Adaptive robust precision control of an active spray boom suspension with disturbance estimation

2021 ◽  
pp. 107754632110552
Author(s):  
Longfei Cui ◽  
Xinyu Xue ◽  
Feixiang Le
Keyword(s):  
Control Algorithm ◽  
Suspension System ◽  
Hydraulic Pressure ◽  
Time Varying ◽  
Nonlinear Dynamic Model ◽  
Parameter Uncertainties ◽  
Motion Platform ◽  
Precision Control ◽  
Disturbance Estimation ◽  
Algorithm Comparison

When the boom sprayer works in the field, the boom must be parallel to the undulating ground or crop canopy. Aiming at the problem of low control accuracy and poor stability caused by parameter uncertainties and time-varying disturbances in the electro-hydraulic active boom suspension system, this paper proposes an adaptive robust precision control algorithm based on disturbance estimation. Firstly, the dynamic analysis modeling method is adopted to establish the nonlinear dynamic model and mechanism geometric equation of the pendulum active and passive suspension. Then, the controller was designed based on the nonlinear model of the suspension system. The proposed controller uses the backstepping design method to integrate the disturbance observer into the adaptive robust controller, which can effectively deal with the parameter uncertainties and time-varying disturbances in the suspension system model. Finally, a large number of experiments were carried out by taking a 28 m large boom active suspension driven by a single-rod hydraulic pressure as an example. Using an established rapid control prototype of a large boom suspension, a variety of control algorithm comparison experiments were carried out, and a 6-DOF motion platform was used to simulate the motion coupling interference of the sprayer chassis. The experiment results illustrate the high-performance characteristics of the proposed controller and improve the tracking performance of the active pendulum suspension system under various parameter uncertainties and time-varying disturbances.

Adaptive altitude flight control of quadcopter under ground effect and time-varying load: theory and experiments

2021 ◽  
pp. 107754632110501
Author(s):  
Ji-Won Lee ◽  
Nguyen Xuan-Mung ◽  
Ngoc Phi Nguyen ◽  
Sung Kyung Hong
Keyword(s):  
Flight Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Ground Effect ◽  
Lyapunov Theory ◽  
Control Technique ◽  
Time Varying ◽  
Control Performance ◽  
Parameter Uncertainties ◽  
Altitude Control

In recent years, the boom of the quadcopter industry resulted in a broad range of real-world applications which highlighted the urgent need to improve quadcopter control quality. Typically, external disturbances, such as wind, parameter uncertainties caused by payload variations, or the ground effect, can severely degrade the quadcopter’s altitude control performance. Meanwhile, widely used controllers like the proportional-integral-derivative control cannot guarantee control performance when the system is critically affected by factors that exhibit a high degree of variability with time. In this paper, an adaptive control algorithm is proposed to improve quadcopter altitude tracking performance in the presence of both the ground effect and a time-varying payload. First, we derive an adaptive altitude control algorithm using the sliding mode control technique to account for these uncertainties in the quadcopter dynamics model. Second, we apply Lyapunov theory to analyze the stability of the closed-loop system. Finally, we conduct several numerical simulations and experiments to validate the effectiveness of the proposed method.

Robust steering assistance control for tracking large-curvature path considering uncertainties of driver’s steering behavior

2021 ◽  
pp. 095440702097682
Author(s):  
Jinxiang Wang ◽  
Zhenwu Fang ◽  
Mengmeng Dai ◽  
Guodong Yin ◽  
Jingjing Xia ◽  
...  
Keyword(s):  
Path Following ◽  
Vehicle Speed ◽  
Time Varying ◽  
Steering Control ◽  
Characteristic Parameters ◽  
Parameter Uncertainties ◽  
Large Curvature ◽  
Steering Characteristics ◽  
Dynamic Compensator ◽  
Takagi Sugeno

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Study of Hydraulic Servo System Based on Fuzzy Sliding Mode Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 2674-2678
Author(s):  
Kun Yang ◽  
Cai Jun Liu ◽  
Shu Min Liu
Keyword(s):  
Control Algorithm ◽  
Sliding Mode ◽  
Servo System ◽  
Time Varying ◽  
External Interference ◽  
System A ◽  
Hydraulic Servo ◽  
Fuzzy Sliding Mode ◽  
Hydraulic Servo System ◽  
Position Servo

Based on the situation that the hydraulic position servo system is easily influenced by the external interference and the parameters of which are different with time-varying, the fuzzy control can soften the buffeting and the sliding algorithm has no the same problems as the hydraulic position servo system, a brandly-new fuzzy sliding control algorithm is designed. In the simulation process, within the parameters of simulated time-varying and outside strong interference, the results show that the hydraulic servo system based on fuzzy sliding mode control algorithm has a greater resistance to internal and external interference and time-varying parameters.

Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
He-Wei Zhao ◽  
Li-bin Yang
Keyword(s):  
Rbf Neural Network ◽  
Tracking Error ◽  
Hypersonic Vehicle ◽  
Nonlinear Dynamic Model ◽  
Content Type ◽  
Lateral Dynamics ◽  
Disturbance Estimation ◽  
System A ◽  
Complexity Problem ◽  
Velocity Tracking

Purpose This paper aims to discuss the precise altitude and velocity tracking control of a hypersonic vehicle, a global adaptive neural backstepping controller was studied based on a disturbance observer (DOB). Design/methodology/approach The DOB combined with a radial basis function (RBF) neural network (NN) was used to estimate the disturbance terms that are generated by the flexible modes of the hypersonic vehicle system. A global adaptive neural method was introduced to approximate the unknown system dynamics, with robust control terms pulling the system transient states back into the neural approximation domain externally. Findings The globally uniformly ultimately bounded for all signals of a closed-loop system can be guaranteed by the proposed control algorithm. Additionally, the command filtered backstepping methods can avoid the explosion of the complexity problem caused by the backstepping design process. In addition, the effectiveness of the proposed controller can be verified by the simulation used in this study. Research limitations/implications Normally lateral dynamics issue should be discussed in the process of control system designed, the lateral dynamics are not included in the nonlinear dynamic model of hypersonic vehicle used in this paper, merely the longitudinal flight dynamics are discussed in this paper. Originality/value The flexible states in rigid modes are considered as the disturbance of the system, which is estimated by structuring DOB with NN approximations. The compensating tracking error and prediction error are used in the update law of RBF NN weight. The differential explosions complexity derived from the backstepping procedure is dealt with by using command filters.

scholarly journals Robust Synchronization Criterion for Coupled Stochastic Discrete-Time Neural Networks with Interval Time-Varying Delays, Leakage Delay, and Parameter Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
M. J. Park ◽  
O. M. Kwon ◽  
Ju H. Park ◽  
S. M. Lee ◽  
E. J. Cha
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Leakage Term ◽  
Interval Time ◽  
Robust Synchronization ◽  
Delay Dependent ◽  
Finsler’S Lemma

The purpose of this paper is to investigate a delay-dependent robust synchronization analysis for coupled stochastic discrete-time neural networks with interval time-varying delays in networks coupling, a time delay in leakage term, and parameter uncertainties. Based on the Lyapunov method, a new delay-dependent criterion for the synchronization of the networks is derived in terms of linear matrix inequalities (LMIs) by constructing a suitable Lyapunov-Krasovskii’s functional and utilizing Finsler’s lemma without free-weighting matrices. Two numerical examples are given to illustrate the effectiveness of the proposed methods.

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