Manipulator Trajectory Tracking Based on Kinematics Model Predictive Control

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

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Efficient Nonlinear Model Predictive Control for Quadrotor Trajectory Tracking: Algorithms and Experiment

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2020.3043361 ◽

2021 ◽

pp. 1-12

Author(s):

Dong Wang ◽

Quan Pan ◽

Yang Shi ◽

Jinwen Hu ◽

Chunhui Zhao

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Trajectory Tracking ◽

Nonlinear Model Predictive Control ◽

Tracking Algorithms

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Trajectory Tracking Control of Intelligent Driving Vehicle Based on Model Predictive Control

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327274 ◽

2020 ◽

Author(s):

Lin Qi ◽

Xiaohong Jiao ◽

Zhong Wang

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Tracking ◽

Tracking Control ◽

Trajectory Tracking Control

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An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽

10.3390/machines9050105 ◽

2021 ◽

Vol 9 (5) ◽

pp. 105

Author(s):

Zhenzhong Chu ◽

Da Wang ◽

Fei Meng

Keyword(s):

Model Predictive Control ◽

Prediction Model ◽

Real Time ◽

Predictive Control ◽

Trajectory Tracking ◽

Tracking Control ◽

Working Environment ◽

Gray Wolf ◽

Trajectory Tracking Control ◽

System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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