Design of the Linear Quadratic Structure Based Predictive Functional Control for Industrial Processes Against Partial Actuator Failures Using GA Optimization

Hu, Xiaomin; Zou, Hongbo; Wang, Limin

doi:10.1007/s12555-018-0365-6

Cited by 9 publications

(4 citation statements)

References 39 publications

(36 reference statements)

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“…us, the PEO is used in this work. It is worthy to be mentioned that compared with the previous studies [17,18], the proposed control method extends the integer-order PFC to the fractional version and uses PEO algorithm to tune the main parameters in FOPFC, which is superior to the GA used in [17,18]. In addition, the existing work [20] designed the parameter FOPFC by the trial-and-error method, while this paper uses the PEO algorithm to tune the related parameters in FOPFC and applies FOPFC to solving the industrial processes under partial actuator failures.…”

Section: Introductionmentioning

confidence: 86%

“…To alleviate this deficiency, a genetic algorithm-(GA-) based PFC was proposed, where GA was used to tune its weighting factors, and six cases of partial actuator failures were used to demonstrate the performance of GAbased PFC [17]. Besides, Hu et al [18] combined PFC with GA and linear quadratic structure for industrial processes against the partial actuator failures. Although a lot of good results have been obtained by PFC strategies, the framework of PFC design for industrial control processes still needs to be further explored for achieving high-quality control performance.…”

Section: Introductionmentioning

confidence: 99%

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Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

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As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.

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Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

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“…is controller has been successfully applied in the injection molding process to control the screw speed and hydraulic back pressure [178,179], coolant flow, coolant temperature [180], and cavity pressure [181,182]. e group of Zhang et al [166,167,170,173,183] designed fault-tolerant control through a predictive functional control framework for a batch process with unknown disturbances and partial actuator failures to achieve the desired closed-loop response.…”

Section: Proportional-integral-derivative (Pid) Controllermentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

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Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

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“…LQ Control which was optimized using GA was implemented for swarm control of UAV, though it is for quadcopters, instead of bicopters [25]. Another work mentioned in [26] also proves that GA managed to get the most optimal parameter of LQ Control. In [27], the combination of LQ Control and GA results shows a more optimal system response compared to LQ although it is applied to fixed-wing aircraft.…”

Section: Introductionmentioning

confidence: 96%

Genetic algorithm-enhanced linear quadratic control for balancing bicopter system with non-zero set point

Apriaskar,

Prastiyanto,

Utomo

et al. 2023

J. Mechatron. Electr. Power Veh. Technol.

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Bicopter is an unmanned aerial vehicle (UAV) with the advantage of saving energy consumption. However, the unique two rotors design presents a challenge in designing a controller that achieves good stability, fast settling time, and the ability to overcome oscillations simultaneously. This article proposes a new control method for bicopter that uses a genetic algorithm optimization approach in the linear quadratic (LQ-GA) control method. The GA is used to search for the best weighting matrix parameters, Q and R, in the Linear Quadratic (LQ) control scheme. The proposed control method was tested on a balancing bicopter test platform with an input in the form of difference in pulse width modulation (PWM) signals for both rotors and an output in the form of roll angle. The control system was evaluated based on the stability of the transient response and the generated control signal. The results of the tests showed that the proposed LQ-GA control method has better stability, faster settling time, and smaller overshoot than the existing PI and standard LQ control methods. Therefore, the proposed LQ-GA control method is the most suitable for use in a balancing bicopter system with a non-zero setpoint.

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Design of the Linear Quadratic Structure Based Predictive Functional Control for Industrial Processes Against Partial Actuator Failures Using GA Optimization

Cited by 9 publications

References 39 publications

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Intelligent Injection Molding on Sensing, Optimization, and Control

Genetic algorithm-enhanced linear quadratic control for balancing bicopter system with non-zero set point

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