Model free predictive path tracking control of variable-configuration unmanned ground vehicle

Jiang, Yue; Xu, Xiaojun; Zhang, Lei; Zou, Teng’an

doi:10.1016/j.isatra.2022.01.026

Cited by 31 publications

(8 citation statements)

References 38 publications

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“…The authors in [11][12][13] implemented PSO in the control of robotics for various applications. PSO is also popular in unmanned vehicles in performing path planning [14,15] and path tracking [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

Shaqarin

Noack

2023

Int J Comput Intell Syst

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We improve convergence speed by two orders of magnitude and the global exploration capabilities of particle swarm optimization (PSO) through targeted position-mutated elitism (TPME). The proposed fast-converging TPME operator requires a fitness-based classification technique to categorize the particles. The introduced classification is motivated by its simplicity, low memory requirements, and automated termination criteria based on convergence. The three key innovations address particle classification, elitism, and mutation in the cognitive and social model. PSO-TPME is benchmarked against five popular PSO variants for multi-dimensional functions, which are extensively adopted in the optimization field, In particular, the convergence accuracy, convergence speed, and the capability to find global minima are investigated. The statistical error is assessed by numerous repetitions. The simulations confirmed that in ten of the thirteen investigated functions, the proposed PSO variant outperforms other variants in terms of convergence rate and accuracy by at least two orders of magnitude. On the other hand, the simulations demonstrated the early exploration capabilities of PSO-TPME in all tested functions. In the first ten iterations, PSO-TPME outperformed all the investigated PSO variants by at least two orders of magnitude.

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Section: Introductionmentioning

confidence: 99%

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

Shaqarin

Noack

2023

Int J Comput Intell Syst

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“…Tan et al [ 12 ] presented a robust model predictive control-based strategy for the path tracking of 4WS4WD vehicles considering external disturbances. Jiang et al [ 13 ] proposed a hierarchical control strategy to realize the path-tracking control of six-wheel independent drive UGV based on differential steering. In order to overcome the structural and unstructured uncertainties, Jiang et al [ 14 ] presented a model-free predictive control strategy using particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimum Control for Path Tracking Problem of Vehicle Handling Inverse Dynamics

Liu

Cui

Wen

2023

Sensors

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Accurate tracking of a given path is one of the primary factors in the maneuverability of a vehicle and is also an important topic in autonomous vehicle research. To solve the problem of vehicle path tracking, the problem must first be transformed into an optimal control problem. Then, a symplectic pseudospectral method (SPM) based on the third-generation function of symplectic theory and pseudospectral discretization is proposed to efficiently solve the nonlinear optimal control problems. Finally, the results obtained by the proposed algorithm are compared with those obtained by the Gauss pseudospectral method (GPM). The simulation results show that the proposed method can effectively solve the vehicle path tracking problem. Furthermore, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and greater applicability than other methods.

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“…The authors designed a set-membership-based parameter estimator using the recursive least squares technique. Jiang, Y. et al investigated the path tracking control strategy of variable-configuration unmanned ground vehicle and proposed an improved model free predictive control scheme [ 37 ]. Li, X. et al developed a novel robust adaptive learning control algorithm that can estimate the system uncertainties through the iterative learning method [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

Seo

2022

Sensors

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This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

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Model free predictive path tracking control of variable-configuration unmanned ground vehicle

Cited by 31 publications

References 38 publications

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

Optimum Control for Path Tracking Problem of Vehicle Handling Inverse Dynamics

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

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