Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

Hu, Hongzhen; Bei, Shaoyi; Zhao, Qixian; Han, Xiao; Zhou, Dan; Zhou, Xiaoling; Li, Bo

doi:10.3390/act11020034

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…The literature on PTC introduced the concepts of lateral offset and heading errors, denoted as e y and e φ , at a specific point C as depicted in Figure 3 . To enhance path tracking performance, this paper introduces a preview function, as used in the previous works [24,26,[30][31][32]. The preview distance, L p , is calculated using Equation ( 5), where k v is the velocity gain representing the preview interval.…”

Section: Vehicle Modelmentioning

confidence: 99%

“…With these definitions, the state-space equation and LQCF from the first to N-th time instant are calculated as Equation (24) and Equation (25), respectively. By replacing X of Equation (25) with Equation ( 24), the LQCF, Equation (25), is obtained by Equation (26). In Equation (27), the bound on steering angle, δ max , is imposed on the control input U.…”

Section: Design Of Mpc With Constraints On Tire Slip Anglesmentioning

confidence: 99%

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Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Lee,

Yim

2024

Applied Sciences

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This paper presents a method to design a path tracking controller with a constraint on tire slip angles under low-friction road conditions. On a low-friction road surface, a lateral tire force is easily saturated and decreases as a tire slip angle increases by a large steering angle. Under this situation, a path tracking controller cannot achieve its maximum performance. To cope with this problem, it is necessary to limit tire slip angles to a value where the maximum lateral tire force is achieved. The most commonly used controllers for path tracking, linear quadratic regulator (LQR) and model predictive control (MPC), are adopted as a controller design methodology. The control inputs of LQR and MPC are front and rear steering angles and control yaw moment, which have been widely used for path tracking. The constraint derived from tire slip angles is imposed on the steering angles of LQR and MPC. To fully verify the performance of the path tracking controller with the constraint on tire slip angles, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that the path tracking controller with the constraint on tire slip angles presented in this paper is quite effective for path tracking on low-friction road surface.

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Section: Vehicle Modelmentioning

confidence: 99%

Section: Design Of Mpc With Constraints On Tire Slip Anglesmentioning

confidence: 99%

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Lee,

Yim

2024

Applied Sciences

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“…In the paper, a rectangular polyhedron is built, and the linear matrix inequality (LMI) at the four vertices is solved to realize the feedback gain with different parameter variations [15] Two non-independent parameters, the longitudinal velocity and the reciprocal of the longitudinal velocity, were chosen as variable parameters to design an improved trapezoidal polytope to improve the correlation and to reduce the area of the region in the trapezoidal polytope that is inaccessible to the real state of the vehicle [16]. In order to boost vehicle stability and enhance trajectory tracking precision, adaptive preview times are calculated based on the lateral deviation and road boundaries during vehicle travel [17]. In fact, the modeling of parameter time-varying has been widely studied.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle

He,

Wu,

et al. 2024

Machines

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Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust H∞ controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system’s overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance.

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“…Among the above methods, the SMC algorithm has a special nonlinear control, known as a variable structure controller. It is insensitive to the uncertainty of inherent parameters and has high robustness to external disturbances [9]. If the control system has many unknown parameters, the SMC design becomes complicated, and its performance degrades.…”

Section: Introductionmentioning

confidence: 99%

Parallel Network-Based Sliding Mode Tracking Control for Robotic Manipulators with Uncertain Dynamics

Zhang

Song

et al. 2023

Actuators

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Robot dynamics model uncertainty and unpredictable external perturbations are important factors that influence control accuracy and stability. To accurately compensate for the dynamics model in sliding mode control (SMC), a new parallel network (PCR) is proposed in this paper. The network parallelizes the radial basis function and convolutional neural network, which gives it the advantage of making full use of one-dimensional data fitting results and two-dimensional data feature information, realizing the deep learning of multidimensional data and improving the model’s compensation accuracy and anti-interference ability. Meanwhile, based on the integration of adaptive control techniques and gradient descent, a new weight update algorithm is designed to realize the online learning of PCR networks under loss-free functions. Then, a new sliding mode controller (PCR-SMC) is established. The model-free intelligent control of the robot is accomplished without knowledge of the predetermined upper bounds. Additionally, the stability analysis of the control system is proved by the Lyapunov theorem. Lastly, robot tracking control simulations are performed on two trajectories. The results demonstrate the high-precision tracking performance of this controller in comparison with the RBF-SMC controller.

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Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

Cited by 8 publications

References 16 publications

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle

Parallel Network-Based Sliding Mode Tracking Control for Robotic Manipulators with Uncertain Dynamics

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