Robust Bounded Control for Fuzzy Mechanical Systems: Fuzzy Optimal Design and Inequality Constraint Reorganize

Wang, Zongfan; Yang, Guolai; Yang, Guolai; Sun, Qinqin; Ma, Yuze

doi:10.1109/tfuzz.2023.3234585

Cited by 7 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Control law can be expressed as follows: According to ( 29) and (31), Equation ( 32) can be obtained as follows:…”

Section: Traditional Smcmentioning

confidence: 99%

“…In recent years, many researchers have applied intelligent control technology to practical projects and achieved good control effects. Literature [31] is the first to consider the tank bidirectional stabilization system with a fully electric actuator as a fuzzy mechanical system with inequality constraints and conduct an optimization design after proposing an error-controllable control strategy. In the Literature [32], a dynamic-free adaptive sliding mode control (adaptive-SMC) methodology for the synchronization of a specific class of chaotic delayed fractional-order neural network systems in the presence of input saturation is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Hong,

Zhou,

2024

Applied Sciences

View full text Add to dashboard Cite

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%.

show abstract

“…Control law can be expressed as follows: According to ( 29) and (31), Equation ( 32) can be obtained as follows:…”

Section: Traditional Smcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hong,

Zhou,

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In this paper, a fuzzy controller is built based on fuzzy control theory (Wang et al, 2023) to realize longitudinal collision avoidance. The relative distance and relative speed between vehicle and pedestrian are taken as the inputs of the fuzzy controller, and the controller outputs the desired deceleration to ensure the safety of longitudinal collision avoidance.…”

Section: Longitudinal Collision Avoidance Deceleration Planningmentioning

confidence: 99%

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Tang,

Yang,

Jiang

et al. 2024

Mech. Sci.

View full text Add to dashboard Cite

Abstract. In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance strategy can effectively reduce the collision risk between intelligent vehicles and pedestrians. In order to improve the prediction accuracy of pedestrian trajectory and the safety of collision avoidance, a longitudinal and lateral intelligent collision avoidance strategy based on pedestrian trajectory prediction is proposed. Firstly, the process of a pedestrian crossing the road is considered as a combination of free motion described by first-order Markov model and the constrained motion presented by improved social force model. The predicted pedestrian trajectory is obtained by weighted fusion of the trajectories of the two models with a multiple linear regression algorithm. Secondly, according to the predicted pedestrian trajectory and time to collision (TTC) the longitudinal and lateral collision avoidance strategy is designed. The improved artificial potential field method is used to plan the lateral collision avoidance path in real time based on the predicted pedestrian position, and a fuzzy controller is constructed to obtain the desired deceleration of the vehicle. Finally, the pedestrian motion fusion model and the longitudinal and lateral collision avoidance strategy are verified by Prescan and Simulink co-simulation. The results show that the average displacement error (ADE) and final displacement error (FDE) of pedestrian trajectory based on pedestrian motion fusion model are smaller compared with a Markov model and improved social force model, and the proposed pedestrian collision avoidance strategy can effectively achieve longitudinal and lateral collision avoidance.

show abstract

“…Therefore, the new generation of tank vehicle stabilization systems fully replaces the traditional actuator hydraulic cylinder of the tank gun by an electric cylinder. In the process of analytical dynamics modeling of the tank vehicle stabilization system, the state-space equation has been completely changed, although the hydraulic transmission characteristics are not considered [5]. Under the highmobility driving environment of tanks, the coupling, nonlinearity and uncertainty of the vehicle stabilization system are more prominent, and it is difficult to establish high-accuracy analytical dynamics equations and design high-performance control strategies [6,7].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

Wang,

Yuan,

Wang

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments.

show abstract

Robust Bounded Control for Fuzzy Mechanical Systems: Fuzzy Optimal Design and Inequality Constraint Reorganize

Cited by 7 publications

References 29 publications

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

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

Development of pedestrian collision avoidance strategy based on the fusion of Markov and social force models

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

Contact Info

Product

Resources

About