scholarly journals Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance

2020 ◽  
Vol 10 (11) ◽  
pp. 3709 ◽  
Author(s):  
Fei Che ◽  
Yifeng Niu ◽  
Jie Li ◽  
Lizhen Wu
Keyword(s):  
Vector Field ◽  
Control Method ◽  
Unscented Kalman Filter ◽  
Convergence Time ◽  
Gradient Descent Method ◽  
Multiple Model ◽  
Moving Targets ◽  
Standoff Tracking ◽  
Lyapunov Vector ◽  
Vector Field Guidance

Cooperative standoff tracking of moving targets is an important application of fixed-wing unmanned aerial vehicles (UAVs). To cope with the problem of long convergence time and unstable tracking in cooperative target tracking, traditional Lyapunov vector field guidance (LVFG) is modified. The guidance parameter c is discussed, and the gradient descent method is utilized to develop the optimal guidance parameter search algorithm. As for tracking moving targets, an interacting multiple model-based unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state, and the result is used for correcting the guidance law. Meanwhile, a speed-based controller is developed for faster convergence to the desired intervehicle phase, and the stability of the controller is proved using the Lyapunov stability theory. Numerical simulation results indicate the proposed guidance converges faster to the standoff circle without intersecting the orbit. The state estimator reduces the estimate error and the intervehicle phase converges faster to the desired phase than the traditional control method. Furthermore, extensive hardware-in-the-loop simulations are carried out to verify the feasibility of the algorithm.

scholarly journals Fast Lyapunov Vector Field Guidance for Standoff Target Tracking Based on Offline Search

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 124797-124808 ◽  
Author(s):  
Shun Sun ◽  
Haipeng Wang ◽  
Jun Liu ◽  
You He
Keyword(s):  
Vector Field ◽  
Target Tracking ◽  
Lyapunov Vector ◽  
Vector Field Guidance

Robust adaptive tracking control for uncertain nonholonomic mobile manipulator

2021 ◽  
pp. 095965182110277
Author(s):  
Abdelkrim Brahmi ◽  
Maarouf Saad ◽  
Brahim Brahmi ◽  
Ibrahim El Bojairami ◽  
Guy Gauthier ◽  
...  
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Robust Adaptive Control ◽  
Convergence Time ◽  
Adaptive Sliding Mode Control ◽  
Mobile Manipulator ◽  
Control Law ◽  
Adaptive Tracking Control ◽  
Robust Adaptive ◽  
Manipulator Robot

In the research put forth, a robust adaptive control method for a nonholonomic mobile manipulator robot, with unknown inertia parameters and disturbances, was proposed. First, the description of the robot’s dynamics model was developed. Thereafter, a novel adaptive sliding mode control was designed, to which all parameters describing involved uncertainties and disturbances were estimated by the adaptive update technique. The proposed control ensures a relatively good system tracking, with all errors converging to zero. Unlike conventional sliding mode controls, the suggested is able to achieve superb performance, without resulting in any chattering problems, along with an extremely fast system trajectories convergence time to equilibrium. The aforementioned characteristics were attainable upon using an innovative reaching law based on potential functions. Furthermore, the Lyapunov approach was used to design the control law and to conduct a global stability analysis. Finally, experimental results and comparative study collected via a 05-DoF mobile manipulator robot, to track a given trajectory, showing the superior efficiency of the proposed control law.

scholarly journals Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 607
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang ◽  
Guimei Cui
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Support Vector ◽  
Multiple Model ◽  
Single Model ◽  
Adaptive Unscented Kalman Filter ◽  
Time Periods ◽  
Atmospheric Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

The PM2.5 concentration model is the key to predict PM2.5 concentration. During the prediction of atmospheric PM2.5 concentration based on prediction model, the prediction model of PM2.5 concentration cannot be usually accurately described. For the PM2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM2.5 concentration prediction is improved in whole time period.

Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation*

2001 ◽  
Vol 124 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle
Keyword(s):  
Neural Network ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Equivalent Model ◽  
Gradient Descent Method ◽  
Nonlinear Simulation ◽  
Control Approach ◽  
Active Noise ◽  
Nonlinear Noise

The nonlinear active noise control (ANC) is studied. The nonlinear ANC system is approximated by an equivalent model composed of a simple linear sub-model plus a nonlinear sub-model. Feedforward neural networks are selected to approximate the nonlinear sub-model. An adaptive active nonlinear noise control approach using a neural network enhancement is derived, and a simplified neural network control approach is proposed. The feedforward compensation and output error feedback technology are utilized in the controller designing. The on-line learning algorithm based on the error gradient descent method is proposed, and local stability of closed loop system is proved based on the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise control method based on neural network compensation is very effective to the nonlinear noise control, and the convergence of the NNEH control is superior to that of the NN control.

scholarly journals Congestion Control Based on Multiple Model Adaptive Control

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xinhao Yang ◽  
Ze Li
Keyword(s):  
Adaptive Control ◽  
Congestion Control ◽  
Output Feedback ◽  
Control Method ◽  
Feedback Controller ◽  
Multiple Model ◽  
Network Condition ◽  
Output Feedback Controller ◽  
Weight Calculation ◽  
Multiple Model Adaptive Control

The congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is adopted in our congestion control. The multiple model adaptive control is introduced in this paper based on the weight calculation instead of the parameter estimation in past adaptive control. The model set is composed by the dynamic model based on the fluid flow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H2output feedback controller, and proportional-integral controller, respectively. Ns-2 simulation results in section 4 indicate that the proposed algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop ratio.

Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model

2017 ◽  
Vol 32 (04) ◽  
pp. 1850010
Author(s):  
Yi Pan ◽  
Hui Ye ◽  
Keke He
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
System Parameter ◽  
Unscented Kalman Filter ◽  
Continuous System ◽  
The State ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Real Time System ◽  
Spherical Simplex

A modified interacting multiple model (IMM) method called spherical simplex unscented Kalman filter-based jumping and static IMM (SSUKF-JSIMM) is proposed to solve the problem of nonlinear filtering with unknown continuous system parameter. SSUKF-JSIMM regards the continuous system parameter space as a union of disjoint regions, and each region is assigned to a model. For each model, under the assumption that the parameter belongs to the corresponding region, one sub-filter is used to estimate the parameter and the state when the parameter is presumed to be jumping, and another sub-filter is used to estimate the parameter and the state when the parameter is presumed to be static. Considering that spherical simplex unscented Kalman filter (SSUKF) is more suitable for a real-time system than the unscented Kalman filter (UKF), SSUKFs are adopted as the sub-filters of SSUKF-JSIMM. Results of the two SSUKFs are fused as the estimation output of the model. Experimental results show that SSUKF-JSIMM achieves higher performance than IMM, SIR, and UKF in bearings-only tracking problem.

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