Robust Tuning for Classical MPC through the Multi-scenarios Approach

Santos, José Eduardo Weber dos; Trierweiler, Jorge Otávio; Farenzena, Marcelo

doi:10.1021/acs.iecr.8b05485

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…In their proposed approach, they use an interactive decision tree to get feedback from the operator and infer the optimal gain weights. Researchers in [39] proposed a multi-scenario approach for designing a robust MPC system. They evaluated the operational system for each scenario and considered them while tuning the MPC.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Azgomi

Faghih

2022

IEEE Open J. Eng. Med. Biol.

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We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledgebased layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledgebased layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

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Section: Introductionmentioning

confidence: 99%

Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Azgomi

Faghih

2022

IEEE Open J. Eng. Med. Biol.

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“…Numerous researchers [27]- [32] have made significant efforts on effective weight tuning methods to enhance stability and error performance. Several empirical tuning guidelines with heuristic rules can be found in the literature [33].…”

Section: Introductionmentioning

confidence: 99%

Automatic Weight Determination in Model Predictive Control for Personalized Car-Following Control

Lim¹,

Lee²,

Yang

et al. 2022

IEEE Access

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Car-following control is a fundamental application of autonomous driving. This control has multiple objectives, including tracking a safe distance to a preceding vehicle and enhancing driving comfort. Model Predictive Control (MPC) is a powerful method due to its intuitiveness and capability to cover multiple objectives. MPC determines the relative importance of objectives through a set of weight factors, depending on which, the controller's behavior changes even if the traffic situations are the same. However, determining the optimal weight is not a trivial problem because there is no benchmark to evaluate the performance of the weight, and searching for weight factors with repeated driving experiments is timeconsuming. To solve this problem, we proposed an automatic tuning method to determine the weights of the MPC based on personal driving data. Personal driving data under naturalistic driving conditions provide car-following situations and driver's behaviors. These data can generate a reference model to represent the driver's driving style. Based on this model, the proposed method defined the automatic tuning problem as an optimization problem that minimizes the difference between the reference and the controller's response using the optimal weight factors. This optimization problem was solved using the Particle Swarm Optimization algorithm. The proposed method was implemented with an embedded optimization coder in an offline fashion. Its performance was evaluated using personal driving data. From this, the proposed method can reduce the effort and time required for an engineer to find the optimal weight factors.

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“…In [10], the authors further proposed a rapid tuning strategy based on the closed-loop system structure for MPC parameters for MIMO paper-making system with first-orderplus-dead-time subsystems and uncertain model parameters. In [11], by adopting the sequential procedure, the authors developed a tuning method that took the reachable trajectories of each operating point of the controlled system as the reference to pursue an improved robust performance.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach for Robust MPC Tuning Based on Machine Learning

Zhang

2021

Mathematical Problems in Engineering

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A robust tuning method based on an artificial neural network for model predictive control (MPC) of industrial systems with parametric uncertainties is put forward in this work. Firstly, an efficient approach to characterize the mapping relationship between the controller parameters and the robust performance indices is established. As there are normally multiple conflicted robust performance indices to be considered in MPC tuning, the neural network is further used to fuse the indices to produce a simple label representing the acceptable level of the robust performance. Finally, an automated algorithm is proposed to tune the MPC parameters for the considered uncertain system to achieve the desired robust performance. In addition, the regulation of the pH value of the sewage treatment system is used to verify the effectiveness of the robust tuning algorithm which is described in this paper.

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Robust Tuning for Classical MPC through the Multi-scenarios Approach

Cited by 13 publications

References 22 publications

Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Enhancement of Closed-Loop Cognitive Stress Regulation Using Supervised Control Architectures

Automatic Weight Determination in Model Predictive Control for Personalized Car-Following Control

An Improved Approach for Robust MPC Tuning Based on Machine Learning

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