An improved predictive algorithm for time delay compensation in UAVs

Thurling, Andrew J.; Greene, Kelly E.

doi:10.2514/6.2001-4424

Cited by 5 publications

(3 citation statements)

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“…To predict the state parameter precisely, different predictive methods have been investigated, such as the Smith predictive method [9][10][11], a Kalman estimator [12,13], feed-forward control [14], reference model based control [15,16], and a state space observer approach [17,18]. Thurling and Greene [19] gave a detailed description of the composition of the delay of a low speed unmanned aerial vehicle (UAV) remotely piloted system and analyzed the result of time delay impacting on the handling qualities; they described how the greater time delay could cause the pilot to over-operate the UAV and even lead to UAV instability. Based on the deterministic linear model and the deterministic delay, the delayed UAV flight state is predicted by solving the state equation.…”

Section: Introductionmentioning

confidence: 99%

“…There have been many researches on time delay prediction, including deterministic objects, objects with parameter uncertainties, fixed delay time and random delay time systems. However, all of these studies [19][20][21][22] can be classified as networked control problems of low dynamic objects. That is to say, the operator sends the control command directly to the remote actuator of the passive object through the WSN.…”

Section: Introductionmentioning

confidence: 99%

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Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Fang

Fan

et al. 2020

Sensors

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Remotely piloted unmanned combat aerial vehicle (UCAV) will be a prospective mode of air fight in the future, which can remove the physical restraint of the pilot, maximize the performance of the fighter and effectively reduce casualties. However, it has two difficulties in this mode: (1) There is greater time delay in the network of pilot-wireless sensor-UCAV, which can degrade the piloting performance. (2) Designing of a universal predictive method is very important to pilot different UCAVs remotely, even if the model of the control augmentation system of the UCAV is totally unknown. Considering these two issues, this paper proposes a novel universal modeling method, and establishes a universal nonlinear uncertain model which uses the pilot’s remotely piloted command as input and the states of the UCAV with a control augmentation system as output. To deal with the nonlinear uncertainty of the model, a neural network observer is proposed to identify the nonlinear dynamics model online. Meanwhile, to guarantee the stability of the overall observer system, an adaptive law is designed to adjust the neural network weights. To solve the greater transmission time delay existing in the pilot-wireless sensor-UCAV closed-loop system, a time-varying delay state predictor is designed based on the identified nonlinear dynamics model to predict the time delay states. Moreover, the overall observer-predictor system is proved to be uniformly ultimately bounded (UUB). Finally, two simulations verify the effectiveness and universality of the proposed method. The results indicate that the proposed method has desirable performance of accurately compensating the time delay and has universality of remotely piloting two different UCAVs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Fang

Fan

et al. 2020

Sensors

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“…To deal with the pure time delay system with state feedback, literatures [38] and [39] analyze the influence of the data link delay on the handling qualities of remotely piloted UAV, and a model-based predictor algorithm is used to improve UAV handling qualities by providing a ''quickened display'' to help the pilot compensate for its increased system time delay. The predictive display is found to substantially decrease pilot workload and improve Cooper-Harper ratings.…”

Section: Introductionmentioning

confidence: 99%

Prediction-Based Adaptive Sliding Mode Control for Remotely Piloted System With Time Delay and Parameter Uncertainty

Fan

et al. 2019

IEEE Access

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This paper proposes a state prediction adaptive sliding mode (SPASM) control method for the remotely piloted system (RPS). With consideration of the time delay caused by the large transmission delay existing in the RPS, a prediction algorithm is proposed to provide the state prediction by using the state transition matrix. To approximate the uncertain lag of the remotely piloted vehicle (RPV) control augmentation system, an adaptive law is proposed to estimate the parameter uncertainties, and the overestimating problem is resolved efficiently. Meanwhile, to deal with the unmodeled dynamics and the predicted errors, a sliding mode controller is designed to guarantee the robustness of the whole closed-loop system. The simulation results show that the SPASM controller can not only guarantee the stability of the RPS in the presence of large time delay but also has a desirable performance of tracking the pilot's inputs while existing the unmodeled dynamics and parameter uncertainties.INDEX TERMS Remotely piloted system (RPS), remotely piloted vehicle (RPV), time delay, state prediction, adaptive sliding mode control.

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Design of time delayed control systems in UAV using model based predictive algorithm

Zhu

Zhou

2010

2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)

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An improved predictive algorithm for time delay compensation in UAVs

Cited by 5 publications

References 1 publication

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Prediction-Based Adaptive Sliding Mode Control for Remotely Piloted System With Time Delay and Parameter Uncertainty

Design of time delayed control systems in UAV using model based predictive algorithm

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