Path tracking controller of an autonomous armoured vehicle using modified Stanley controller optimized with particle swarm optimization

Amer, Noor Hafizah; Zamzuri, Hairi; Hudha, Khisbullah; Aparow, Vimal Rau; Kadir, Zulkiffli Abd; Abidin, Amar Faiz Zainal

doi:10.1007/s40430-017-0945-z

Cited by 31 publications

(30 citation statements)

References 37 publications

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“…Both MPC-based and RSC-based tracking controllers use standard design methods, and specifically, the RSC-based tracking controller design mainly adopts the theory applied in the literature [7] [20]. To carry out a fair comparison of tracking responses with those from MPC, RSC is also based on the linearized vehicle model (10) and the general objective function is as follows…”

Section: Preliminary Knowledge Of Rscmentioning

confidence: 99%

“…For the system depicted by equation (10), it can be found that time-varying parameters , 1 , and are coupled in the system matrices. These parameters need to be processed before carrying out the design of RSC control law.…”

Section: Formulation Of Trajectory Tracking Problemmentioning

confidence: 99%

“…Combined equation (10) and equation (33), the system plant of trajectory tracking system can be rewritten in the polytypic form as: (37) where is the control gain, the key parameters need to be determined. Combined the equations (36) with (37), the closed-loop system can be obtained as…”

Section: Formulation Of Trajectory Tracking Problemmentioning

confidence: 99%

“…In recent years, much research work on trajectory tracking have been studied [8][9][10][11][12][13] and the main challenges of achieving accurate trajectory tracking lie in the following aspects: (1) non-holonomic property and multi-constrains of AGVs; (2) trade-off between vehicle model accuracy and computation efficiency; (3) uncertainties and time-varying parameters of vehicle dynamic model; (4) external disturbances of complex driving environment. Besides, the interaction between road and tires is also an important source of coupling.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Yang

Qin

Huang

et al. 2020

Preprint

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A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.

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Section: Preliminary Knowledge Of Rscmentioning

confidence: 99%

Section: Formulation Of Trajectory Tracking Problemmentioning

confidence: 99%

Section: Formulation Of Trajectory Tracking Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Yang

Qin

Huang

et al. 2020

Preprint

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show abstract

“…Amer et al [9] presented a development and optimization of a proposed path tracking controller for an autonomous armored vehicle and developed a path tracking control based on an established Stanley controller for autonomous vehicles. The basic controller was modified and applied on a non-linear, 7-degrees-of-freedom armored vehicle model, and consisted of various modules such as handling model, tire model, engine, and transmission model.…”

Section: Introductionmentioning

confidence: 99%

Path tracking with minimum time of vehicle handling inverse dynamics based on adaptive gauss pseudospectral method

Liu¹,

Liu²

2019

J. meas. eng.

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Vehicle driving safety is the urgent key problem to be solved by an independent automobile development project. And it is also the premise and one of the necessary conditions for active vehicle safety. A new technique for path tracking with minimum time of vehicle handling inverse dynamics is proposed in this paper. Based on a new optimal control method-Adaptive Gauss Pseudospectral Method (AGPM), the optimal control for path tracking with minimum time problem was converted into a nonlinear programming problem which met the boundary condition and a series of path constraints including path constraint, state constraint, control constraint. And then the problem was solved using the sequential quadratic programming (SQP). The simulation results showed that the proposed method was not sensitive to the initial value and the optimization efficiency was higher compared with indirect methods and traditional direct methods. When solving the problem of path tracking with minimum time as per this method, boundary constraints and path constraints were well satisfied. The algorithm was not only precise but it could also shorten the evaluation period of vehicle handling stability and could reduce the tremendous cost for real vehicle testing. So the maneuverability of two different vehicles that complete the pylon course slalom test road with minimum time can be evaluated objectively by utilizing the method. The model correctness is verified through a real vehicle test.

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Research on Trajectory Tracking Control of Driverless Vehicles

Fang,

Yu,

Zeng

et al. 2024

Lecture Notes in Electrical Engineering

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Path tracking controller of an autonomous armoured vehicle using modified Stanley controller optimized with particle swarm optimization

Cited by 31 publications

References 37 publications

Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Path tracking with minimum time of vehicle handling inverse dynamics based on adaptive gauss pseudospectral method

Research on Trajectory Tracking Control of Driverless Vehicles

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