A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

We present an adaptive hierarchical sliding mode control based on fuzzy neural network for a class of underactuated systems to solve the problem of high-precision trajectory tracking. This system is viewed as several subsystems. One subsystem is used to design the first-layer sliding surface, which constructs the second-layer sliding surface with another subsystem. When the top layer includes all the subsystems, the design process is finished. Meanwhile, the equivalent control law and the switching control law are achieved at every layer. Because the hierarchical sliding mode control law relies excessively on the requirement of detailed information of the underactuated dynamic system, and because that method causes an inevitable chattering phenomenon, an online fuzzy neural network system is applied to mimic the hierarchical sliding mode control law. Moreover, the bounds of system uncertainties and modeling error caused by the fuzzy neural network system are estimated online by a robust term. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and Barbalat's Lemma. Finally, the examples, a single-pendulum-type overhead crane system and an inverted pendulum system, are simulated to verify the effectiveness and robustness of the proposed method compared with some conventional methods.

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“…According to equation (35), V (S(0),W ,m,c,Q(0)) is a bounded value and V (S(t),W ,m,c,Q(t)) is a nonincreasing and bounded function. Therefore…”

Section: Afnnhsmcmentioning

confidence: 99%

Adaptive hierarchical sliding mode control based on fuzzy neural network for an underactuated system

Huang

Gao

Ralescu

et al. 2018

Advances in Mechanical Engineering

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“…To derive the proper model, some reasonable assumptions are applied as follows: [27][28][29] 1. Ignore the influence of pitch and yaw motion.…”

Section: Lane Change Process and Vehicle Kinematics Modelmentioning

confidence: 99%

Model predictive control–based cooperative lane change strategy for improving traffic flow

Wang

et al. 2016

Advances in Mechanical Engineering

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Lane change has attracted more and more attention in recent years for its negative impact on traffic safety and efficiency. However, few researches addressed the multi-vehicle cooperation during lane change process. In this article, feasibility criteria of lane change are designed, which considers the acceptable acceleration/deceleration of neighboring vehicles; meanwhile, a cooperative lane change strategy based on model predictive control is proposed in order to attenuate the adverse impacts of lane change on traffic flow. The proposed strategy implements the centralized decision making and active cooperation among the subject vehicle performing lane change in the subject lane and the preceding vehicle and the following vehicle in the target lane during lane change. Using model predictive control, safety, comfort, and traffic efficiency are integrated as the objectives, and lane change process is optimized. Numerical simulation results of the cooperative lane change strategy suggest that the deceleration of following vehicle can be weakened and further the shock wave propagated in traffic flow can be alleviated to some degree compared with traditional lane change.

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“…In the past 10 years, the researchers have employed different control rules including sliding mode control (SMC), [3][4][5][6][7][8][9][10][11][12] HN robust control, 13,14 model predictive control (MPC), [15][16][17][18][19][20] fuzzy control, [21][22][23][24][25][26] backstepping, 27,28 adaptive control, [29][30][31] proportional-integralderivative (PID) controllers, [32][33][34][35] linear-quadratic regulator (LQR), 22,36,37 optimization algorithms 38,39 and solution of linear matrix inequalities (LMI) 40 to design controllers. Sliding mode control as a non-linear control plays an important role against different friction changes of road and different velocities in the presence of parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…Sliding mode control as a non-linear control plays an important role against different friction changes of road and different velocities in the presence of parameter uncertainties. 4 Sliding mode control can be combined with different control laws including fuzzy control rules, 41 adaptive, 6 fuzzy neural networks, 8 backstepping 28 and other control law and optimization algorithms. Li et al 41 combined fuzzy control rules and sliding mode and generated the required steering of the produced maneuver.…”

Section: Introductionmentioning

confidence: 99%

Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer

Norouzi

Kazemi

Azadi

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Recent researches on advanced driver-assistance systems indicate great advances in terms of safety and comfort in automated driving. Advanced driver-assistance systems use control systems to perform most of the maneuvers as performed by the driver in the past. One of the useful advanced driver-assistance systems is automatic lane change system in order to avoid accidents. This study designs the controller of an automatic lane change system for an autonomous vehicle. The control law in this study is adaptive sliding mode control. To avoid chattering in adaptive sliding mode control, fuzzy boundary layer is used. Also, adaptive law is used for sliding-based switching gain. This adaptive controlling law is used to avoid the calculation of upper bound of system uncertainties. In this study, based on the boundary conditions, the vehicle lane change path planning and different maneuver periods are evaluated. To simulate the designed controller, CarSim-Simulink joint simulation model is used. This linkage leads to a full non-linear vehicle model. The results of simulation show excellent tracking for dry road conditions and acceptable tracking in icy and wet roads in some maneuvers of above 4-s long.

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A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

Cited by 12 publications

References 16 publications

Adaptive hierarchical sliding mode control based on fuzzy neural network for an underactuated system

Adaptive hierarchical sliding mode control based on fuzzy neural network for an underactuated system

Model predictive control–based cooperative lane change strategy for improving traffic flow

Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer

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