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In this article, a novel data-driven constrained control scheme is proposed for automatic parking systems. The design of the proposed scheme only depends on the steering angle and the orientation angle of the car, and it does not involve any model information of the car. Therefore, the proposed scheme-based automatic parking system is applicable to...
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... Currently, there are no studies have used the RRT-ACS algorithm in an automated parking system. Another disadvantage of the reference's autonomous parking method is that it only addresses one or two sorts of parking issues even though there are three broad categories of parking methods namely vertical, parallel, and oblique parking [23]. The difference between this study and other studies is that it uses a path planning algorithm that is faster than the RRT algorithm for automatic parking in oblique parking, vertical parking, and parallel parking. ...
One key autonomous driving car application that can be utilized to address the problem of parking spaces is autonomous parking. The goal of this study is to develop a path planning algorithm that can swiftly create a path for autonomous parking in a variety of car parking settings. The method employed is a mix of Rapidly Exploring Random Tree Star and Ant Colony Systems (RRT-ACS). The RRT-ACS method can quickly provide an ideal path. The reed sheep planner technique is also used to provide a smooth curving path that non-holonomic vehicles can follow. The performance of the RRT-ACS algorithm-based autonomous parking system is compared to that of the RRT*-connect and informed RRT*-connect algorithms. The simulation tests were performed in common parking settings such as parallel parking and vertical parking. The test results reveal that the suggested autonomous parking system outperforms other comparative algorithms. It is possible to infer that a route planning algorithm capable of rapidly designing a path for automatic parking in a variety of car parking circumstances has been successfully built. These findings may have ramifications for self-driving cars equipped with advanced driver support systems.
... Hou and Dong [22] proposed an algorithm based on coordinates compensation, which further reduced the coordinates steadystate tracking error of the desired trajectory, by correcting the vehicle body angle online. In [23], a dynamic anti-saturation compensator is proposed, which can deal with the change amplitude and saturation velocity of system input, effectively reducing the tracking error of parking trajectory coordinates. In [24] and [25], the deviation between the expected trajectory and the feedback actual trajectory is analyzed, the interference of the steering system is considered, a non-time referenced sliding mode control and a quadratic optimal control scheme were designed to generate the target angle. ...
... where, f refers to the extended state, which can be estimated by the extended state observer in Eq. (23). In order to suppress the chattering phenomenon and ensure the smooth switching surface of the system, the sign function sgn . in Eq. (30) is replaced by the following saturation function. ...
Owing to the increased demand for autonomous driving and advanced driver assistant systems, autonomous parking configurations have been widely researched. In order to solve the problem of path tracking accuracy degradation, caused by ignoring the external interference in the actual parking process and the uncertainty of vehicle steering modeling, a parking path tracking algorithm, combining Sliding Model Control (SMC) and Extended State Observer (ESO),is proposed. First, the vehicle kinematics model is established, which includes many external interference factors, such as lateral position, speed, heading angle, uncertainty parameters and time delay of steering mechanism. Considering the vehicle physical constraints and boundary condition constraints, during the parking process, the parking reference path is designed according to reverse outbound driving, while the parking collision analysis and path smoothing are further carried out. Next, an ESO is designed to observe and compensate the external disturbances and model uncertainties, treating these as the total disturbances of the system. On this basis, a SMC for parallel parking path tracking is designed. The observed value of ESO is used as the compensation in the SMC, to weaken the influence of external interference. Finally, using Matlab simulation, the feasibility and effectiveness of the proposed path planning and tracking control method are verified. The simulation results show that, the maximum tracking error of lateral position is less than 0.01m, whereas the maximum tracking error of heading angle is less than 2.5°.Comparing two control strategies, the control effect of the designed SMC with ESO is better than that of the traditional SMC controller, while the ability of resisting external interference is stronger. In addition, real vehicle tests are carried out, to verify the effectiveness of the proposed control method. The test results show that, the vehicle can safely park in the parking space, quickly and accurately, even if there is external interference. The proposed method can produce the parking trajectory according to the given constraints and control the vehicle to complete the parking operation accurately along the planned path.
... Tan et al. [15] presented the parking guidance control for a general parallel parking scenario and explained how to check its feasibility. Yan et al. [16] proposed a novel data-driven constrained control scheme, which only depends on the car's steering and orientation angle. Bai et al. [17] proposed a path planning algorithm based on A* and DWA, constructed a global optimal path evaluation subfunction and optimized the path length and planning time. ...
Compared to wheeled vehicles, tracked vehicles have unique advantages in disaster relief and engineering sites. The working environment of tracked vehicles is mostly in fixed-point conditions, so high-precision automatic stopping is essential for tracked vehicles. Improved A* and dynamic window approach are proposed in this paper to enhance the accuracy and speed of automatic parking of tracked vehicles. The parking trajectory is significantly optimized within low deviations. Furthermore, a prototype is designed to verify the working conditions of fixed-point parking to improve the work efficiency of engineering rescue. The simulation results based on the robot operating system show that both single-step and two-step parking methods can reach the parking position without collision. More importantly, simulated and experimental parking positions and angles have considerable accuracy within acceptable limits. This study has essential guidance and application value for the high-precision automatic parking of tracked vehicles for various extreme application scenarios.
... An automatic parking system can avoid parking accidents. By using sensors' feedback the parking environment can be detected and the vehicle can be led to be placed based on the optimal parking path in the parking lot [6]. ...
... As a result, the spatiotemporal decomposition technique has been presented which speeds up the NLP process. In reference [6] the datadriven constrained control system has been utilized. This system is controlled based on the process model identification or the acquired data from experiments. ...
In this article, an adaptive proportional–integral–derivative–type neural network constrained control method based on radial basis function neural network model identifier is presented for automatic parking system. In the design process of the control method, the parameters of proportional–integral–derivative–type neural network controller can be adjusted online using the Jacobian information (the sensitivity of system output with respect to its input) of the controlled system. In this way, the proposed method will have a better adaptability. Meanwhile, we design a novel dynamic anti-windup compensation unit to solve the magnitude saturation and rate constraint problems of automatic parking system. The stability analysis based on Lyapunov function is given to prove the convergence of the proposed control algorithm. The final simulation results for automatic parking system show the effectiveness of the proposed method.
