Collision avoidance system using state dependent Riccati equation technique: An experimental robustness evaluation

Alirezaei, Mohsen; Jansen, S.T.H.; Schmeitz, A.J.C.; Madhusudhanan, Anil Kunnappillil

doi:10.1201/9781315265285-21

Cited by 9 publications

(10 citation statements)

References 1 publication

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“…The components of the tire slip are calculated by expressions, which include linear and angular velocities calculated by the equation system (1). These expressions are omitted here and can be found in [14].…”

Section: Model Of Vehicle Dynamicsmentioning

confidence: 99%

“…The first two regulator types are derived from models of controlled plants. In application to automated vehicle driving, these regulators, being accompanied by rather advanced and complex observers of vehicle dynamic parameters, show good performance even in emergency maneuvers at low adhesion surfaces [1,2]. LQR and MPC require parameters of the controlled plant to be known (identified).…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of the Sliding Mode Control for Automated Vehicle Path Tracking at Low Adhesion Surfaces

Kulikov¹,

Ulchenko²

2019

MATEC Web Conf.

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The article analyses prospects of using a type of robust controllers called relay regulators for automation of vehicle lateral motion. The operation of these regulators in so-called sliding modes is considered along with the “chattering” problem caused by deviations from the “ideal” sliding mode inevitable in actual implementations. For the analysis of vehicle motion, a mathematical model was elaborated, which calculates vehicle dynamics taking into account non-linear tire-road adhesion characteristics. In the conducted study, emphasis was put on low adhesion surfaces, which can be considered as the most difficult case for automatic lateral control of a vehicle. In order to implement automated path tracking within the model, two relay regulators were elaborated differing from one another in the order of dynamics. A comparative study of these regulators was conducted by means of simulations. The regulator that had shown best performance was then tested for robustness by means of modeling, in which maneuvers on snow, ice and a mixed surface were simulated.

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Section: Model Of Vehicle Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of the Sliding Mode Control for Automated Vehicle Path Tracking at Low Adhesion Surfaces

Kulikov¹,

Ulchenko²

2019

MATEC Web Conf.

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“…In the literature on the automated path-tracking control, a number of adaptive control techniques are described and proposed including scheduled gain PID regulators [17], model-based optimal regulators [18], fuzzy logic [19], and sliding mode regulators [20]. Selection of the regulator type for using in this work can be found in [21].…”

Section: Path-tracking Control Systemmentioning

confidence: 99%

Using Real World Data in Virtual Development and Testing of a Path Tracking Controller for an Autonomous Vehicle

Kulikov*¹,

Ulchenko²,

Chaplygin³

2019

IJITEE

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The article describes an approach to development and testing of a path tracking function for an autonomous vehicle. The essence of the approach consists in combining experimental data and mathematical modeling in order to simulate operation of a path-tracking regulator in real world maneuvers. The procedure can be divided into two stages. The first one implies field-testing of the vehicle under control of a human driver with logging of the essential dynamic variables including the driving trajectory. Then the obtained data is used to validate the model of vehicle dynamics being a tool for further simulations. At the second stage, a simulation is performed with tracking of the previously logged trajectory by an automatic regulator. The results of these steps allow for comparison between the human and automatic controls with assessment of pros and cons of the latter and the ways of improving its performance. The proposed approach was implemented within a research and development project aimed at building of an experimental autonomous vehicle. The article describes the obtained results as well as the experiments and the mathematical model used for implementation of the said approach.

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“…which results in A(x(t)) = 0.1x(t),dA(x(t))/dx(t) = 0.1, B = À 1, dB/dx(t) = 0, R = 0.1, Q = 10, F = 10. Substituting parameters in (9), a nonlinear PDE is found (21) will be subjected to changes based on FDM and the MOL in (13) to (15), to finally achieve the difference equation (16). Time of simulation was set t f = 1s, a = 1, n x = 0.01, n t = 0.00005, and r = 0.5.…”

Section: Scalar Systemmentioning

confidence: 99%

“…• The first point is that (15) must be substituted in (13) to approximate time derivative of gain. • Counters in the loops start from 1, though the state is bounded between a negative and a positive value.…”

Section: Appendix Amentioning

confidence: 99%

A PDE breach to the SDRE

Nekoo¹

2018

Asian Journal of Control

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The state‐dependent Riccati equation (SDRE) is a nonlinear optimal controller derived from applying optimality conditions on a Hamiltonian equation. A co‐state vector is involved in the derivation process. This has been commonly considered a function of time only, despite the existence of states in the co‐state vector. This has resulted in a series of nonlinear coupled ordinary differential equations (ODEs) with a final boundary condition, known as the SDRE. In this work, for the first time, the co‐state vector is regarded as a function of time and states that results in a partial differential equation (PDE) instead of an ODE. The new governing equation is named partial differential state‐dependent Riccati equation (PDSDRE), and the PDE provides a tensor for gain over domains of time and states. Since the generated PDE is highly nonlinear, the solution to the PDSDRE is proposed based on the method of lines (MOL), which is an extension to the finite difference method (FDM). The proposed approach is implemented on both scalar and second order systems and is compared with an SDRE technique to validate the results and show the advantages of proposed structure.

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Collision avoidance system using state dependent Riccati equation technique: An experimental robustness evaluation

Cited by 9 publications

References 1 publication

Performance Analysis of the Sliding Mode Control for Automated Vehicle Path Tracking at Low Adhesion Surfaces

Performance Analysis of the Sliding Mode Control for Automated Vehicle Path Tracking at Low Adhesion Surfaces

Using Real World Data in Virtual Development and Testing of a Path Tracking Controller for an Autonomous Vehicle

A PDE breach to the SDRE

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