Design of the LQR controller and observer with fuzzy logic GA and GA-PSO algorithm for triple an inverted pendulum and cart system

Molazadeh, Vahid Reza; Banazadeh, Afshin; Shafieenejad, Iman

doi:10.1109/icamechs.2014.6911560

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Step 3 Construct the weighting matrices Q(t) and R(t) by substituting z δ , v δ and maximal allowable adjustment of guidance signal σ δmax into Eqs. (28), (21) and (22).…”

Section: Lqr With Time-varying Weighting Matricesmentioning

confidence: 99%

“…A trade-off between penalties on the state and control inputs for optimization of the cost function was considered in [25], where specified closed-loop eigenvalues were obtained, but the computation normally needed more iterations. Genetic algorithm (GA) can be applied to find a global optimal solution [26][27][28], and the differential evolution algorithms inspired from GA are efficient evolution strategies for fast optimization technique [29][30][31]. However, the approaches in [26][27][28][29][30][31] have little improvement for HSV profile-following performances under different disturbances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hypersonic Vehicles Profile-Following Based on LQR Design Using Time-Varying Weighting Matrices

Chen¹,

Xiong²,

Liang³

et al. 2018

Advances in Some Hypersonic Vehicles Technologies

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In the process of applying linear quadratic regulator (LQR) to solve aerial vehicle reentry reference trajectory guidance, to obtain better profile-following performance, the parameters of the aerial vehicle system can be used to calculate weighting matrices according to the Bryson principle. However, the traditional method is not applicable to various disturbances in hypersonic vehicles (HSV) which have particular dynamic characteristics. By calculating the weighting matrices constructed based on Bryson principle using time-varying parameters, a novel time-varying LQR design method is proposed to deal with the various disturbances in HSV reentry profile-following. Different from the previous approaches, the current states of the flight system are employed to calculate the parameters in weighting matrices. Simulation results are given to demonstrate that using the proposed approach in this chapter, performance of HSV profile-following can be improved significantly, and stronger robustness against different disturbances can be obtained.

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“…Step 3 Construct the weighting matrices Q(t) and R(t) by substituting z δ , v δ and maximal allowable adjustment of guidance signal σ δmax into Eqs. (28), (21) and (22).…”

Section: Lqr With Time-varying Weighting Matricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hypersonic Vehicles Profile-Following Based on LQR Design Using Time-Varying Weighting Matrices

Chen¹,

Xiong²,

Liang³

et al. 2018

Advances in Some Hypersonic Vehicles Technologies

View full text Add to dashboard Cite

show abstract

“…The cart-inverted pendulum is a system that usually used for testing many control algorithms [5]. There are some control algorithms that can be used for stabilising a cart-inverted pendulum such as Linear Quadratic Regulator (LQR) [6], neural network [7], genetic algorithm [8], fuzzy control [9], and PID [10] which have been studied by many researchers. The studies about the stability of a cart-inverted pendulum is very useful for developing real world application systems, for instances: a segway, an earthquake resistant building design, a human walking, etc.…”

Section: Introductionmentioning

confidence: 99%

Stabilising a cart inverted pendulum with an augmented PID control scheme

et al. 2018

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A cart inverted pendulum is an under actuated system that highly unstable and nonlinear. Therefore, it makes a good problem example which attracts control engineers to validate the developed control algorithms. In this paper, an augmented PID control algorithm is proposed to stabilise a cart inverted pendulum at the desired state. The derivation of a mathematical model of the cart inverted pendulum using Lagrange's equation is discussed in detail. The system dynamics is illustrated to understand better the behaviour of the system. A simulation program has been developed to verify the performance of the proposed control algorithm. The system dynamic behaviours with respect to the variation of the controller parameters are analysed and discussed. Controllers parameters are expressed into two PID gain sets which associated with 2 dynamic states: the cart position (ϰ) and the pendulum angle (θ). It can be concluded from the simulation result that the proposed control algorithm can perform well where acceptable steady errors can be achieved. The best response from the cart inverted pendulum system has been obtained with the value of kPX 190, kDX 50, kIX 5, kPθ 140, kDθ 5, and kIθ 25.

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“…The results showed that proposed control provides faster convergence and accompanies less iterations. Molazadeh et al (2014) designed a LQR controller for control of triple inverted pendulum. The tuning of LQR gains was achieved using fuzzy logic, GA and GA-particle swarm optimisation (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

Yuvaraja¹,

Ramya²

2018

IJIMR

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This manuscript describes the hybrid learning algorithm for training the error optimisation in an MIMO nonlinear system. The automated controller is designed using lenient computation technique with a Levenberg-Marquardt training algorithm. The designed controller is interfaced to a microgrid which has renewable energy sources like solar, wind, fuel cell, or smart battery as input and the output power generated by these sources can be utilised for various grid and atomised applications. The erudition capability and designing methodology of adaptive networks and sturdiness of PID controllers are described. Finally, the study illustrates an offline mode comparison of PID-based ANFIS and neural controllers in terms of settling time, steady state error and overshoot.

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Design of the LQR controller and observer with fuzzy logic GA and GA-PSO algorithm for triple an inverted pendulum and cart system

Cited by 7 publications

References 17 publications

Hypersonic Vehicles Profile-Following Based on LQR Design Using Time-Varying Weighting Matrices

Hypersonic Vehicles Profile-Following Based on LQR Design Using Time-Varying Weighting Matrices

Stabilising a cart inverted pendulum with an augmented PID control scheme

Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

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