innsikt - Robotics Control - # Disturbance-robust control synthesis under Signal Temporal Logic specifications

Maximizing Disturbance Robustness for Signal Temporal Logic Control Synthesis

Q: How can the proposed approach be extended to handle a more expressive fragment of Signal Temporal Logic, beyond the bounded-time fragment considered in this work

To extend the proposed approach to handle a more expressive fragment of Signal Temporal Logic beyond the bounded-time fragment, several key modifications and enhancements can be implemented. One approach could involve incorporating additional temporal operators such as "Until" with unbounded intervals, "Release," or "Weak Until" operators. These operators introduce more complex temporal relationships and dependencies, allowing for richer and more intricate specifications. By extending the fragment to include these operators, the system can capture a broader range of temporal logic properties, enabling more sophisticated control synthesis. Furthermore, the algorithm can be adapted to handle nested temporal operators and complex formula structures. This enhancement would involve developing recursive procedures to navigate through nested subformulas, resolving temporal flexibility at different levels of the formula hierarchy. By recursively addressing subformulas with varying levels of complexity, the approach can effectively handle a more expressive fragment of Signal Temporal Logic. Additionally, the semantics of the disturbance-robustness metrics can be refined to accommodate the nuanced requirements of the extended fragment. By defining robustness metrics for the new operators and temporal relationships, the framework can accurately evaluate and optimize controllers for specifications with diverse temporal logic constructs.

Q: What are the potential limitations of the Hamilton-Jacobi reachability-based approach, and how could alternative techniques be leveraged to further improve the computational efficiency of the disturbance-robust control synthesis

The Hamilton-Jacobi reachability-based approach, while powerful for solving reachability problems and synthesizing disturbance-robust controllers, may face certain limitations in terms of scalability and computational efficiency. One potential limitation is the computational complexity associated with solving high-dimensional systems or specifications with intricate temporal logic structures. As the dimensionality of the system increases, the computational burden of solving the Hamilton-Jacobi reachability equations grows significantly, potentially leading to longer computation times and resource-intensive calculations. To address these limitations and enhance computational efficiency, alternative techniques can be leveraged in conjunction with the Hamilton-Jacobi reachability approach. One strategy is to employ abstraction and decomposition methods to reduce the complexity of the system and specifications. By abstracting the system dynamics or decomposing the problem into smaller, more manageable subproblems, the computational load can be distributed more effectively, leading to faster and more efficient synthesis of disturbance-robust controllers. Furthermore, leveraging machine learning and data-driven approaches to approximate the reachability sets or learn control policies can expedite the controller synthesis process. By training neural networks or reinforcement learning algorithms on simulated or real-world data, the system can learn to predict optimal control actions in response to disturbances, offering a more scalable and adaptive solution for disturbance-robust control synthesis.

Q: Given the focus on maximizing disturbance robustness, how could the proposed framework be adapted to also consider other performance metrics, such as energy efficiency or task completion time, as part of the multi-objective optimization problem

To adapt the proposed framework to consider other performance metrics alongside disturbance robustness in a multi-objective optimization problem, several modifications and enhancements can be implemented. One approach is to introduce weighting factors or utility functions that balance the trade-offs between disturbance robustness and other metrics such as energy efficiency or task completion time. By assigning weights to each objective and formulating a multi-objective optimization problem, the framework can generate a set of Pareto-optimal solutions that represent the optimal trade-offs between different performance criteria. Additionally, the framework can incorporate additional constraints or objectives related to energy consumption, task deadlines, or other performance metrics. By defining constraints that capture the desired levels of energy efficiency or task completion time, the optimization problem can be extended to consider these factors alongside disturbance robustness. This extension would involve modifying the objective function and constraints in the optimization algorithm to reflect the multi-objective nature of the problem. Moreover, the framework can utilize advanced optimization techniques such as multi-objective evolutionary algorithms or multi-criteria decision-making methods to efficiently explore the trade-offs between different performance metrics. These algorithms can help identify the Pareto-optimal solutions that represent the best compromises between disturbance robustness, energy efficiency, task completion time, and other objectives, providing decision-makers with valuable insights into the performance trade-offs in the control synthesis process.

Grunnleggende konsepter

This work aims to jointly synthesize the maximal permissible disturbance bounds and the corresponding controllers that ensure a given Signal Temporal Logic specification is satisfied under these bounds.

Sammendrag

This work addresses the problem of maximally robust control synthesis under unknown disturbances. The authors consider a general nonlinear system subject to a Signal Temporal Logic (STL) specification and aim to jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds.

The key highlights and insights are:

The authors introduce computationally efficient underapproximations of disturbance robustness for a fragment of STL.
They present an algorithm to obtain maximally disturbance-robust controllers for this STL fragment.
The authors prove the soundness of their approach and show empirical evidence of its effectiveness in simulation, using an Autonomous Underwater Vehicle (AUV) as an example.
Many existing works have considered STL satisfaction under given bounded disturbances, but this is the first work that aims to maximize the permissible disturbance set and find the corresponding controllers that ensure satisfying the STL specification with maximum disturbance robustness.
The authors extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm to get the maximal disturbance robust controllers satisfying an STL specification using Hamilton-Jacobi reachability.

Tilpass sammendrag

Omskriv med AI

Generer sitater

Oversett kilde

Til et annet språk

Generer tankekart

fra kildeinnhold

Besøk kilde

arxiv.org

Statistikk

The system dynamics are given by the following nonlinear model for an Autonomous Underwater Vehicle (AUV):
ẋ = v cos(θ) + dx(Afront cos(θ) + Aside sin(θ))
ẏ = v sin(θ) + dy(Afront sin(θ) + Aside cos(θ))
v̇ = uv
θ̇ = uθ
where dx and dy are the disturbances in x and y dimensions, Afront and Aside are the frontal and side surface areas of the AUV, uv and uθ are the control inputs for velocity and orientation, respectively.

Sitater

"This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation."

Viktige innsikter hentet fra

Robust STL Control Synthesis under Maximal Disturbance Sets

by Joris Verhag... klokken arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05535.pdf

Robust STL Control Synthesis under Maximal Disturbance Sets

Dypere Spørsmål

How can the proposed approach be extended to handle a more expressive fragment of Signal Temporal Logic, beyond the bounded-time fragment considered in this work

To extend the proposed approach to handle a more expressive fragment of Signal Temporal Logic beyond the bounded-time fragment, several key modifications and enhancements can be implemented. One approach could involve incorporating additional temporal operators such as "Until" with unbounded intervals, "Release," or "Weak Until" operators. These operators introduce more complex temporal relationships and dependencies, allowing for richer and more intricate specifications. By extending the fragment to include these operators, the system can capture a broader range of temporal logic properties, enabling more sophisticated control synthesis.
Furthermore, the algorithm can be adapted to handle nested temporal operators and complex formula structures. This enhancement would involve developing recursive procedures to navigate through nested subformulas, resolving temporal flexibility at different levels of the formula hierarchy. By recursively addressing subformulas with varying levels of complexity, the approach can effectively handle a more expressive fragment of Signal Temporal Logic.
Additionally, the semantics of the disturbance-robustness metrics can be refined to accommodate the nuanced requirements of the extended fragment. By defining robustness metrics for the new operators and temporal relationships, the framework can accurately evaluate and optimize controllers for specifications with diverse temporal logic constructs.

What are the potential limitations of the Hamilton-Jacobi reachability-based approach, and how could alternative techniques be leveraged to further improve the computational efficiency of the disturbance-robust control synthesis

The Hamilton-Jacobi reachability-based approach, while powerful for solving reachability problems and synthesizing disturbance-robust controllers, may face certain limitations in terms of scalability and computational efficiency. One potential limitation is the computational complexity associated with solving high-dimensional systems or specifications with intricate temporal logic structures. As the dimensionality of the system increases, the computational burden of solving the Hamilton-Jacobi reachability equations grows significantly, potentially leading to longer computation times and resource-intensive calculations.
To address these limitations and enhance computational efficiency, alternative techniques can be leveraged in conjunction with the Hamilton-Jacobi reachability approach. One strategy is to employ abstraction and decomposition methods to reduce the complexity of the system and specifications. By abstracting the system dynamics or decomposing the problem into smaller, more manageable subproblems, the computational load can be distributed more effectively, leading to faster and more efficient synthesis of disturbance-robust controllers.
Furthermore, leveraging machine learning and data-driven approaches to approximate the reachability sets or learn control policies can expedite the controller synthesis process. By training neural networks or reinforcement learning algorithms on simulated or real-world data, the system can learn to predict optimal control actions in response to disturbances, offering a more scalable and adaptive solution for disturbance-robust control synthesis.

Given the focus on maximizing disturbance robustness, how could the proposed framework be adapted to also consider other performance metrics, such as energy efficiency or task completion time, as part of the multi-objective optimization problem

To adapt the proposed framework to consider other performance metrics alongside disturbance robustness in a multi-objective optimization problem, several modifications and enhancements can be implemented. One approach is to introduce weighting factors or utility functions that balance the trade-offs between disturbance robustness and other metrics such as energy efficiency or task completion time. By assigning weights to each objective and formulating a multi-objective optimization problem, the framework can generate a set of Pareto-optimal solutions that represent the optimal trade-offs between different performance criteria.
Additionally, the framework can incorporate additional constraints or objectives related to energy consumption, task deadlines, or other performance metrics. By defining constraints that capture the desired levels of energy efficiency or task completion time, the optimization problem can be extended to consider these factors alongside disturbance robustness. This extension would involve modifying the objective function and constraints in the optimization algorithm to reflect the multi-objective nature of the problem.
Moreover, the framework can utilize advanced optimization techniques such as multi-objective evolutionary algorithms or multi-criteria decision-making methods to efficiently explore the trade-offs between different performance metrics. These algorithms can help identify the Pareto-optimal solutions that represent the best compromises between disturbance robustness, energy efficiency, task completion time, and other objectives, providing decision-makers with valuable insights into the performance trade-offs in the control synthesis process.