FPGA Implementation Framework for Explicit Hybrid Model Predictive Control

Patne, Vaishali; Ingole, Deepak; Sonawane, Dayaram

doi:10.1016/j.ifacol.2020.06.061

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Hybrid MPC is another solution for systems with continuous-valued states and discrete-valued states. Patne et al [12] presented an FPGA application of HMPC on an IPC system, wherein a step-by-step technique for FPGA implementation using the inverted pendulum model is demonstrated. A further solution is Robust MPC (RMPC) to control nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Noise and Disturbance Rejection Performance Evaluation on Explicit Model Predictive Control Technique Applied to Inverted Pendulum with Various Test Scenarios

ÖZGÜR¹,

Özbek²,

Sarigecili

2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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An inverted pendulum on a cart (IPC) system, which is a widely used test environment for controller design due to ease of applicability, has the opportunity to be applied in different fields with nonlinear and under-actuated characteristics. In this study, the performance of the explicit MPC control method has been examined against the noise and disturbances by using two test cases and analysis approaches. Different trajectory tracking, disturbance, and noise situations have been taken into account in the elaborated scenarios. The numerical applications have been performed by the model predictive control toolbox of Matlab®/Simulink®. The advantages and drawbacks of the controller have been discussed in terms of time-domain specifications.

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Section: Introductionmentioning

confidence: 99%

Noise and Disturbance Rejection Performance Evaluation on Explicit Model Predictive Control Technique Applied to Inverted Pendulum with Various Test Scenarios

ÖZGÜR¹,

Özbek²,

Sarigecili

2022

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Summary of Model Predictive Control Methods in Motor Drive System

Yang

2021

2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE)

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Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications

Vansovits

Petlenkov

Tepljakov

et al. 2022

Sensors

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In the present paper, a software framework comprising the implementation of Model Predictive Control—a popular industrial control method—is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control—a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant.

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FPGA Implementation Framework for Explicit Hybrid Model Predictive Control

Cited by 3 publications

References 14 publications

Noise and Disturbance Rejection Performance Evaluation on Explicit Model Predictive Control Technique Applied to Inverted Pendulum with Various Test Scenarios

Noise and Disturbance Rejection Performance Evaluation on Explicit Model Predictive Control Technique Applied to Inverted Pendulum with Various Test Scenarios

Summary of Model Predictive Control Methods in Motor Drive System

Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications

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