Adaptive Cruise Control Problem Research Articles

Model-Free Robust Backstepping Adaptive Cruise Control

This paper studies the model-free robust adaptive cruise control problem of a vehicle with unknown nonlinear dynamics and disturbances. First, under backstepping control framework, the position tracking errors with different spacing strategies are used to design a virtual control law, which provides a velocity reference. Then, a novel data-driven sliding surface whose parameters are updated by designing estimation algorithm is developed to handle the unknown uncertainties and disturbances. Finally, the model-free robust backstepping adaptive cruise control (MFRB-ACC) method including PI control, model-free control, and robust control is designed. The novelty of the proposed control technique lies in its strong robustness, which is not based on the precise vehicle model. The designed data-driven sliding surface releases the necessity for the accurate mathematical model of the vehicle and guarantees the inherent robustness of the controller, in particular to uncertainties, modelling error, or external disturbance. Moreover, the designed controller contains three terms such that it has an effective decoupling ability and strong robustness. The effectiveness and superiority of the designed MFRB-ACC method are validated on MATLAB, and the simulation results show that compared to the PID algorithm, the designed MFRB-ACC algorithm can track its preceding vehicle with lower tracking error under different spacing strategies, different operating conditions, and low sampling frequencies. Especially at a sampling frequency of 0.1 s, the error under the PID-ACC increases from 0.2 m at a sampling frequency of 0.01 s to 2 m, and the error under MFRB-ACC has little change compared to the error at a sampling frequency of 0.01 s.

Compositional Construction of Safety Controllers for Networks of Continuous-Space POMDPs

In this article, we propose a compositional framework for the synthesis of safety controllers for networks of partially observable discrete-time stochastic control systems (also known as continuous-space partially observable Markov decision processes (POMDPs)). Given an estimator, we utilize a discretization-free approach to synthesize controllers ensuring safety specifications over finite-time horizons. The proposed framework is based on a notion of so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local control barrier functions</i> computed for subsystems in two different ways. In the first scheme, no prior knowledge of estimation accuracy is needed. The second framework utilizes a probability bound on the estimation accuracy using a notion of so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic simulation functions</i> . In both proposed schemes, we derive sufficient small-gain-type conditions in order to compositionally construct control barrier functions for interconnected POMDPs using local barrier functions computed for subsystems. The constructed control barrier functions for the overall networks enable us to compute lower bounds on the probabilities that the interconnected POMDPs avoid certain unsafe regions in finite-time horizons. We demonstrate the effectiveness of our proposed approaches by applying them to an adaptive cruise control problem.

Synthesis of Recursive Control Barrier Functions

Abstract This paper deals with the enforcement of safety constraints using control barrier functions (CBFs). We focus on dynamic systems that have high relative degree and where the output needs to be differentiated multiple times to obtain an explicit dependence on the control input. As the system approaches operating points that are close to the safety boundary, the CBF shrinks the allowed control action space. To quantify this shrinkage, we propose practical proxy metrics to capture the volume of the CBFs action space. We exploit these proxy metrics to derive sum-of-squares optimization formulations that synthesize CBFs with high-order degree, in particular their extended class K functions. We apply this tuning technique to synthesize CBFs that avoid vehicle collisions in an adaptive cruise control problem. Simulation results demonstrate the ability of our tuning approach to reduce loss of control performance when the system operates close to the safety boundary.

Fixed-time control under spatiotemporal and input constraints: A Quadratic Programming based approach

This paper presents a control synthesis framework for a general class of nonlinear, control-affine systems under spatiotemporal and input constraints. First, we study the problem of fixed-time convergence in the presence of input constraints. The relation between the domain of attraction for fixed-time stability with respect to input constraints and the required time of convergence is established. It is shown that increasing the control authority or the required time of convergence can expand the domain of attraction for fixed-time stability Then, we consider the problem of finding a control input that confines the closed-loop system trajectories in a safe set and steers them to a goal set within a fixed time. To this end, we present a Quadratic Programming (QP) formulation to compute the corresponding control input. We use slack variables to guarantee the feasibility of the proposed QP under input constraints. Furthermore, when strict complementary slackness holds, we show that the solution of the QP is a continuous function of the system states and establish the uniqueness of closed-loop solutions to guarantee forward invariance using Nagumo’s theorem. To corroborate our proposed methods, we present two case studies, an example of an adaptive cruise control problem and a two-robot motion planning problem.

Event-Triggered Control for Safety-Critical Systems With Unknown Dynamics

This paper addresses the problem of safety-critical control for multi-agent systems with unknown dynamics in unknown environments. It has been shown that stabilizing affine control systems to desired (sets of) states while optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this approach is obtaining accurate system dynamics of all components in the system, which is especially difficult when online model identification is required given limited computational resources and system data. We address this problem by proposing a robust framework (to unknown dynamics including uncertainties) through defining adaptive affine control dynamics that are updated based on the error states obtained by real-time sensor measurements. We define a CBF for a safety requirement on the unmodelled agents based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the above mentioned sequence of QPs. Then we determine a set of events that trigger the QPs and ensure safety when solving them. We also derive a condition that guarantees the satisfaction of a CBF constraint between events. The proposed framework can also be used for state convergence guarantees for systems with unknown dynamics based on CLFs. We illustrate the effectiveness of the proposed framework on a robot control problem, an adaptive cruise control problem and a traffic merging problem using autonomous vehicles. We also compare the proposed event-driven method with the classical time-driven approach.

Lazy controller synthesis for monotone transition systems and directed safety specifications

In this paper, we provide a lazy control synthesis algorithm for monotone transition systems and directed safety specifications. Two classes of monotone transition systems are presented: state monotone transition systems and input-state monotone transition systems. For the first class of systems, a partial order is defined only on the state space. For the second, the input space is ordered as well. The introduced lazy synthesis approach is based on the efficient computation of predecessors. It benefits not only from a monotone property of transition systems but also from the ordered structure of the state (input) space and the fact that directed safety specifications are considered. To enrich the class of the considered specifications, we also present an incremental controller synthesis framework, which allows us to deal with intersections of upper and lower-closed safety requirements. We then compare the proposed approach with the classical safety synthesis algorithm and illustrate the advantages, in terms of run-time and memory efficiency, on an adaptive cruise control problem.

Sufficient conditions for feasibility of optimal control problems using Control Barrier Functions

It has been shown that satisfying state and control constraints while optimizing quadratic costs subject to desired (sets of) state convergence for affine control systems can be reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this approach is ensuring the feasibility of these QPs, especially under tight control bounds and safety constraints of high relative degree. The main contribution of this paper is to provide sufficient conditions for guaranteed feasibility. The sufficient conditions are captured by a single constraint that is enforced by a CBF, which is added to the QPs such that their feasibility is always guaranteed. The additional constraint is designed to be always compatible with the existing constraints, therefore, it cannot make a feasible set of constraints infeasible — it can only increase the overall feasibility. We illustrate the effectiveness of the proposed approach on an adaptive cruise control problem.

Cooperative Adaptive Cruise Control Using Delay-Based Spacing Policy: A Robust Adaptive Non-Singular Terminal Sliding Mode Approach

This study proposes two speed controllers based on a robust adaptive non-singular terminal sliding mode control approach for the cooperative adaptive cruise control problem in a connected and automated vehicular platoon. The delay-based spacing policy is adopted to guarantee that all vehicles in the platoon track the same target velocity profile at the same position while maintaining a predefined time gap. Factors such as nonlinear vehicle longitudinal dynamics, engine dynamics with time delay, undulating road profiles, parameter uncertainties, and external disturbances are considered in the system modeling and controller design. Different control objectives are assigned to the leading and following vehicles. Then, controllers consisting of a sliding mode controller with parameter adaptive laws based on the ego vehicle’s state deviation and linear coupled state errors, and a Smith predictor for time delay compensation are designed. Both inner stability and strong string stability are guaranteed in the case of nonlinear sliding manifolds. Finally, the effectiveness of the proposed controllers and the benefits of 44.73% shorter stabilization time, 11.20% less speed overshoot, and virtually zero steady-state inner vehicle distance deviation are illustrated in a simulation study of a seven-vehicle platoon cooperative adaptive cruise control and comparison experiments with a coupled sliding mode control approach.

Lazy Symbolic Controller for Continuous-Time Systems Based on Safe Set Boundary Exploration

Abstract In this paper, we present an abstraction-based approach to robust safety controller synthesis for continuous-time nonlinear systems. To reduce the computational complexity associated with symbolic control approaches, we develop a lazy controller synthesis algorithm, which iteratively explores states on the boundary of controllable domain while avoiding exploration of internal states, supposing that they are safely controllable a priory. A closed-loop safety controller for the original problem is then defined as follows: we use the abstract controller to push the system from a boundary state back towards the interior, while for inner states, any admissible input is valid. We then compare the proposed approach with the classical safety synthesis algorithm and illustrate the advantages, in terms of run-time and memory efficiency, on an adaptive cruise control problem.

Model predictive control for drift counteraction of stochastic constrained linear systems

The paper presents a stochastic MPC (SMPC) formulation suitable for maximizing the average time until a discrete-time linear system with additive random disturbance violates prescribed constraints. The SMPC procedure is based on a scenario tree that encodes the most likely system behavior for a given tree density, where each branch of the tree represents a specific evolution of the system that occurs with a certain probability. A mixed-integer linear program (MILP) is developed that maximizes the average time until constraint violation for a given scenario tree. Feedback is provided by reconstructing the scenario tree and recomputing the MILP solution over a receding time horizon based on the current state of the system. The average time until constraint violation achieved by the SMPC strategy approaches the optimal value as the scenario tree density is increased. Two numerical case studies, including an adaptive cruise control problem, demonstrate the effectiveness of the proposed SMPC strategy compared to dynamic programming solutions.

Gaussian learning‐based fuzzy predictive cruise control for improving safety and economy of connected vehicles

This study considers an adaptive cruise control problem of connected vehicles in the vehicular ad-hoc network and proposes a Gaussian learning-based fuzzy predictive cruise control approach to enhance the fuel efficiency and safety of the connected vehicles in a vehicle-following scenario. First, a Gaussian process regression model is introduced and trained with real data to estimate the future acceleration of the preceding vehicle over the prediction horizon. Moreover, with assessing traffic scenarios, the weights characterising the importance of individual performance are adjusted by a fuzzy decision method in real time. Then a fuzzy predictive cruise controller is obtained by online solving a constrained receding horizon optimal control problem with a changing cost function and acceleration prediction of the preceding vehicle. Finally, through CarSim/Simulink co-simulation, it is shown that the proposed approach has an improvement in fuel economy and safety compared with conventional predictive cruise control algorithms.

Efficient predictive cruise control of autonomous vehicles with improving ride comfort and safety

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.

Time-delay feedback cooperative adaptive cruise control of connected vehicles by heterogeneous channel transmission

In this paper, we consider the cooperative adaptive cruise control problem of connected autonomous vehicles networked by heterogeneous wireless channel transmission. The cooperative adaptive cruise control model with variable input delays is established to describe the varying time-delays induced from vehicular actuators and heterogeneous channel transmission. Then a set of decentralized time-delay feedback cooperative adaptive cruise control controllers is computed in such way that each vehicle evaluates its own adaptive cruise control strategy using only neighborhood information. In order to establish string stability of the connected vehicle platoon with the decentralized controllers, the sufficient conditions are obtained in the form of linear matrix inequalities. The scenarios, consisting of four different cars with three heterogeneous wireless channels, are used to demonstrate the effectiveness of the presented method.

PROBABILISTIC VALIDATION OF ADVANCED DRIVER ASSISTANCE SYSTEMS

We present a methodological approach for validation of advanced driver assistance systems, based on randomized algorithms. The new methodology is more efficient than conventional validation by simulations and field tests, especially with increasing system complexity. The methodology consists of first specifying the perturbation set and performance criteria. Then a minimum required number of samples and a relevant sampling space is selected. Next an iterative randomized simulation is executed, followed by validation with hardware tests. The concept is illustrated with a simple adaptive cruise control problem.