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Feedback Linearized Model Predictive Control for Input-Constrained Self-Driving Cars


Core Concepts
A novel dual-mode feedback linearized model predictive control strategy is proposed to solve the trajectory tracking problem for self-driving cars subject to longitudinal and steering angular velocity constraints.
Abstract
The paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. The authors develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, the authors derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner approximation. Then, a dual-mode MPC is derived to be real-time affordable and ensuring, by design, constraints fulfillment, recursive feasibility, and uniformly ultimate boundedness of the tracking error in an ad-hoc built robust control invariant region. The approach's effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar. The obtained results show that the proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes.
Stats
The car's longitudinal velocity is bounded by v > 0. The car's steering angular velocity is bounded by ω > 0.
Quotes
"The proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes." "The approach's effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar."

Deeper Inquiries

How can the proposed feedback linearized MPC framework be extended to handle model uncertainties and external disturbances

To extend the proposed feedback linearized MPC framework to handle model uncertainties and external disturbances, one can incorporate robust control techniques. By introducing robust control strategies such as H-infinity control or sliding mode control, the system can be designed to be more resilient to uncertainties and disturbances. These techniques allow for the synthesis of controllers that can provide robust performance even in the presence of unknown dynamics or external disturbances. Additionally, one can utilize adaptive control methods to continuously adjust the controller parameters based on the system's response, thereby improving the system's ability to adapt to changing conditions and uncertainties. By integrating these robust and adaptive control strategies into the feedback linearized MPC framework, the autonomous vehicle can maintain stability and performance in the face of uncertainties and disturbances.

What are the potential challenges in scaling the proposed approach to real-world autonomous driving scenarios with complex environments and dynamic obstacles

Scaling the proposed approach to real-world autonomous driving scenarios with complex environments and dynamic obstacles poses several challenges. One major challenge is the computational complexity of the MPC optimization, especially in scenarios with a large number of obstacles or complex environmental conditions. Real-time implementation of the MPC controller becomes crucial in such scenarios, requiring efficient algorithms and hardware to handle the computational load. Additionally, the robustness of the controller in handling unforeseen obstacles and dynamic changes in the environment needs to be thoroughly tested and validated. Incorporating advanced perception and sensor fusion techniques to provide accurate and timely information about the surroundings is essential for the controller to make informed decisions in complex environments. Furthermore, ensuring safety and reliability in dynamic and uncertain environments requires comprehensive testing and validation procedures, including simulation studies and real-world testing in controlled environments before deployment in complex scenarios.

How can the insights from this work on input-constrained vehicle control be applied to other robotic systems with similar kinematic constraints, such as mobile manipulators or legged robots

The insights gained from this work on input-constrained vehicle control can be applied to other robotic systems with similar kinematic constraints, such as mobile manipulators or legged robots. By utilizing the principles of feedback linearization and model predictive control, these robotic systems can benefit from trajectory tracking capabilities while adhering to input constraints. The development of dual-mode MPC strategies can enable stable full-state tracking and input constraint fulfillment in various robotic applications. Additionally, the characterization of input constraints and the design of robust control invariant regions can be extended to mobile manipulators and legged robots to ensure stability and performance in the presence of constraints. By adapting the concepts and methodologies from this work, robotic systems with complex kinematic structures can enhance their control strategies for improved trajectory tracking and maneuverability.
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