Path tracking of an autonomous vehicle by means of an indirect adaptive neural controller

Amirkhani, Abdollah; Shirzadeh, Masoud; Tork, Nastaran; Shokouhi, Shahriar B.

doi:10.1109/icrom48714.2019.9071897

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…In this paper, a ST2-RBFbased approach has been suggested for improving the system performance and reducing the computational cost. Table III compares the performances of the proposed method with other well-known control techniques, including multi-layer perceptron (MLP) [53], RBF [45], T1-FLS, interval T2-FLS (IT2-FLS) [22], as well as VGSMC [1] and FPID-BS [47] methods. It is evident from the table that the proposed approach outperforms the other control techniques, yielding more significant and favorable results.…”

Section: Comparison and Discussionmentioning

confidence: 99%

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Automotive Electric Power Steering Control With Robust Observer Based Neuroadaptive Type-2 Radial Basis Function Methodology

Amirkhani,

Shirzadeh,

Heydari

2024

IEEE Open J. Veh. Technol.

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In this paper, a simplified type-2 (ST2) radial basis function (RBF) based neuroadaptive technique for controlling an automotive electric power steering (AEPS) system is designed. The dynamics of the AEPS are assumed to be unknown and the system is subjected to certain disturbances. A ST2-RBF system is proposed for approximating the unknown nonlinear functions. The ST2-RBF parameters are tuned online based on the adaptation laws obtained via Lyapunov stability analysis. A robust observer is also used in this process. The effects of uncertainties as well as approximation and estimation errors are compensated by means of an adaptive component. The parameters of the robust observer-based neuroadaptive ST2-RBF network are optimally determined by applying the Coronavirus disease optimization algorithm (COVIDOA), which mimics the replication mechanism of Coronaviruses taking over the human cells. The results indicate that the COVIDOA can reduce the cost function for neuroadaptive ST2-RBF controller compared to other strategies. Comparison of numerical results is presented to show the efficacy of the suggested technique. Interestingly, based on implementation results, the designed methodology is able to control the AEPS system successfully.

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Section: Comparison and Discussionmentioning

confidence: 99%

“….., H jN  respectively denote the weights associated with the upper and the lower parts of activation functions. Using the ST2-RBF, the approximation error is defined as [22], [53]:…”

Section: Type-2 Radial Basis Functionmentioning

confidence: 99%

Automotive Electric Power Steering Control With Robust Observer Based Neuroadaptive Type-2 Radial Basis Function Methodology

Amirkhani,

Shirzadeh,

Heydari

2024

IEEE Open J. Veh. Technol.

Self Cite

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“…In an actual system, f Rv and f R0 are initially zero but are affected by the environmental disturbances and model uncertainties, and k i is affected by the model uncertainties, including the change in vehicle mass and control gains. Therefore, (17) can be rewritten as follows:…”

Section: Upper-level Model-predictive Control Lawmentioning

confidence: 99%

“…Fuzzy logic and neural network methodologies were implemented to design the switching control gain and uncertainty bound estimation. 16,17 A novel ASMC strategy using a radial basis function neural network (RBFNN) has been presented. [18][19][20] The RBFNN shows good estimation performance and robustness to the sensor noise with smaller computational efforts.…”

Section: Introductionmentioning

confidence: 99%

Model-reference adaptive sliding mode control of longitudinal speed tracking for autonomous vehicles

Lee

Seo

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a longitudinal speed control algorithm using a model-reference adaptive sliding mode control (ASMC) scheme for an autonomous vehicle in various driving environments using only wheel speed sensors. The proposed algorithm could control the vehicle’s speed not using parameter estimators but using an adaptation technique. The parameter adaptation laws were designed to compensate for the changes in the environmental disturbances and model uncertainties. Moreover, the upper bound of unknown disturbances, that were not compensated by the adaptation algorithm, was estimated using radial basis function neural network (RBFNN). The sliding mode controller updated the upper bound from the RBFNN and obtained robustness without knowing the bound in advance. Adaptive equivalent control input was also defined to compensate for zero-throttle acceleration varying with speed. This input could enhance the mode switch smoothly between throttle and brake control. We conducted computer simulations and vehicle tests under various driving environments to evaluate the performance of the proposed algorithm. In the simulation result, the average tracking error of the proposed algorithm was 0.718 kph, and the maximum change rate of the error due to the disturbances was 11%. The improvements were 55% and 68%, respectively, compared to the PID control. The average error in the vehicle test result was 0.414 kph, which was improved by 48% compared to the PID control in the test track. The results demonstrate that the proposed algorithm ensures desirable tracking performance under environmental disturbances and model uncertainties.

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An Indirect Type-2 Fuzzy Neural Network Optimized by the Grasshopper Algorithm for Vehicle ABS Controller

2022

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Path tracking of an autonomous vehicle by means of an indirect adaptive neural controller

Cited by 3 publications

References 30 publications

Automotive Electric Power Steering Control With Robust Observer Based Neuroadaptive Type-2 Radial Basis Function Methodology

Automotive Electric Power Steering Control With Robust Observer Based Neuroadaptive Type-2 Radial Basis Function Methodology

Model-reference adaptive sliding mode control of longitudinal speed tracking for autonomous vehicles

An Indirect Type-2 Fuzzy Neural Network Optimized by the Grasshopper Algorithm for Vehicle ABS Controller

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