Deadbeat Control for Multivariable Discrete Time Systems with Time Varying Delays

Azar, Ahmad Taher; Serrano, Fernando E.

doi:10.1007/978-3-319-13132-0_6

Cited by 36 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is expressed as ITAE = R T 0 t e(t) j jdt, where T is the adjustment time of the system and e(t) is the difference between the output value of the control system and the target value. The other function is deadbeat response, 20 which is the time response with the smallest overshoot. It quickly reaches the allowable range of fluctuation of the steady-state response and continues to remain within the fluctuation range.…”

Section: The Reference Model Construction Methodsmentioning

confidence: 99%

A method to construct a reference model for model reference adaptive control

Zhu

Wang

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Model reference adaptive control takes the output of a reference model as the target and ensures that the actual output is consistent with the target. In this scenario, the reference model determines the performance of the control system. Constructing a reference model with specific performance is challenging, and most researchers construct the reference model by experience. As alternative frameworks, we propose two novel reference model construction methods, namely, zero-pole method and frequency response method. The zero-pole method establishes a nonlinear optimization model to ensure that the added zeros and poles are as far away from the imaginary axis as possible. In the case where this cannot be done, the frequency response method matches the system frequency response of the reference model and the given model to the extent possible using a nonlinear optimization model. Experimental results are presented to confirm that both methods can construct a reference model based on a given transfer function. Deviations in response between the reference models constructed using the two methods and the given model are compared, and the reasons for higher accuracy of the frequency response method are discussed.

show abstract

Section: The Reference Model Construction Methodsmentioning

confidence: 99%

A method to construct a reference model for model reference adaptive control

Zhu

Wang

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Some commonly known applications are oscillators [40,41], lasers [42,43], chemical reactions [44][45][46][47][48][49][50][51], biology [52][53][54][55][55][56][57][58][59][60][61], ecology [62,63], encryption [64,65], cryptosystems [66,67], mechanical systems [68][69][70][71][72], secure communications [73][74][75], robotics [76][77][78], cardiology [79,80], intelligent control [81,82], neural networks [83][84][85], finance [86,87], memristors [88,…”

Section: Introductionmentioning

confidence: 99%

A Novel 3-D Circulant Highly Chaotic System with Labyrinth Chaos

Vaidyanathan

2016

Advances in Chaos Theory and Intelligent Control

View full text Add to dashboard Cite

In this work, we describe a novel 3-D circulant highly chaotic system with labyrinth chaos. The novel chaotic system is a nine-term polynomial system with six sinusoidal nonlinearities. The phase portraits of the novel circulant chaotic system are illustrated and the dynamic properties of the novel circulant chaotic system are discussed. The novel circulant chaotic system has infinitely many equilibrium points and it exhibits labyrinth chaos. We show that all the equilibrium points of the novel circulant chaotic system are saddle-foci and hence they are unstable. The Lyapunov exponents of the novel circulant chaotic system are obtained as L 1 = 10.3755, L 2 = 0 and L 3 = −10.4113. Thus, the Maximal Lyapunov Exponent (MLE) of the novel chaotic system is obtained as L 1 = 10.3755, which is a large value. This shows that the novel 3-D circulant chaotic system is highly chaotic. Also, the Kaplan-Yorke dimension of the novel circulant highly chaotic system is obtained as D K Y = 2.9966. Since the Kaplan-Yorke dimension of the the novel circulant chaotic system has a large value and close to three, the novel circulant chaotic system with labyrinth chaos exhibits highly complex behaviour. Since the sum of the Lyapunov exponents is negative, the novel chaotic system is dissipative. Next, we derive new results for the global chaos control of the novel circulant highly chaotic system with unknown parameters using adaptive control method. We also derive new results for the global chaos synchronization of the identical novel circulant highly chaotic systems with unknown parameters using adaptive control method. The main control results are established using Lyapunov stability theory. MATLAB simulations are depicted to illustrate the phase portraits of the novel circulant highly chaotic system and also the adaptive control results derived in this work.

show abstract

“…The proposed controller was shown to be effective for under actuated systems due to chattering avoidance. A novel approach for the deadbeat control of multivariable discrete time systems with variable time delays was proposed (Azar and Serrano, 2015d). The benefits of sliding modes when applied to the field of fault tolerant control were presented (Mekki et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Modelling, stability analysis and computational aspects of nonlinear fuzzy PID controllers using Mamdani minimum inference

Arun

Mohan

2018

IJAAC

View full text Add to dashboard Cite

This paper presents two new mathematical models of the simplest fuzzy PID controller which employ two fuzzy sets (negative and positive) on each of the three input variables (error, change in error and double change in error) and four fuzzy sets (−2, −1, +1, +2) on the output variable (incremental control). L-type, Γ-type and Π-type membership functions are considered in fuzzification process of input and output variables. Controller modelling is done via algebraic product AND operation, maximum/bounded sum OR operation, Mamdani minimum inference method, and centre of sums (CoS) defuzzification. The new models obtained in this manner turn out to be nonlinear, and their properties are studied. Since digital controllers are implemented on the digital processors, the computational and memory requirements of the fuzzy controllers and conventional (non-fuzzy) controller are compared. Stability analysis of closed loop systems containing the fuzzy controller models is done using the small gain theorem.

show abstract

Deadbeat Control for Multivariable Discrete Time Systems with Time Varying Delays

Cited by 36 publications

References 22 publications

A method to construct a reference model for model reference adaptive control

A method to construct a reference model for model reference adaptive control

A Novel 3-D Circulant Highly Chaotic System with Labyrinth Chaos

Modelling, stability analysis and computational aspects of nonlinear fuzzy PID controllers using Mamdani minimum inference

Contact Info

Product

Resources

About