Model Predictive Control of the Exit Part Temperature for an Austenitization Furnace

Ganesh, Hari S.; Edgar, Thomas F.; Bâldea, Michael

doi:10.3390/pr4040053

Cited by 21 publications

(11 citation statements)

References 31 publications

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“…[48] Genesh et al used a model predictive control system developed to control furnace exit temperature to prevent parts from overheating in a quench hardening process. Knowing only a proportional state and time, a mere 5.3% efficiency increase was achievable [49].…”

Section: Introductionmentioning

confidence: 99%

Comparison and Interpretation Methods for Predictive Control of Mechanics

Sands

2019

Algorithms

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Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.

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Section: Introductionmentioning

confidence: 99%

Comparison and Interpretation Methods for Predictive Control of Mechanics

Sands

2019

Algorithms

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“…In recent years, the model predictive control (MPC) technology has been well developed due to its conceptual simplicity, high dynamic response, and easy implementation etc. [11]. As mentioned in the work by the authors of [12], the MPC is one of the most popular control strategy in industrial applications, where satisfactory system performances can be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Extended State Observer-Based Predictive Speed Control for Permanent Magnet Linear Synchronous Motor

Wang

Che

et al. 2019

Processes

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Combining the feedback of predictive function control and the feedforward of extended state observer, a composite control strategy is proposed for the permanent magnet linear synchronous motor (PMLSM). The mathematical model of the PMLSM vector control system is established based on the basic structure and operation mechanism of PMLSM. Then, a speed regulator based on predictive function control (PFC) is designed to improve the speed tracking performance of the PMLSM drive system. The state and disturbance of the PMLSM system estimated by the extended state observer (ESO) transferred to the PMLSM drive system, and the robustness of the drive system will be improved. Comparative simulation and experiment results show that the proposed method has better speed tracking performance and disturbance rejection property.

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“…Thus, the system identification methods have been applied to the temperature control system, and the most popular method is the step response method [14][15][16]. With the mathematical model of the system, the Model Predictive Control (MPC) method has been proposed on the heating system to provide precise control [17].…”

Section: Introductionmentioning

confidence: 99%

Slow Mode-Based Control Method for Multi-Point Temperature Control System

Hashimoto

Jiang

et al. 2019

Processes

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In recent years, thermal processing systems with integrated temperature control have been increasingly needed to achieve high quality and high performance. In this paper, responding to the growing demands for proper transient response and to provide more accurate temperature controls, a novel slow-mode-based control (SMBC) method is proposed for multi-point temperature control systems. In the proposed method, the temperature differences and the transient response of all points can be controlled and improved by making the output of the fast modes follow that of the slow mode. Both simulations and experiments were carried out, and the results were compared to conventional control methods in order to verify the effectiveness of the proposed method.

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Model Predictive Control of the Exit Part Temperature for an Austenitization Furnace

Cited by 21 publications

References 31 publications

Comparison and Interpretation Methods for Predictive Control of Mechanics

Comparison and Interpretation Methods for Predictive Control of Mechanics

Extended State Observer-Based Predictive Speed Control for Permanent Magnet Linear Synchronous Motor

Slow Mode-Based Control Method for Multi-Point Temperature Control System

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