Research on Foot Trajectory Tracking of Parallel Wheel-legged Robot based on Dynamic Model Predictive Control

2020 ◽  
Author(s):  
Daohe Liu ◽  
Shoukun Wang ◽  
Zhihua Chen ◽  
Junzheng Wang
Keyword(s):  
Model Predictive Control ◽  
Dynamic Model ◽  
Predictive Control ◽  
Time Domain ◽  
Trajectory Tracking ◽  
Parallel Structure ◽  
Legged Robot ◽  
Response Frequency ◽  
Discrete State ◽  
Foot Trajectory

Abstract In this paper, the foot trajectory tracking control for parallel structure of the sixwheel-legged robot is investigated. The accuracy of trajectory tracking and dynamic responsewith heavy load are the main challenges of parallel mechanism. To guarantee the tracking performance and improve dynamic response frequency to posture input, a method based on dynamic model predictive control is proposed under the establishment of dynamic model of single leg. Newton-Eulerian equation is derived and converted into a discrete state space expression for velocity loop control, appropriate parameters including prediction time domain, control time domain and proportional gain are determined by co-simulation. Desired sinusoidal trajectories with different frequencies are tracked with satisfactory performance in terms of accuracy and response frequency. Finally, comparative experimental results using BIT-NAZA robot derived from the proposed control strategy indicate that the delay error and amplitude error are better than PI controller under the same conditions. This research can provide theoretical and engineering guidance for accurate planning of intelligent robot, and facilitate the control performance of wheel-legged robot in practical system.

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

2020 ◽  
pp. 095440702097908
Author(s):  
Mingcong Cao ◽  
Chuan Hu ◽  
Rongrong Wang ◽  
Jinxiang Wang ◽  
Nan Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Collision Probability ◽  
Critical Conditions ◽  
Physical Analysis ◽  
Compensatory Model ◽  
Torque Vectoring ◽  
Post Impact

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.

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

Machines ◽  
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.

Robust Laguerre based model predictive control for trajectory tracking of LTV systems

2021 ◽  
Author(s):  
Marzieh Jamalabadi ◽  
Mahyar Naraghi ◽  
Iman Sharifi ◽  
Elnaz Firouzmand
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking
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