Robust Control Co-Design with Receding-Horizon MPC

Nash, Austin L.; Pangborn, Herschel C.; Jain, Neera

doi:10.23919/acc50511.2021.9483216

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The MPC controller is a more advanced method of process control with the ability to deal with the constraints used for MIMO systems (multiple inputs, multiple outputs) compared with the PID controller that has no knowledge about the constraints. An MPC can be considered as a multi-variable control algorithm that relies on an internal dynamic model of the process, a cost function J over the receding horizon [24], which is a time window for the prediction, and an optimization algorithm minimizing the cost function J using the control inputs.…”

Section: The Predictive Controllermentioning

confidence: 99%

Path Following for an Omnidirectional Robot Using a Non-Linear Model Predictive Controller for Intelligent Warehouses

Galati

Mantriota

2023

Robotics

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This paper presents results coming from a non-linear model predictive controller used to generate optimized trajectories specifically for an omnidirectional robot equipped with a spraying unit to mark on the floor the perimeter of dangerous areas or to move large palletized goods inside warehouses. Results on different trajectories and with moving obstacles are provided along with considerations on the controller performance.

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Section: The Predictive Controllermentioning

confidence: 99%

Path Following for an Omnidirectional Robot Using a Non-Linear Model Predictive Controller for Intelligent Warehouses

Galati

Mantriota

2023

Robotics

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“…In this section, we start by discussing the optimal control structures under uncertainties in UCCD problems. These structures attempt to answer inherently different questions and have been used in separate studies in the literature [20,[25][26][27][28][29][30][31][32][33]. While these structures have a significant impact on the problem implementation and its associated solution, they have not been collectively discussed in the literature to the best of the authors' knowledge.…”

Section: Uccd Implementationmentioning

confidence: 99%

“…In this article, we introduce three distinct control structures that highlight the role of control decisions in response to uncertainties: (i) open-loop multiple-control (OLMC), (ii) multi-stage control (MSC), and (iii) open-loop single-control (OLSC). These structures are related to concepts from stochastic control [3,13,14], robust multi-stage model predictive control (MPC) [15,16], and worst-case robust control [17][18][19][20], respectively. Insights from our discussion on control structures, presented in Sec.…”

Section: Introductionmentioning

confidence: 99%

Investigations Into Uncertain Control Co-Design Implementations for Stochastic in Expectation and Worst-Case Robust

Azad

Herber

2022

Volume 5: Dynamics, Vibration, and Control

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As uncertainty considerations become increasingly important aspects of concurrent plant and control optimization, it is imperative to identify and compare the impact of uncertain control co-design (UCCD) formulations on their associated solutions. While previous work has developed the theory for various UCCD formulations, their implementation, along with an in-depth discussion of the structure of UCCD problems, implicit assumptions, method-dependent considerations, and practical insights, is currently missing from the literature. Therefore, in this study, we address some of these limitations by proposing two optimal control structures for UCCD problems that we refer to as the open-loop single-control (OLSC) and open-loop multiple-control (OLMC). Next, we implement the stochastic in expectation UCCD (SE-UCCD) and worst-case robust UCCD (WCR-UCCD) for a simplified strain-actuated solar array (SASA) case study. For the implementation of SE-UCCD, we use generalized Polynomial Chaos expansion and benchmark the results against Monte Carlo Simulation. Next, we solve a simple SASA WCR-UCCD through OLSC and OLMC structures. Insights from such implementations indicate that constructing, implementing, and solving a UCCD problem requires an in-depth understanding of the problem at hand, formulations, and solution strategies to best address the underlying co-design under uncertainty questions.

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“…To reduce the computational burden in MPC, the authors of [18] proposed a control scheme with the adaptive transmission intervals via a sample-and-hold way for nonholonomic systems with multiple constraints. A co-design technique was developed for event-triggered control and MPC in [19], where triggering conditions and control inputs were jointly designed to achieve potentially less conservative results. In [20], two EMPCs guaranteed that the system state of the closed-loop system remained in a robust control invariant set and the recursive feasibility of terminal constraints, respectively, and in addition, the sample-hold approach was used to avoid the transmission of continuous predictive control input trajectories.…”

Section: Introductionmentioning

confidence: 99%

A Gradient-Based Event-Driven MPC for Nonlinear Systems With Additive Disturbances Using State-Dependent Thresholds

2022

IEEE Access

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In this article, we propose a gradient-based event-driven model predictive control (GEMPC) algorithm with a state-dependent threshold for nonlinear systems with additive disturbances and input and state constraints. Firstly, a novel gradient-based event-driven strategy is constructed in the light of the error gradient between the optimal prediction of the state and the real one, which could ensure the Zeno-free property via a positive triggering interval. Subsequently, the novel triggering mechanism and the dualmode control are combined to establish a GEMPC framework, to further reduce the computing burden and communication transmission especially when the computational resources are limited. Additionally, the feasibility of the GEMPC algorithm and the input-to-state practical stability (ISpS) property of the considered system have been strictly proved in theory. Finally, the simulation comparison results on control of a perturbed nonlinear system are utilized to show the validity of the GEMPC algorithm.INDEX TERMS Event-driven control, model predictive control (MPC), gradient-based mechanism, constrained systems, state-dependent threshold.

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Robust Control Co-Design with Receding-Horizon MPC

Cited by 10 publications

References 18 publications

Path Following for an Omnidirectional Robot Using a Non-Linear Model Predictive Controller for Intelligent Warehouses

Path Following for an Omnidirectional Robot Using a Non-Linear Model Predictive Controller for Intelligent Warehouses

Investigations Into Uncertain Control Co-Design Implementations for Stochastic in Expectation and Worst-Case Robust

A Gradient-Based Event-Driven MPC for Nonlinear Systems With Additive Disturbances Using State-Dependent Thresholds

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