Deep Model Reference Adaptive Control (1909.08602v1)

Abstract: We present a new neuroadaptive architecture: Deep Neural Network based Model Reference Adaptive Control (DMRAC). Our architecture utilizes the power of deep neural network representations for modeling significant nonlinearities while marrying it with the boundedness guarantees that characterize MRAC based controllers. We demonstrate through simulations and analysis that DMRAC can subsume previously studied learning based MRAC methods, such as concurrent learning and GP-MRAC. This makes DMRAC a highly powerful architecture for high-performance control of nonlinear systems with long-term learning properties.

• The paper introduces DMRAC, integrating deep neural networks with adaptive control to handle complex nonlinearities in dynamic systems.
• It presents a dual time-scale learning algorithm that updates the network's outer layer in real-time while refining inner layers via batch processing.
• The approach guarantees uniform ultimate boundedness, ensuring system stability for safety-critical applications in areas like robotics and autonomous vehicles.

Deep Model Reference Adaptive Control: A Comprehensive Overview

The paper "Deep Model Reference Adaptive Control" introduces a novel approach in the field of adaptive control systems through the integration of Deep Neural Networks (DNNs). The authors, Girish Joshi and Girish Chowdhary, propose a unique architecture termed Deep Neural Network based Model Reference Adaptive Control (DMRAC). This architecture harnesses the representational power of deep neural networks to manage substantial nonlinearities in dynamic systems while maintaining the stability assurances intrinsic to Model Reference Adaptive Control (MRAC) techniques.

Key Contributions

1. Integration of DNNs in MRAC: The core innovation presented by the authors is the integration of DNNs into MRAC frameworks. The primary motivation for using DNNs arises from their superior ability to approximate complex functions compared to traditional, shallower network architectures such as Radial Basis Function networks. DNNs are leveraged within the adaptation scheme to handle the uncertainty modeling in dynamic systems thoroughly.
2. Dual Time-Scale Learning Algorithm: The authors introduce a dual time-scale learning strategy, distinguishing their method from existing single-time-scale adaptation processes. This approach improves learning efficiency by updating the outer layer of the network in real-time while the inner layers are updated using batch processing. This duality ensures robust feature extraction and enhancement of the control policy over time.
3. Uniform Ultimate Boundedness: The paper provides theoretical assurance of the stability of the DMRAC through uniform ultimate boundedness (UUB). This characteristic is crucial for adaptive controllers, especially in safety-critical applications where maintaining system stability is imperative.
4. Beyond Traditional Methods: Building on their prior work with Gaussian Processes in MRAC (GP-MRAC), the authors demonstrate through simulations that DMRAC encompasses GP-MRAC and concurrent learning methods. By addressing the limitations of parametric learning laws, DMRAC stands out as potentially more effective in managing high-dimensional, nonlinear control tasks.

Implications and Future Directions

The introduction of DNNs into adaptive control frameworks reflects a significant step towards addressing complex system dynamics with high precision and reliability. The ability to leverage deep learning models while maintaining control stability broadens the application scope of adaptive control systems significantly.

The potential future developments that emerge from this research include:

• Generalized Application in Reinforcement Learning:

The dual time-scale learning approach can be extended to reinforcement learning scenarios, particularly where feature representation and stability are critical.

• Safety-Critical Systems:

DMRAC's stability assurance makes it an attractive choice for implementation in safety-critical systems such as autonomous vehicles and robotics, where system failures must be minimized.

• Adaptive Control in High-Dimensional Systems:

The architecture presents a way forward for deploying adaptive control strategies in systems characterized by high-dimensional state spaces and complex dynamics.

This paper provides a robust foundation for future research in adaptive control using deep learning. The methodology could promote further exploration and integration of DNNs within various domains of system control, paving the way for more adaptive, resilient, and efficient control systems. As research in artificial intelligence and control theory progresses, the practical implications of this work could significantly influence various fields, from aerospace to robotics, where adaptive stability and precision are paramount.