Comparison of control strategies for a 2 DOF helicopter

Sharma, Roshan; Pfeiffer, Carlos F.

doi:10.3384/ecp17138271

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The effect of V mp on # is very strong denoted by strong cross-coupling, while the effect of V my on / is weak denoted by weak cross-coupling. The 2-DOF helicopter model is described by the following nonlinear equations [48,57]:…”

Section: -Dof Helicopter Systemmentioning

confidence: 99%

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Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

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Section: -Dof Helicopter Systemmentioning

confidence: 99%

“…The parameters of the 2-DOF helicopter system with the description is listed in Table 1 [57]. Figure 4 shows the block diagram of a 2-DOF helicopter system controlled by the proposed FOPID-FOAC controller.…”

Section: -Dof Helicopter Systemmentioning

confidence: 99%

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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“…In recent years, two true multivariable systems have become popular to use for teaching and research in academia: a helicopter prototype with two degrees of freedom (Neto, 2016), and a quadruple tank system for level control (Johansson, 2000;Pfeiffer, 2011). Both systems present different challenges: the helicopter is an open loop unstable system, highly nonlinear with strongly coupled input-output variables, and it requires very fast sampling times.…”

Section: Experimental Systems In Academiamentioning

confidence: 99%

“…The system outputs are the tanks levels given by ℎ 1 ,ℎ 2 ,ℎ 3 and ℎ 4 The system diagram is given in Figure 4. The model for the four tanks system, assuming the pumps dynamics is much faster than the tanks dynamics, is included in Figure 5 (Pfeiffer, 2011), with the corresponding parameters description provided in Table 2. …”

Section: Experimental System Modelsmentioning

confidence: 99%

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Covering the Gap Between Advanced Control Theory Design and Real Time Implementation Using Simulink

Pfeiffer¹,

Sharma²

2017

Linköping Electronic Conference Proceedings

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New developments in sensor technology and control actuators make it viable to monitor and regulate more process variables, providing an opportunity to apply advanced multivariable control techniques. Although modern control techniques allows the implementation of true multiple inputs -multiple output controllers, there has been a big gap between theoretical developments and real life applications. At the University of Southeast Norway, we proposed a bachelor level course aimed to students who have had a previous introductory course to classic control, to teach them the fundamentals of modern multivariable control techniques, including state feedback, LQR and linear MPC. The use of Simulink is integrated with the course, to analyze and design modern controllers for two real multivariable experimental processes. We advocate the use of advanced simulation and data acquisition tools to help to cover the existing gap between the development of modern control algorithms, and their implementation with real processes. The experimental testing and final tuning of the controllers are an important part of the course.

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Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method

Qin

Xin

Sun

2019

Asian Journal of Control

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This paper presents a study of multi‐objective optimal design of nonlinear control systems and has validated the control design with a twin rotor model helicopter. The gains of the porportional integral differential (PID) control are designed in the framework of multi‐objective opitmization. Eight design paramaters are optimized to minimize six time‐domain objective objective functions. The study of multi‐objective optimal design of feedback control with such a number of design paramaters and objective functions is rare in the literature. The Pareto optimal solutions are obtained by the proposed parallel simple cell mapping method consisting of a robust Pareto set recovery algorithm and a rolling subdivision technique. The proposed parallel simple cell mapping algorithm has two features: the number of cells in the invariant set grows linearly with the rolling subdivisions, and the Pareto set is insensitive to the inital set of seed cells. The current control design is compared with the classical LQE control for linear systems, and is also experimentally validated. The current design provides improved control performance as compares with the LQR control, and is applicable to complex nonlinear systems.

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Comparison of control strategies for a 2 DOF helicopter

Cited by 7 publications

References 10 publications

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Covering the Gap Between Advanced Control Theory Design and Real Time Implementation Using Simulink

Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method

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