An adaptive control study for the DC motor using meta-heuristic algorithms

Rodríguez-Molina, Alejandro; Villarreal-Cervantes, Miguel Gabriel; Aldape-Pérez, Mario

doi:10.1007/s00500-017-2797-y

Cited by 23 publications

(17 citation statements)

References 30 publications

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“…Regarding the process scheduling of a machinery system, the focus has been mostly towards estimating and improving the efficiency of a machine operation. The "adaptive scheduling" concept is proposed to solve the parametric uncertainty problems, such as when there is an extra noise or an operation environment is dynamic [9]. In other words, the goal is to estimate or learn machine parameters online that lead to control signals for a desired performance [27].…”

Section: Related Workmentioning

confidence: 99%

“…As for the scheduling, we are agnostic to the algorithm used. We select a state-of-the-art genetic algorithm (GA) as it is one of the most commonly used metaheuristic adaptive control techniques [9]. Each chromosome in the GA model describes a different operation mode, in our case a range of nominal speed and power values for the conveyor to safely operate, which are obtained from the fingerprint.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Görür

Sivrikaya³

2021

Applied Sciences

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Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such prognostics in process scheduling. The existing solutions mostly integrate preventive approaches to protect the machines, usually causing downtimes. The premise of this study is to develop a process scheduling mechanism that selects an acceptable operating condition for an industrial process to adapt to the predicted anomalies. As PM is largely a data-driven approach (hence, it relies on the setup), we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time, adaptive process scheduling mechanism. According to the abnormal readings, it schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. To demonstrate the pipeline on the action, we implement our approach on a small-scale conveyor belt, utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control retains an efficient process under abnormal conditions with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Görür

Sivrikaya³

2021

Applied Sciences

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“…Equation 3, which satisfies the non-holonomic constraint A(q)q = 0, can be written in terms of the internal state variableη by substituting (6) and 7in (3), as shown in (9) - (11),…”

Section: General Coupled Inverse Dynamic Model Of a MMmentioning

confidence: 99%

“…It has been necessary to develop alternatives either for the design of the robot structure or in the controller type. In the design area, optimization problems have been generated for the synthesis of planar mechanisms based on their dynamics [10], and for the adaptive control of motors, based on metaheuristic algorithms [11]. In the control area, new techniques of artificial intelligence have been used for the control of manipulators, as observed in [12]- [16].…”

Section: Introductionmentioning

confidence: 99%

Tuning a PD+ Controller by Means of Dynamic Optimization in a Mobile Manipulator With Coupled Dynamics

et al. 2019

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This article presents the development of a dynamic optimization method for controller tuning. This is proposed because most traditional tuning methods for complex coupled dynamic models are based on experience, and thus lacking accuracy. The case study is a Mobile Manipulator that consists of an anthropomorphic manipulator and a differential mobile platform. The system model has a trajectory generator that includes the coupled kinematic model, the Jacobian model, the coupled dynamic model, and a Proportional-Derivative plus controller. The tuning of the model is obtained by solving an optimization problem, using the Differential Evolution algorithm. This optimization approach allows to minimize simultaneously the energy consumption and the error on the trajectory tracking by the end effector. A novel strategy is applied to formulate the objective function, including constant weights for balancing the minimization effect. The objective is to avoid an energy consumption equal to zero that represents an error condition of no motion. The results of the case study and its statistical analysis are presented. The best result was modeled in Solidworks R and simulated in Matlab R. This model was exported to Simscape Multibody TM of Matlab, and its simulation produced satisfactory results, suggesting that the proposed optimization method can be a useful tool to solve real engineering problems.

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“…A study of an adaptive control for the DC motor using meta-heuristic algorithms explained the simulation results that proposed adaptive control strategies as a viable alternative to regulate the speed of the motor subject to different operation scenarios. The comparative analysis with a robust control approach revealed the advantages of the adaptive strategy based on the meta-heuristic techniques in the velocity regulation of the DC motor [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid FLC-LMS Algorithm for Predicting Sediment Volume in the River

Prasetyowati¹

2021

IJIES

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Problems such as flooding, various diseases from microbial pathogens, rivers becoming slum and unsightly, reduced availability: The data found that 82% of the 550 rivers in Indonesia were polluted and in critical condition. The high level of pollution caused various of clean water and many more. Sediment was one of the causes of that problem. There have been many studies detecting objects in the water, but no one predicted the volume of objects in the water. The purpose of this research is to make a device to detect the volume of sediment in the rivers to improve the performance of river monitoring. A device was equipped with an infrared sensor. Raising and lowering the sensor under the device was controlled by the front DC motor with a Fuzzy Logic Controller (FLC) algorithm using two input and one output. Error (E) and change of error (CE) were inputs for FLC and change of control was the output. The fuzzy membership function depended on the distance of the sensor to the sediment. The number of membership functions used was less than the previous research, so the calculation was much simple. This research used hybrid FLC and Adaptive LMS predictive algorithm. Fuzzy Logic Controller was for controlling the Front DC motor when raising and lowering the sensor and Adaptive LMS predictive algorithm was for predicting the volume and minimalizing the error calculation. PID control was also used as a comparison. The results showed that using the FLC algorithm can find sediment in less than four seconds with an error rate of 0.0005, while the PID sensor found sediment in more than 4 seconds with an error rate of 0.001. In addition, the Adaptive LMS predictive algorithm minimized the error calculation with an accuracy level of 99.66 percent.

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An adaptive control study for the DC motor using meta-heuristic algorithms

Cited by 23 publications

References 30 publications

Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process

Tuning a PD+ Controller by Means of Dynamic Optimization in a Mobile Manipulator With Coupled Dynamics

Hybrid FLC-LMS Algorithm for Predicting Sediment Volume in the River

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