses the finite-time tracking control problem for a class of quadrotor unmanned aerial vehicle (QUAV) subject to unknown mixed faults and external disturbances. The considered mixed faults include both input quantization and actuator faults. First, radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear dynamics of the QUAV system, with adaptive control laws designed for online weights updates. Second, since the neural network approximation errors and external disturbances can be treated as unknown but bounded constants, adaptive control laws are developed to estimate these parameters. Third, to address the design complexity caused by unknown control coefficients arising from mixed faults, a Nussbaum gain function is introduced. Subsequently, based on the designed global fast terminal sliding mode (GFTSM) functions, adaptive GFTSM neural network control strategies are proposed for position and attitude tracking control. Theoretical analysis confirms that these control strategies guarantee the QUAV system’s position and attitude outputs converge to reference trajectories, with tracking errors reaching a very small neighborhood of zero within a finite time. Finally, the effectiveness of proposed control strategies is validated through an actual system.