An active disturbance rejection control for hysteresis compensation based on Neural Networks adaptive control

Zhao

2022

Nonlinear Dyn

Self Cite

In this paper, a mass adaptive control method combining robust sliding mode control (SMC) and linear active disturbance rejection control (LADRC) is designed for the quadrotor load unmanned aerial vehicle (UAV) with mass variation. The scheme combines the advantages of SMC and LADRC. SMC can enhance the robustness of the controller, improve the anti-disturbance performance and overcome the problem of low control precision caused by bandwidth limitation of LADRC. The linear extended state observer (LESO) can estimate the external and internal disturbances of the system in real time, and then compensate the total disturbance through the PD controller. In order to simplify parameter setting, adaptive control is introduced in LADRC to adjust controller parameters in real time. In addition, adaptive law is also used to control the mass variation of the quadrotor. Then the stability of the whole system is verified by Lyapunov stability theory. Finally, the comparison with LADRC shows the superiority of the designed scheme, which can track the reference signal stably and effectively.

Section: Design Of Linear Active Disturbance Rejection Controlmentioning

confidence: 99%

Based on robust sliding mode and linear active disturbance rejection control for attitude of quadrotor load UAV

Zhao

2022

Nonlinear Dyn

Self Cite

“…From the mathematical model of the quadrotor, the attitude system of quadrotor is nonlinear and strongly coupled. The LADRC technology can regard the coupling between each attitude channel as an uncertain internal disturbance, which is regarded as a part of the total disturbance and estimated by the LESO (Liu and Zhao, 2021). Therefore, there is no need to decouple each channel of the system, each channel is independently controlled.…”

Section: Control Scheme Designmentioning

confidence: 99%

Adaptive-based linear active disturbance rejection attitude control for quadrotor with external disturbances

Transactions of the Institute of Measurement and Control

Zhao

2021

Self Cite

This paper proposes a linear active disturbance rejection control (LADRC) scheme for a quadrotor unmanned aerial vehicle (UAV) with external disturbances based on adaptive control to address the attitude control problem. Firstly, the dynamic model of the quadrotor is established, and LADRC is used to control the altitude, yaw angle, pitch angle and roll angle of the quadrotor UAV affected by external disturbances, which enhances the anti-disturbance ability of the system. In addition, adaptive control is introduced to solve the problem of difficult parameter tuning in LADRC. Then, the stability of the system is demonstrated by Lyapunov theory. Finally, the simulation results verify the effectiveness of the proposed control scheme under external disturbances.

“…Shi et al designed an adaptive neural controller with a new neural weight update law, which ensures that the RBF neural network can accurately identify unknown systems and enables the estimated neural weights to converge to their ideal values [25]. Combining the adaptive neural network with ADRC design techniques, Liu et al proposed a new dual-channel composite controller scheme, which can guarantee the tracking of the desired signal within a small domain of the origin [26]. From this, it is not difficult to see that the learning control method based on a neural network is more flexible than other control methods and can solve more complex control problems of unknown nonlinear systems, but it cannot realize control problems with constraints.…”

Section: Introductionmentioning

confidence: 99%

A Model Predictive Control for Lot Sizing and Scheduling Optimization in the Process Industry under Bidirectional Uncertainty of Production Ability and Market Demand

Tang

Computational Intelligence and Neuroscience

2022

In the face of bidirectional uncertainty of market demand and production ability, this paper establishes a multiobjective mathematical model for lot sizing and scheduling integrated optimization of the process industry considering both material network and production manufacturing and finds the optimal decision of the model through model predictive control to minimize total completion time and total production cost. While realizing the model predictive control proposed in this paper, the Elman neural network predicts the relevant parameters required by learning historical orders for the uncertain market demand and equipment production ability. Then, the calculation formulas of product supply and demand matching and equipment production ability are formed and introduced into the next stage of the model as a constraint condition. In addition to the above constraints for constructing lot sizing and scheduling integrated models in the process industry, this paper also considers both the material network and production manufacturing and uses the IMOPSO algorithm to solve the problem iteratively. So far, a complete model predictive control can be generated. Through the model predictive control, the production system can respond in advance, make appropriate changes to offset the foreseeable interference, and obtain the lot sizing and scheduling scheme considering bidirectional uncertainty, thereby improving the system’s overall robustness. Finally, this paper realizes the model's predictive control process through example simulation and analyzes the operation results combined with the scheduling Gantt chart to verify the applicability and effectiveness of the model.