Control Law Design for Twin Rotor MIMO System with Nonlinear Control Strategy

Ilyas, Muhammad; Abbas, Nizar Hadi; UbaidUllah, M.; Imtiaz, Waqas A.; Shah, Munam Ali; Kazemzad, Mahmood

doi:10.1155/2016/2952738

Cited by 23 publications

(9 citation statements)

References 24 publications

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“…However, applying Lyapunov functions does not always give good performance and accuracy, and the use of non-linear controllers may lead to adverse effects such as chattering, oscillations, and noises. Ilyas et al [57] designed first-order SMC and BC schemes to deal with the oscillations and chattering in pitch and yaw angles where BC shows better results in handling them compared to SMC. Concerning the strong cross-coupling effect and non-linearity between the main and tail rotors, Raghavan and Thomas [58] presented a model predictive control (MPC) design, via systematical and experimental means.…”

Section: Twin-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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This paper presents a review of the various control strategies that have been conducted to address and resolve several challenges for a particular category of unmanned aerial vehicles (UAVs), the emphasis of which is on the rotorcraft or rotary-wing systems. Initially, a brief overview of the important relevant definitions, configurations, components, advantages/disadvantages, and applications of the UAVs is first introduced in general, encompassing a wide spectrum of the flying machines. Subsequently, the focus is more on the two most common and versatile rotorcraft UAVs, namely, the twin-rotor and quadrotor systems. Starting with a brief background on the dual-rotor helicopter and a quadcopter, the full detailed mathematical dynamic model of each system is derived based on the Euler-Lagrange and Newton-Euler methods, considering a number of assumptions and considerations. Then, a state-of-the-art review of the diverse control strategies for controlling the rotorcraft systems with conceivable solutions when the systems are subjected to the different impediments is demonstrated. To counter some of these limitations and adverse operating/loading conditions in the UAVs, several innovative control techniques are particularly highlighted, and their performance are duly analyzed, discussed, and compared. The applied control techniques are deemed to produce a useful contribution to their successful implementation in the wake of varied constraints and demanding environments that result in a degree of robustness and efficacy. Some of the off-the-shelf developments in the rotorcraft systems for research and commercial applications are also presented.

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Section: Twin-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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“…In order to evaluate the performance of the NBELM in estimating the matrices of a linear dynamic system, the linear model of a twin rotor MIMO system (TRMS) 34 in the form of (1) is considered in which A and B matrices are as follows: …”

Section: Examplementioning

confidence: 99%

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

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Recently, a computational model of Amygdala based on the brain emotional learning is presented by psychologists. This brain emotional learning model (BELM) that has a neuro-inspired architecture is utilized to train the weights which are in Amygdala and Orbitofrontal. In this paper, unknown parameters of dynamic systems are estimated by developing the normalized BELM (NBELM).To this end, after proving the stability of the model output, the sufficient condition for weights convergence is extracted while the sensitivity analysis is applied for this model. In order to evaluate the performance of NBELM, in the first example, the matrices of a twin rotor MIMO system are estimated and compared with the equation error method (EEM). In the second example, the nonlinear model of a servomotor is utilized as a case study. In the third example, the performance of the NBELM in experimental systems is validated using a reaction wheel with a DC motor. An important feature of the brain emotional system is its fast response, leading the NBELM to have a high speed performance in estimating the parameters of dynamic systems. A few number of adjustable parameters and low computing complexity also cause the NBELM to be an appropriate method for online estimation of the unknown parameters of dynamic systems. KEYWORDSAmygdala computing model, BEL model, dynamic system parameter estimation, online and stable estimation Parameters of a large number of industrial systems need to be monitored online for various reasons, ranging from adaptive controls 1 to fault detection systems. 2,3 As a consequence, many research studies have been devoted to introducing novel parameters identification algorithms able to insure the stability of the system. Additionally, the proposed techniques must converge to real values as fast as possible in order to be applicable to engineering systems while high accuracy in the converging process is an evitable need in any novel method. The low complexity of the new algorithm is another challenge, leading the system identification method to be implemented with less effort and become wider. For mentioned challenges to be addressed, various investigations create new frameworks in terms of problem formulation, methodology development, theoretical foundation, software tools, and hardware modules. 4Int J Adapt Control Signal Process. 2019;33:1047-1065.wileyonlinelibrary.com/journal/acs

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“…In a few decades ago, many researchers proposed the different types of control techniques to control the performance of TRMS on horizontal and vertical planes. For instance, the proposed control techniques that applied in TRMS system such as Proportional-Integral-Derivative (PID) controller [4][5][6][7][8][9], Linear Quadratic Regulator (LQR) [10], Sigmoid-PID (SPID) controller [11], Fractional-order PID (FOPID) controller [12][13][14][15][16][17], Fuzzy Logic controller [18], Sliding Mode Controller (SMC) [19][20][21], Fuzzy-PID (FPID) controller [22][23][24][25][26] and Model Predictive Controller (MPC) [27]. Nevertheless, researchers and scientific community put the greatest attention in the combinations of different type control technique based PID controller.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Sigmoid Proportional-Integral-Derivative (SPID) Controller for Twin Rotor MIMO System

Qian¹,

Ghazali²,

Ahmad³

et al. 2023

JESA

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This paper presents a data driven Sigmoid Proportional-Integral-Derivation (SPID) controller for a Twin Rotor Multiple-Input-Multiple-Output (MIMO) System (TRMS). A time-varying PID parameters based on sigmoid function is adopted to solve the low control accuracy of the conventional PID controller. In particular, the parameters of new version controller were vigorously changed based on its error signal of sigmoid function where its variability is limited in a predefined upper and lower bound. These SPID parameters are then optimized by using Adaptive Safe Experimentation Dynamics (ASED) method such that the control performance accuracy in terms of trajectory tracking error and control input energy are minimized. The simulations of step response analysis and stability analysis are conducted to evaluate the effectiveness of the proposed SPID controller compared to PID controller on TRMS system. Consequently, the results obtained from the simulations revealed that the SPID controller has successfully produced improvement in terms of objective function, 𝐽, total norm of error, 𝑒̅ 1 + 𝑒̅ 2 and total norm of output, 𝑢 ̅ ℎ + 𝑢 ̅ 𝑣 by reduced 6.84%, 6.38% and 4.25%, respectively compared to the PID controller. In addition, the results of Integral-Absolute-Error (IAE), Integral-Square-Error (ISE), Integral-Time-Absolute-Error (ITAE) and Integral-Square-Error (ITSE) are proven that the SPID controller is outperform on horizontal and vertical planes for TRMS system in comparison with PID controller.

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Control Law Design for Twin Rotor MIMO System with Nonlinear Control Strategy

Cited by 23 publications

References 24 publications

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Data Driven Sigmoid Proportional-Integral-Derivative (SPID) Controller for Twin Rotor MIMO System

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