Concurrent Learning Adaptive Model Predictive Control

Chowdhary, Girish; Mühlegg, Maximilian; How, Jonathan P.; Holzapfel, Florian

doi:10.1007/978-3-642-38253-6_3

Cited by 41 publications

(36 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These techniques have been successfully employed for flight vehicle control. 16,17 However, the main limitation of the concurrent learning approach, and in fact many other adaptive control and online system identification approaches, is that they utilize an a priori fixed structure for modeling the unknown system dynamics, the parameters of which are learned online. Adaptive control literature also consists of hybrid direct-indirect control architectures.…”

Section: Figurementioning

confidence: 99%

“…Such techniques have been implemented, eg, for the altitude control of a microsatellite. 16 In the past, we have also applied MPC successfully together with concurrent learning adaptive controllers using a switched system architecture based on a criterion on the quality of the uncertainty approximation. 33 However, the performance of MPC can depend on how the accuracy of the employed predictive model of the system dynamics is.…”

Section: Related Workmentioning

confidence: 99%

“…32 With advent in compact computing resources, authors have recently proposed and employed MPC architectures for real-time control of aerospace vehicles. 16,34 The underlying idea is that if the data points are stored at a time when the regressors were excited, this information can be used in future updates to achieve parameter convergence. Several authors have proposed learning-based MPC algorithms [7][8][9] ; however, the presence of learning transients prevents a general nonconservative solution to be formed.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive‐optimal control under time‐varying stochastic uncertainty using past learning

Abdollahi

Chowdhary²

2019

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

An adaptive-optimal control architecture is presented for adaptive control of constrained aerospace systems with matched uncertainties that are subject to dynamic stochastic change. The architecture brings together three key elements, ie, model predictive control-based reference command shaping, Gaussian process (GP)-based Bayesian nonparametric model reference adaptive control (MRAC), and online GP clustering over nonstationary GPs. Model predictive control optimizes reference model and its shaped output is passed into GP-based MRAC, which is used to learn the model in presence of significant time-varying stochastic uncertainty while maintaining stability. Based on a likelihood ratio test, the changepoints are detected and learned. Lastly, the models are created and clustered by non-Bayesian clustering algorithm. The key salient feature of our architecture is that not only can it detect changes but also it uses online GP clustering to enable the controller to utilize past learning of similar models to significantly reduce learning transients. Furthermore, persistence of excitation conditions are significantly relaxed due to the use of GP-MRAC. Stability of the architecture is argued theoretically and performance is validated empirically on different scenarios for wing rock dynamics.

show abstract

Section: Figurementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive‐optimal control under time‐varying stochastic uncertainty using past learning

Abdollahi

Chowdhary²

2019

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

show abstract

“…23 In the past, we have also applied MPC successfully together with concurrent learning adaptive controllers using a switched system architecture based on a criterion on the quality of the uncertainty approximation. 24,25 Shaping various signals to improve certain performance characteristics has attracted increasing interest in recent years. Shaping the reference command in an optimal sense has become known as the reference governor.…”

Section: Introductionmentioning

confidence: 99%

“…31 In this paper we propose a non-switching concurrent learning adaptive-optimal controller where the exogenous reference commands are shaped based on an a-priori determined optimal solution of a chosen linear reference model. One of the main challenges in implementing MPC, and specifically the architectures in, 24,25 is to guarantee the feasibility of obtaining an optimal solution online on resource constrained aircraft. In this paper, this problem is tackled by solving offline the optimal control problem for the linear reference model in a predescribed parameter space.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Reference Commands for Concurrent Learning Adaptive-Optimal Control of Uncertain Dynamical Systems

Mühlegg

Chowdhary

Holzapfel

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

Self Cite

View full text Add to dashboard Cite

Optimal control of autonomous aircraft with modeling uncertainties is a challenging problem, especially when onboard computational resources are limited, and in presence of modeling uncertainty. A concurrent learning based adaptive-optimal control architecture is presented that is suitable for implementation on resource constrained platforms. Exponential parameter convergence properties of concurrent learning adaptive controllers are leveraged to reduce modeling uncertainty through adaptation. A multiparametric quadratic optimization based model predictive control approach is used to optimally shape the reference command. Since the reference model is preselected in our approach, the optimal solutions for several flight conditions can be generated a-priori. Hence, the optimal control problem does not need to be solved online, significantly reducing the computational burden. Exponentially convergent stability bounds are presented for the entire adaptiveoptimal control architecture. Numerical simulations show significant increase in controller performance under input and state constraints.

show abstract

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Doerr,

Albee,

Ekal

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

show abstract

Concurrent Learning Adaptive Model Predictive Control

Cited by 41 publications

References 29 publications

Adaptive‐optimal control under time‐varying stochastic uncertainty using past learning

Adaptive‐optimal control under time‐varying stochastic uncertainty using past learning

Optimizing Reference Commands for Concurrent Learning Adaptive-Optimal Control of Uncertain Dynamical Systems

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Contact Info

Product

Resources

About