Neural network approach to trajectory synthesis for robotic manipulators

Pashkevich, Anatol; Kazheunikau, M.

doi:10.1007/s10845-004-5887-5

Cited by 9 publications

(1 citation statement)

References 17 publications

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“…Neural network algorithm, genetic algorithm, ant colony algorithm, etc., need a lot of iterative operations, and the current vehicle hardware conditions are difficult to meet the requirements. [21][22][23][24] Path planning based on geometric model includes cosine trajectory, circular trajectory, isokinetic trajectory, trapezoidal acceleration trajectory, polynomial trajectory. 15,25 These trajectory models are intuitive, accurate, and have a small amount of calculation.…”

Section: Introductionmentioning

confidence: 99%

Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment

Wang

Fei

et al. 2021

Advances in Mechanical Engineering

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In order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles was constructed. Based on the vehicle model and the model predictive control theory, the time-varying linear variable path vehicle predictive model was derived by combining soft constraint of the side slip angle. Combined with fuzzy control algorithm, the weight coefficient of the deviation of the lateral displacement was dynamically adjusted. Finally, the FMPC (model predictive controller based on fuzzy control) and MPC controller were compared and analyzed by co-simulation of CarSim and Simulink under different speeds. The simulation results show that the designed FMPC controller can track the lane change trajectory better, and the controller has better robustness when the vehicle changes lanes at different speeds.

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