This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations.

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Design and Implementation of Deep Neural Network-Based Control for Automatic Parking Maneuver Process.

Review of advanced guidance and control algorithms for space/aerospace vehicles

Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This article introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this article provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Prescribed performance based model-free adaptive sliding mode constrained control for a class of nonlinear systems

Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.

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An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car “Ruilong” for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016.

A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car

In this work, a novel model free adaptive control method based on a mathematical algorithm named double successive projection (DSP-MFAC) is proposed, from which it can be inferred that conventional MFAC algorithm is only a special case of DSP-MFAC with the Cartesian product of the output and the input Hilbert space. For a general unknown nonlinear system, an innovative partial form dynamic linearization (PFDL) technique is first presented based on a new concept named pseudo gradient (PG). Then, the controller for the system is designed based on the PG with the aid of a novel DSP algorithm modified from the conventional successive projection algorithm. Consequently, the structures of the controller and its parameters estimator are symmetric similar, which makes the control performance become stable. Meanwhile, the convergence of DSP-MFAC for a regulation problem of a class of nonlinear discrete systems are guaranteed by rigorous mathematical analysis under several reasonable assumptions. Furthermore, numerical simulation results show that DSP-MFAC is effective and applicable.

Zhenxuan Li

Papers

A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car

Data-driven model-free adaptive control based on a novel double successive projection algorithm