Biological pest control using a model‐based robust feedback

Puebla, Héctor; Roy, Priti Kumar; Velasco-Perez, Alejandra; González-Brambila, M.

doi:10.1049/iet-syb.2018.5010

Cited by 18 publications

(16 citation statements)

References 40 publications

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“…In this model, pests and their natural enemies are considered as preys and predators, respectively [7], [11], [12], [13]. According to [15], dynamic optimization and feedback control allow the determining of biological control policies based on the mathematical models.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, for higher versatility, it is beneficial to extend the concept of finite-time convergence to the biological pest control so that the policy becomes eligible for usage with multiple pest species using multiple control inputs. Apparently, difficulties in applying the nonlinear controller design for population control represented by the Lotka-Volterra equations depend on control objectives and the appearance of the control inputs [7], [15], [18], [24], [25]. Moreover, designing the controllers for the n-dimensional Lotka-Volterra system, which contains p preys and p predators with p control inputs, is complex since it is a nonlinear multiple-input and multiple-output (MIMO) problem.…”

Section: Introductionmentioning

confidence: 99%

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Fixed-Time Synergetic Approach for Biological Pest Control Based on Lotka-Volterra Model

et al. 2021

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Biological pest control has a strong advantage in its non-chemical effects on the environment. In this study, a fixed-time synergetic control scheme for the biological pest control problems represented by the n-dimensional Lotka-Volterra model was proposed. The proof of stability shows that the proposed controller can regulate the biological pest control systems with the characteristic of fixed-time convergence. The performance of the proposed scheme was demonstrated through simulation studies. The simulation results show that the pre-specified bound of the settling time can be satisfied regardless of the initial conditions, confirming a desired fixed-time convergence characteristic. Moreover, unlike the existing control policy based on the sliding mode control, the control inputs of the proposed policy are free from chattering.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fixed-Time Synergetic Approach for Biological Pest Control Based on Lotka-Volterra Model

et al. 2021

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“…Using Lotka-Volterra models brings about significant benefits for the attempts to further conduct the dynamic analysis and to determine control policies for regulating or maintaining pest population to be within the economic injury level (EIL) [4,6,[7][8][9][10][11][12][13][14][15][16][17]. Typically, determination of the biological control policy based on mathematical models can be carried out by using optimal control according to Pontryagin's maximum principle [6,[13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Rafikov et al [13][14] employed the Pontryagin's maximum principle to determine the biological control policy for the ecosystem represented by the n-dimensional Lotka-Volterra model. Alternatively, the nonlinear feedback controller design can be utilized for defining the control policies for predator and prey systems [4,[15][16][17][18]. In [18], Meza et al studied various nonlinear feedback control designs for the one-predator one-prey Lotka-Volterra system.…”

Section: Introductionmentioning

confidence: 99%

“…Peubla et al [15] applied the sliding mode control to define the control policy for a general pest control system with the case study of a one-pest and one-predator model. Recently, Peubla et al [16] employed the modelling error compensation (MEC) method for the biological pest control for a class of the biological control system. Boonyaprapasorn et al [17] determined the biological pest control based on the synergetic controller design.…”

Section: Introductionmentioning

confidence: 99%

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Biological Pest Control Based on Tensor Product Transformation Method

Boonyaprapasorn¹,

Kuntanapreeda²,

Sangpet³

et al. 2020

ACTA POLYTECH HUNG

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Pest management, based on biological control, has drawn attention from several research groups, due to the exclusion of chemical pesticides, which have debilitating outcomes, both on the environment and human health. Biological pest control policies have been determined using the model-based control approach. In this study, the tensor product model transformation (TPMT) was applied to model the nonlinear dynamic of the biological pest control system. Consequently, the feedback control law representing the biological pest control policy was synthesized based on LMI. Under the designed controller, the pest population was regulated based on the desired level. The simulation of the biological pest control system was presented to confirm the performance of the designed control law. It is evident, that the feedback control method based on TPMT can be employed appropriately, in this application.

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Tensor product model transformation‐based control for fractional‐order biological pest control systems

Boonyaprapasorn

Kuntanapreeda

Ngiamsunthorn

et al. 2020

Asian Journal of Control

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As environmental pollution and safety of human and creatures have been an issue of concern, pest management based on nonchemical use is preferable. Biological pest control can satisfy the regulation of pest population without causing environmental problems. Moreover, representation of dynamical systems in the form of fractional‐order dynamic models can explain several phenomena better than the integer‐order dynamic models can. The aim of this study is to determine the biological pest control policy for the fractional‐order pest control systems, which is a complex nonlinear multiple input and multiple output control problem. Here, the system is represented in the form of a fractional‐order Lotka–Volterra model. In order to avoid complexity in the nonlinear feedback controller design process, the tensor product (TP) model transformation was applied to automatically transform the biological pest control system to the TP polytopic model which allows the designer to synthesize the controller under linear framework. Moreover, the design process can be easily carried out by solving an linear matrix inequality (LMI) problem, which is very well applicable to high‐dimensional multiple input and multiple output control problems. Simulation studies were conducted to demonstrate the ease of the TP‐based design process. The simulation results showed that the designed control policy effectively manipulates the pest populations of the fractional‐order Lotka–Volterra model to the desired level which is economically viable.

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Biological pest control using a model‐based robust feedback

Cited by 18 publications

References 40 publications

Fixed-Time Synergetic Approach for Biological Pest Control Based on Lotka-Volterra Model

Fixed-Time Synergetic Approach for Biological Pest Control Based on Lotka-Volterra Model

Biological Pest Control Based on Tensor Product Transformation Method

Tensor product model transformation‐based control for fractional‐order biological pest control systems

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