The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective

Maxim, Anca; Copot, Dana; Copot, Cosmin; Ionescu, Clara-Mihaela

doi:10.3390/inventions4010010

Cited by 51 publications

(35 citation statements)

References 50 publications

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“…In the same line, a survey conducted among the industrial committee members' of the International Federation of Automatic Control published in [5] shows the dominant position of PID algorithms with respect to other advanced control strategies as MPC. Recently, the primordial role of PID in the context of Industry 4.0 has been highlighted in [6], as well as the necessity of introducing new features to adapt this well established control technology to this new paradigm, whose one of the most distinctive signs is the high connectivity between devices through wired and wireless communication networks. This fact, jointly with the dominance of PID in industry mentioned before, has encouraged the development of many researches about the event-based PID controllers during the last decades.…”

Section: Introductionmentioning

confidence: 99%

Regular quantisation with hysteresis: a new sampling strategy for event‐based PID control systems

Miguel-Escrig¹,

Romero-Pérez²

2020

IET Control Theory & Appl

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In this study, a new sampling strategy for networked control systems, called regular quantification with hysteresis (RQH), is proposed. This alternative presents some benefits with respect to symmetric-send-on-delta sampling, which is one of the most used strategies in the event-based proportional-integral-derivative (PID) control loops. The behaviour of the RQH is defined by two parameters, the signal quantification and hysteresis, whose effect on the overall system performance is studied and guidelines about its choice are given in terms of noise measurement and steady-state error. The limit cycle oscillations that could be induced by this sampling strategy are studied and new robustness measures to avoid them are proposed based on the describing function approach. The suitability of some tuning rules for continuous PI when applied to control systems with a RQH sampling is evaluated using the proposed measures. The results show that these tuning rules can be applied under certain conditions.

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Section: Introductionmentioning

confidence: 99%

Regular quantisation with hysteresis: a new sampling strategy for event‐based PID control systems

Miguel-Escrig¹,

Romero-Pérez²

2020

IET Control Theory & Appl

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“…The methodology is suitable for hands-on tuning of MPC. Compared with PID control, it is discussed about when, where and why to apply the MPC with the advent of Industry 4.0 [10].…”

Section: Introductionmentioning

confidence: 99%

The Potential of Fractional Order Distributed MPC Applied to Steam/Water Loop in Large Scale Ships

et al. 2020

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The steam/water loop is a crucial part of a steam power plant. However, satisfying control performance is difficult to obtain due to the frequent disturbance and load fluctuation. A fractional order model predictive control was studied in this paper to improve the control performance of the steam/water loop. Firstly, the dynamic of the steam/water loop was introduced in large-scale ships. Then, the model predictive control with an extended prediction self adaptive controller framework was designed for the steam/water loop with a distributed scheme. Instead of an integer cost function, a fractional order cost function was applied in the model predictive control optimization step. The superiority of the fractional order model predictive control was validated with reference tracking and load fluctuation experiments.

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“…In 2019, Maxim et al [66] investigated how, when, and why to choose model predictive control over proportional-integral-derivative control. With this backdrop, the goal of the research presented in this manuscript is to develop and then examine several variations of classical and modern era optimal controllers and compare their performance against each other.…”

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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The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective

Cited by 51 publications

References 50 publications

Regular quantisation with hysteresis: a new sampling strategy for event‐based PID control systems

Regular quantisation with hysteresis: a new sampling strategy for event‐based PID control systems

The Potential of Fractional Order Distributed MPC Applied to Steam/Water Loop in Large Scale Ships

Comparison and Interpretation Methods for Predictive Control of Mechanics

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