Journal ArticleDOI
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
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TLDR
In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.About:
This article is published in Journal of Process Control.The article was published on 2013-10-01. It has received 291 citations till now. The article focuses on the topics: Model predictive control & Robust control.read more
Citations
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Proceedings ArticleDOI
Asymptotically Stabilizing Multi-Stage Model Predictive Control
TL;DR: In this article, the authors proposed an asymptotically stabilizing formulation of multi-stage nonlinear model predictive control (NMPC) for plants with state and input dependent uncertainties.
Book ChapterDOI
Designing of Fractional Order Controller Using SQP Algorithm for Industrial Scale Polymerization Reactor
TL;DR: In this paper, a fractional order-based PID controller designed using SQP algorithm and compared to a PID controller regarding time response characteristics is presented. The objective is to minimize settling time and overshoot for the MIMO process including parameters to be controlled and pairing of controlled and manipulated variables.
Journal ArticleDOI
Evaluation of Different Control Strategies for Trajectory Following of a Robotic Capsule Endoscope Under Rotating Magnetic Actuation
TL;DR: In this article , the authors investigated the trajectory following problem of a robotic capsule under rotating magnetic actuation, in order to realize efficient and accurate navigation of the capsule in the narrow, complex intestinal environments.
Proceedings ArticleDOI
On Optimal Control Based on Parametric Gradient Approximations for Nonlinear Systems with Stochastic Parameters
TL;DR: This paper presents a design method for a suboptimal feedback controller to minimize the expectation of a cost function for uncertain nonlinear systems by combining a parametric approximation with a gradient-based optimization method.
Journal ArticleDOI
Multi-step Greedy Reinforcement Learning Based on Model Predictive Control
Yucheng Yang,Sergio Lucia +1 more
TL;DR: This paper proposes a novel modelbased reinforcement learning approach that exploits all the information of a model predictive control computing step, and not only the first input that is actually applied to the plant, to efficiently learn a good approximation of the state value function.
References
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Journal ArticleDOI
Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
Journal ArticleDOI
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
TL;DR: An SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems is discussed and a reduced-Hessian semidefinite QP solver (SQOPT) is discussed.
Journal ArticleDOI
Robust model predictive control of constrained linear systems with bounded disturbances
TL;DR: This paper provides a novel solution to the problem of robust model predictive control of constrained, linear, discrete-time systems in the presence of bounded disturbances by solving the optimal control problem that is solved online.
Journal ArticleDOI
A Multiple Shooting Algorithm for Direct Solution of Optimal Control Problems
Hans Georg Bock,K.J. Plitt +1 more
TL;DR: A condensing algorithm for the solution of the approximating linearly constrained quadratic subproblems, and high rank update procedures are introduced, which are especially suited for optimal control problems and lead to significant improvements of the convergence behaviour and reductions of computing time and storage requirements.
Journal ArticleDOI
Scenarios and policy aggregation in optimization under uncertainty
TL;DR: This paper develops for the first time a rigorous algorithmic procedure for determining a robust decision policy in response to any weighting of the scenarios.