Integrating PSO Optimized LQR Controller with Virtual Sensor for Quadrotor Position Control

Anditio, Boby; Andrini, Angela Dian; Nazaruddin, Yul Y.

doi:10.1109/ccta.2018.8511323

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…However, the universal approximator has some potential drawbacks, which include overfitting, training data requirements, computational complexity, sensitivity to initial conditions and Limited domain of validity. One of the most widely used techniques for MIMO control is the Linear Quadratic Gaussian (LQG) control method [12][13]. This method uses a Kalman filter to estimate the system state and a linear quadratic regulator to design the feedback controller.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mimo Systems With Universal Controller

Mohammed A. Ali,

Vodichev

2023

ELTECS

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Abstract. The control of multi-input multi-output (MIMO) systems poses significant challenges due to the intricate dynamics and interactions between multiple input and output channels. This paper presents a simulation study on MIMO systems control using the universal control technique. The proposed method offers a versatile framework for regulating MIMO systems with varying dynamics, utilizing a single control algorithm. The underlying concept of the universal control law is to devise a set of principles or strategies that can be universally applied to any system, regardless of its specific characteristics or parameters. To achieve this, the control algorithm is designed based on the derivatives balance of the system's input-output dynamics, granting the controller adaptability to both single-input single-output (SISO) and MIMO systems. Numerical examples are provided to demonstrate the effectiveness of the proposed method, involving a MIMO system comprising four inputs in a two-coupled distillation column process and two inputs in a twin rotor setup. The challenges associated with distillation column control, such as nonlinearity, disturbances, and interaction between multiple inputs and outputs, are considered. Twin rotor systems, consisting of two interconnected rotors, pose unique control difficulties, primarily arising from the coupling between the two rotors and their nonlinear behavior. The simulation results show the capability of the proposed universal controller to achieve precise and robust control over the MIMO system. Keywords: MIMO, Control, Simulation, Distillation Column Process, Twin Rotor.

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Section: Introductionmentioning

confidence: 99%

Modeling of Mimo Systems With Universal Controller

Mohammed A. Ali,

Vodichev

2023

ELTECS

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“…This article has firstly proposed the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the conventional trial and error method. Moreover, the generality of the proposed approach is global, whereas the Jacobian linearized approach is local [49].…”

Section: Introductionmentioning

confidence: 99%

Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Chen

2021

IEEE Access

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Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess "simultaneously" additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.INDEX TERMS Almost disturbance decoupling, COVID-19 epidemic prevention , feedback linearized approach, linear quadratic regulator, particle swarm optimization, quadrotor.

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“…This article has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO to take the place of the conventional trial and error method. Moreover, the generality of the proposed approach is global, whereas the Jacobian linearized approach is local [46].…”

Section: Introductionmentioning

confidence: 99%

Global optimization control for nonlinear full‐car active suspension system with multi‐performances

Chen

2021

IET Control Theory & Appl

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In this article, based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization, the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multi-performances of highly nonlinear multi-input multi-output full-car uncertain system are simultaneously achieved without applying any nonlinear function approximator including neural network approach and fuzzy logic approach. The article proposes a new method to achieve the optimal control matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally determined via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This study has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO optimization algorithm to take the place of the conventional trial and error method and locally Jacobian linearized approach under the guarantee of globally exponential stability. The simulation results show how the full-car system can be controlled optimally and satisfactorily and confirm the superiority of the proposed method.

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Integrating PSO Optimized LQR Controller with Virtual Sensor for Quadrotor Position Control

Cited by 5 publications

References 7 publications

Modeling of Mimo Systems With Universal Controller

Modeling of Mimo Systems With Universal Controller

Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Global optimization control for nonlinear full‐car active suspension system with multi‐performances

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