Adaptive Control for Unknown Heterogeneous Vehicles Synchronization with Unstructured Uncertainty (2002.05207v1)

Abstract: The cooperative control applied to vehicles allows the optimization of traffic on the roads. There are many aspects to consider in the case of the operation of autonomous vehicles on highways since there are different external parameters that can be involved in the analysis of a network. In this paper, we present the design and simulation of adaptive control for a platoon with heterogeneous vehicles, taking into account that not all vehicles can communicate their control input, and in turn include structured nonlinear uncertainty input parameters.

• The paper introduces an adaptive MRAC framework that synchronizes heterogeneous vehicles using neural network approximations to mitigate unstructured uncertainties.
• The methodology employs a distributed control law with Lyapunov-based analysis, ensuring bounded synchronization errors even under communication lapses.
• Simulations in a cooperative adaptive cruise control scenario demonstrated robust convergence of speeds and positions, confirming the strategy's efficacy amid nonlinear disturbances.

Adaptive Control for Unknown Heterogeneous Vehicles Synchronization with Unstructured Uncertainty

Introduction

The paper explores the design and implementation of an adaptive control strategy for the synchronization of vehicle platoons characterized by heterogeneous dynamics and unstructured uncertainties. The primary application avenue is within autonomous vehicles on roadways, where cooperative control strategies can enhance traffic efficiency and coordination. Notably, the heterogeneous nature of the vehicles and the communication limitations among them necessitate an advanced adaptive control approach that can handle nonlinear disturbances without full communication of control inputs across the network.

Problem Formulation

The paper focuses on a control framework designed for a system of vehicles expressed as a set of heterogeneous agents. The agents are governed by unknown dynamics with nonlinear input uncertainties modeled as disturbances. The control objective is to synchronize these agents to a common reference model under conditions of imperfect communication. Specifically, the agents are described by:

$$\dot{x}_i = A_ix_i + b_i(u_i + f_i(x_i)), \quad i \in \{1, \ldots, N\}$$

Here, $x_i$ represents the state of each agent, $u_i$ denotes the control input, and $f_i(x_i)$ captures the bounded nonlinear input uncertainty. The agents operate under matching conditions that align them to a reference model and their neighbors.

Adaptive Control Strategy

This paper introduces a distributed Model Reference Adaptive Control (MRAC) framework, extended with neural network approximations to mitigate uncertainties. A neural network is utilized to estimate and compensate for the structured uncertainty, allowing cancellation through adaptive laws:

• Neural Network Approximation: The nonlinear input uncertainty is parameterized through neural networks, aiding in its cancellation in the adaptive control law.
• Control Law: The proposed control strategy integrates adaptive laws for feedback matching and utilizes a recurrent neural network for estimating nonlinear uncertainties.
• Synchronization Guarantee: Through the use of Lyapunov methods, the paper demonstrates that all synchronization errors are bounded.

The adaptive controller is constructed to compensate for the lack of direct control input communication, using an estimator to derive inputs based on neighbor interactions when communication fails.

Simulation and Results

Extensive simulations were carried out to evaluate the effectiveness of the control strategy in a typical Cooperative Adaptive Cruise Control (CACC) scenario. The multi-agent vehicle system simulations demonstrated successful synchronization to a reference speed and spacing, confirming the control law's efficacy even with communication lapses and significant initial disturbances.

• Configuration: Simulations involved vehicles modeled as linear systems with parameters tailored to exhibit instability in open-loop conditions, providing a robust test bed for the controller.
• Performance: Results show strong convergence of vehicle speeds and positions, adhering to the reference model, thereby validating the theoretical constructs and controller design.

Conclusions and Future Work

The adaptive control methodology developed provides a robust solution for synchronizing autonomous vehicles with heterogeneous and unknown dynamics in a network. This approach offers a means to counteract the uncertainties inherent in platoon operations and communication disruptions. Future directions include extensions to cyclic topologies, physical installation of control strategies in autonomous vehicle networks, and application to broader domains like robotic coordination.

This paper lays the groundwork for further refinement and extension of cooperative adaptive control technologies, critical to advancing autonomous vehicle networks and improving traffic management systems.