Successive linearization based model predictive control of variable stiffness actuated robots

Zhakatayev, Altay; Rakhim, Bexultan; Adiyatov, Olzhas; Baimyshev, Almaskhan; Varol, Hüseyin Atakan

doi:10.1109/aim.2017.8014275

Cited by 19 publications

(23 citation statements)

References 20 publications

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“…However, the mentioned problem was not observed in this research. The steps of developing the SLMPC can be found in detail in [23] and here we bring forth the most important components of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…The power demand used in the study ranges from 0 to 60 kW so an MPC developed for the system linearized around a single OP would fail to fulfill all the control goals. To cope with the nonlinearity, a successive linearization based MPC (SLMPC) [23][24][25][26] is used meaning that the system is continuously linearized every time step during the simulation and a linear MPC developed for that particular point in the state-space ensuring the validity of the MPC at all times. However, the linearization point is not a steady-state one and the treatment of this is described in Section 4.…”

Section: Introductionmentioning

confidence: 99%

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Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data

2020

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In this paper, a polymer electrolyte membrane fuel cell (PEMFC) stack control study is presented. The goal is to track the transient power demand of a real fuel cell (FC) vehicle while ensuring safe and efficient operation. Due to the dynamically changing power demand, fast transients occur in the internal states of the fuel cell (e.g., pressure, humidity, reactant mass) leading to degradation effects (e.g., high/low membrane overpressure, reactants starvation) which are avoided by imposing safety constraints. Efficiency is considered in terms of internal voltage losses minimization as well as minimization of the power of the compressor used to pressurize the cathode. For solving the optimization problem of power demand tracking, adhering to safety constraints, and maximizing efficiency, model predictive control (MPC) has been chosen. Due to the nonlinearity of the FC system, a successive linearization based MPC (SLMPC) is used to control the FC throughout its operating region. Simulation results show that the power demand can be fulfilled while at the same time ensuring safe operation in terms of adhering to constraints and that the minimization of internal voltage losses and compressor power lead to an approximate 9.5% less hydrogen consumption than in the actual reference vehicle.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data

2020

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“…In this subsection, the successive linearization framework will be presented and later on used by the controller. This way, nonlinear systems can be easily controlled using the concept of the successive-linearization-based MPC [19,25,27,28,30]. An illustration of the concept can be seen in Figure 6.…”

Section: Successive Linearizationmentioning

confidence: 99%

“…Due to present direct-feedthrough in the model, the D k matrix shows up in Equation (25). To address this issue, the optimal control move is calculated for the next time step i + 1, incorporating the last solution ∆u i as a predicted disturbance and rendering the system causal.…”

Section: Mpc Formulationmentioning

confidence: 99%

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Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle

2021

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In this paper, a real-time capable reference governor superordinate model predictive controller (RG-MPC) is developed for fuel cell (FC) control suitable for automotive application. The RG-MPC provides reference trajectories for the subordinate proportional-integral (PI) controllers, which act directly on the FC system. Antiwindup and decoupling schemes, which are common problems in multivariable PI control, are unnecessary, given that the RG-MPC can inherently consider constraints and multivariable systems. The PI dynamics are incorporated into the prediction model used for control, ensuring the feasibility of the provided references for the PI controllers. The successive linearization technique is used in the RG-MPC to cope with the model’s nonlinear nature in real-time. The concept has been illustrated in a simulation scenario featuring efficient and safe power control of an FC stack in automotive application using real driving data obtained from an in-house-built FC vehicle. This work is the first step towards upgrading an existing, PI-based control scheme without the necessity of completely rebuilding the interface.

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Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge technologies to meet the growing demand for life‐saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real‐time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

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Successive linearization based model predictive control of variable stiffness actuated robots

Cited by 19 publications

References 20 publications

Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data

Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data

Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle

Control strategy for biopharmaceutical production by model predictive control

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