Autonomous vehicle control using a kinematic Lyapunov-based technique with LQR-LMI tuning

Alcalá, Eugenio; Puig, Vicenç; Quevedo, Joseba; Escobet, Teresa; Comasolivas, Ramon

doi:10.1016/j.conengprac.2017.12.004

Cited by 104 publications

(56 citation statements)

References 16 publications

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“…A linear quadratic regulator (LQR) has been used to design a controller for autonomous vehicles in the steady state [28,29]. Alcala et al [30] proposed an LQR controller, which shows more stable performance than PI and PD control in terms of the ability of an LQR to suppress overshoot. Chuan et al [31] constrained the sideslip angle in the reasonable region by using a robust LQR controller, which maintains the vehicle stability through the path following process.…”

Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

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Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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“…Denoting the state, control and reference vectors, respectively, as [24]) is transformed into the Takagi-Sugeno representation by using the sector nonlinearity approach…”

Section: Kinematic Ts Modelmentioning

confidence: 99%

TS-MPC for Autonomous Vehicles Including a TS-MHE-UIO Estimator

Alcalá

Puig

Quevedo

2019

IEEE Trans. Veh. Technol.

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In this work, a novel approach is presented to solve the problem of tracking trajectories in autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno -Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno -Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi Sugeno estimator -Moving Horizon Estimator -Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 40-50 times. To demonstrate the potential of the TS-MPC we propose a comparison between three methods of solving the kinematic control problem: using the non-linear MPC formulation (NL-MPC), using TS-MPC without updating the prediction model and using updated TS-MPC with the references of the planner.

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“…Alcala et al [37] presented the control of an autonomous vehicle using a Lyapunov-based technique with a LQR-LMI tuning. They could apply a non-linear control strategy based on Lyapunov theory for solving the autonomous guidance control problem.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, there are some novel technologies that would be applicable to the problem such as conversion of CO 2 into clean fuels, autonomous vehicle control and so on [32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

et al. 2018

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Greenhouse gases (GHG) are the main reason for the global warming during the past decades. On the other hand, establishing a well-structured transportation system will yield to create least cost-pollution. This paper addresses a novel model for the multi-trip Green Capacitated Arc Routing Problem (G-CARP) with the aim of minimizing total cost including the cost of generation and emission of greenhouse gases, the cost of vehicle usage and routing cost. The cost of generation and emission of greenhouse gases is based on the calculation of the amount of carbon dioxide emitted from vehicles, which depends on such factors as the vehicle speed, weather conditions, load on the vehicle and traveled distance. The main applications of this problem are in municipalities for urban waste collection, road surface marking and so forth. Due to NP-hardness of the problem, a Hybrid Genetic Algorithm (HGA) is developed, wherein a heuristic and simulated annealing algorithm are applied to generate initial solutions and a Genetic Algorithm (GA) is then used to generate the best possible solution. The obtained numerical results indicate that the proposed algorithm could present desirable performance within a suitable computational run time. Finally, a sensitivity analysis is implemented on the maximum available time of the vehicles in order to determine the optimal policy.

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Autonomous vehicle control using a kinematic Lyapunov-based technique with LQR-LMI tuning

Cited by 104 publications

References 16 publications

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

TS-MPC for Autonomous Vehicles Including a TS-MHE-UIO Estimator

A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

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