Model‐free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft

Liu, Shida; Hou, Zhongsheng; Zhang, Xin; Ji, Honghai

doi:10.1049/iet-cta.2020.0073

Cited by 41 publications

(18 citation statements)

References 33 publications

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“…For the complex environment in the non‐linear system, the generated non‐Gaussian noise filtering needs to be taken into consideration [22]. As a result, the Gaussian‐sum method is introduced into SRCKF as a non‐linear filtering method [16].…”

Section: Gaussian‐sum Methodsmentioning

confidence: 99%

Aircraft trajectory filtering method based on Gaussian‐sum and maximum correntropy square‐root cubature Kalman filter

Bai

et al. 2022

Cognitive Comp and Systems

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Aiming at meetiing the need to filtering flight trajectory data for aircraft testing, a novel adaptive cubature Kalman filter (CKF) is proposed based on the maximum correntropy and Gaussian-sum in this paper. Firstly, based on the traditional CKF algorithm, we introduced a Gaussian-sum method to approximate non-Gaussian noise to get more accurate filtering results in view of the problem of reduced filtering accuracy caused by the inherent non-Gaussian nature of the noise and the system non-linearity. Secondly, the maximum correntropy criterion is introduced to solve further the problem of improving the filtering accuracy of the system in the case of non-linearity. Simulation results and actual data verification showed that the Square-root cubature Kalman filter algorithm based on the maximum correntropy and Gaussian-sum has higher accuracy than traditional filtering algorithms, which verified the algorithm's effectiveness in the application.

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Section: Gaussian‐sum Methodsmentioning

confidence: 99%

Aircraft trajectory filtering method based on Gaussian‐sum and maximum correntropy square‐root cubature Kalman filter

Bai

et al. 2022

Cognitive Comp and Systems

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“…Motivated by the above issue, the problem of compact-form MFAC, abbreviated as CFMFAC and developed by Hou and Jin [13], for MIMO systems with different characteristics between control channels is first considered in this work. It should be noted that researches on the CFMFAC for MIMO systems inevitably encounter this problem and thus they mainly focus on controlling a class of systems with similar channel characteristics [8,13,21,22]. In Liu et al [21] and Xiong et al [22], robust CFMFAC takes into account the same measurement disturbance for different control channels, and Liu et al [8] propose an enhanced CFMFAC considering the time delay with the same value for different channels, where the common restriction is essentially caused by the fixed topology framework that inherently existed in the prototype CFMFAC.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that researches on the CFMFAC for MIMO systems inevitably encounter this problem and thus they mainly focus on controlling a class of systems with similar channel characteristics [8,13,21,22]. In Liu et al [21] and Xiong et al [22], robust CFMFAC takes into account the same measurement disturbance for different control channels, and Liu et al [8] propose an enhanced CFMFAC considering the time delay with the same value for different channels, where the common restriction is essentially caused by the fixed topology framework that inherently existed in the prototype CFMFAC. However, as far as the authors know, there have been no studies on the CFMFAC aspect about different characteristics between control channels for MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

Different‐factor compact‐form model‐free adaptive control with neural networks for MIMO nonlinear systems

Chen

2021

Asian Journal of Control

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In this paper, a different-factor structure-based compact-form model-free adaptive control method with neural networks (DF-CFMFAC-NN) is proposed for a class of general multiple-input and multiple-output (MIMO) nonlinear systems. Its novelty lies in that it is a pure data-driven control method using merely input/output data without any model information involved. Moreover, by virtue of the different-factor structure, it addresses the problem in the prototype CFMFAC that mainly deals with a class of MIMO systems with similar channel characteristics under a fixed topology. Aiming at different characteristics between control channels, widely existing in MIMO systems, DF-CFMFAC-NN with learning parameters shows powerful tracking ability and flexible design for each control channel, and learning parameters are auto-tuned by back propagation neural networks (BPNNs) online based on their self-learning and self-adapting properties. In the tuning process, a one-step-ahead partial derivative is directly derived by the dynamic linearization technique utilized in DF-CFMFAC, which greatly improves the prediction accuracy of parameters.The convergence of tracking error and the stability of the tuning process are guaranteed by rigorous theoretical analysis. A coal mill system is provided to demonstrate its effectiveness and practicability, demonstrating it to be a promising control method for MIMO nonlinear systems.

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“…Current data-driven control technology is widely used in machinery manufacturing [ 28 ], motor control [ 29 ], transportation [ 30 ], and chemical production control [ 31 ], etc. In the field of unmanned aerial vehicles control, it is still less; reference [ 32 ] proposed a novel robust model-free adaptive control (Ro-MFAC) algorithm for quadrotor control with a class of unknown multiple-input multiple-output (MIMO) systems with measurement noise, but it is only for quadrotor attitude control. Reference [ 33 ] designed an iterative learning method, using a double-layer formation control system, and applied it to UAV formation, but it has a large amount of calculation and cannot meet the requirements of real-time performance.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Model-Free Adaptive Control Method for Quadrotor Formation Trajectory Tracking Based on RISE and ISMC Algorithm

Yuan

Wang

2021

Sensors

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In order to solve the problems of complex dynamic modeling and parameters identification of quadrotor formation cooperative trajectory tracking control, this paper proposes a data-driven model-free adaptive control method for quadrotor formation based on robust integral of the signum of the error (RISE) and improved sliding mode control (ISMC). The leader-follower strategy is adopted, and the leader realizes trajectory tracking control. A novel asymptotic tracking data-driven controller of quadrotor is used to control the system using the RISE method. It is divided into two parts: The inner loop is for attitude control and the outer loop for position control. Both use the RISE method in the loop to eliminate interference and this method only uses the input and output data of the unmanned aerial vehicle(UAV) system and does not rely on any dynamics and kinematics model of the UAV. The followers realize formation cooperative control, introducing adaptive update law and saturation function to improve sliding mode control (SMC), and it eliminates the general SMC algorithm controller design dependence on the mathematical model of the UAV and has the chattering problem. Then, the stability of the system is proved by the Lyapunov method, and the effectiveness of the algorithm and the feasibility of the scheme are verified by numerical simulation. The experimental results show that the designed data-driven model-free adaptive control method for the quadrotor formation is effective and can effectively realize the coordinated formation trajectory tracking control of the quadrotor. At the same time, the design of the controller does not depend on the UAV kinematics and dynamics model, and it has high control accuracy, stability, and robustness.

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Model‐free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft

Cited by 41 publications

References 33 publications

Aircraft trajectory filtering method based on Gaussian‐sum and maximum correntropy square‐root cubature Kalman filter

Aircraft trajectory filtering method based on Gaussian‐sum and maximum correntropy square‐root cubature Kalman filter

Different‐factor compact‐form model‐free adaptive control with neural networks for MIMO nonlinear systems

Data Driven Model-Free Adaptive Control Method for Quadrotor Formation Trajectory Tracking Based on RISE and ISMC Algorithm

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