In this paper, we present a novel control law for longitudinal speed control of autonomous vehicles. The key contributions of the proposed work include the design of a control law that reactively integrates the longitudinal surface gradient of road into its operation. In contrast to the existing works, we found that integrating the path gradient into the control framework improves the speed tracking efficacy. Since the control law is implemented over a shrinking domain scheme, it minimizes the integrated error by recomputing the control inputs at every discretized step and consequently provides less reaction time. This makes our control law suitable for motion planning frameworks that are operating at high frequencies. Furthermore, our work is implemented using a generalized vehicle model and can be easily extended to other classes of vehicles. The performance of gradient aware-shrinking domain based controller is implemented and tested on a stock electric vehicle on which a number of sensors are mounted. Results from the tests show the robustness of our control law for speed tracking on a terrain with varying gradient while also considering stringent time constraints imposed by the planning framework.
In this section, we discuss the existing methods that have attempted to develop control laws for speed tracking. The initial work includes the development of control laws for vehicle following at both low speed and high speed scenarios @cite_12 . A non-linear vehicle system is modeled to obtain the dynamic solution with safety and comfort constraints in @cite_21 . In @cite_4 , @cite_16 , @cite_10 they even explored the hybrid system framework, where the vehicle system is divided into local subsystems.
@cite_4 @cite_21 @cite_16 @cite_10 @cite_12
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This article addresses the trajectory tracking problem for a wheeled mobile base, considering the presence of disturbances that violate the nonholonomic constraint, and using an approximated discrete-time model for the vehicle. The proposed solution is based on discrete-time sliding mode control, in order to ensure that the controller is both robust and implementable. The asymptotic boundedness of the discrete-time tracking errors is theoretically proved, and experimental results are reported, showing the effectiveness of the proposed control law. © 2002 Wiley Periodicals, Inc. This paper presents a hybrid system approach to longitudinal speed and traction control of a vehicle with wheel slip constraints. The vehicle system is modeled as a hybrid system where the system is divided into local subsystems and each subsystem is selected to control the vehicle, in terms of control modes and operating region. Controllers are designed to track a desired speed value while maintaining the safety constraint that the absolute value of the slip between wheels and ground must be less than a given limit value to prevent the wheels from skidding or spinning. Simulation results are provided to show the feasibility of the proposed control system. High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this paper, we introduce a parallel autonomy , or shared control, framework that computes safe trajectories for an automated vehicle, based on human inputs. We minimize the deviation from the human inputs while ensuring safety via a set of collision avoidance constraints. Our method achieves safe motion even in complex driving scenarios, such as those commonly encountered in an urban setting. We introduce a receding horizon planner formulated as nonlinear model predictive control (NMPC), which includes the analytic descriptions of road boundaries and the configuration and future uncertainties of other road participants. The NMPC operates over both steering and acceleration simultaneously. We introduce a nonslip model suitable for handling complex environments with dynamic obstacles, and a nonlinear combined slip vehicle model including normal load transfer capable of handling static environments. We validate the proposed approach in two complex driving scenarios. First, in an urban environment that includes a left-turn across traffic and passing on a busy street. And second, under snow conditions on a race track with sharp turns and under complex dynamic constraints. We evaluate the performance of the method with various human driving styles. We consequently observe that the method successfully avoids collisions and generates motions with minimal intervention for parallel autonomy. We note that the method can also be applied to generate safe motion for fully autonomous vehicles. The paper considers the problem of automatic path tracking by autonomous farming vehicles subject to wheel slips, which are characteristic for agricultural applications. Two guidance laws are proposed to solve this problem, and both explicitly take into account the constraints on the steering angle and ensure tracking an arbitrarily curved path. The first law is implemented by the pure sliding-mode controller, whereas the second one combines the sliding mode approach with a smooth nonlinear control law and requests control chattering at the reduced amplitude as compared with the first law. Mathematically rigorous proofs of global convergence and robust stability of the proposed guidance laws are presented. In doing so, the slipping effects are treated as bounded uncertainties. Simulation results and real world experiments confirm the applicability and performance of the proposed guidance approach.