Development of neural fractional order PID controller with emulator

Pirasteh-Moghadam, Mostafa; Saryazdi, Maryam Gh.; Loghman, Ehsan; Kamali, E Ali; Bakhtiari-Nejad, Firooz

doi:10.1016/j.isatra.2020.06.014

Cited by 31 publications

(15 citation statements)

References 33 publications

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“…It can be seen from Figs 16. and 17 and from Table I that for IMC-PID-MLESO, the Max|e|, IAE, and TV are the smallest under the external step disturbance and that the IAE is 75.7% and 62.9% lower than those of IMC-PID and LADRC, respectively.…”

mentioning

confidence: 89%

“…In the past, the combination of PID and advanced control methods and PID parameter optimization are always the research hotspots in the field of control engineering. On the one hand, some nonlinear PID control strategies, such as fractional PID [16], [17], neural network PID [18], [19], neural fuzzy PID [20], [21], and particle swarm optimization PID [22], [23] have been proposed. On the other hand, it is particularly important to adjust the PID controller parameters appropriately, using methods such as the well-known Ziegler -Nichols (ZN) tuning rule [24], H 2 /H ∞ robust optimization design [25], internal model control (IMC) principle [26], and other parameter adjustment methods [27], [28].…”

Section: Introductionmentioning

confidence: 99%

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Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

et al. 2021

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Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.INDEX TERMS Disturbance suppression, internal model control, permanent magnet linear synchronous motor, tracking accuracy, uncertainties.

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confidence: 89%

Section: Introductionmentioning

confidence: 99%

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

et al. 2021

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“…Many toolbars like CRONE [16], Ninteger [17], and FOMCON [18] have been used to deal fractional-order controller. Further many research articles are being done to develop different tuning methods of FOPID controller [19,20], particularly different optimization algorithms are presented such as genetic algorithm [21], PSO [15], NN [22], gravitational search algorithm [23], artificial bee colony [24], sine cosine algorithm [25], metaheuristic bat algorithm [26], and so on. Despite many naturally inspired optimization techniques, PSO has maintained a special place among research groups due to its efficiency, straightforward method, a limited number of parameters, and a great deal of flexibility in modifying the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

Ramakrishnan

Nachimuthu

2021

J. Inst. Eng. India Ser. C

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Several control strategies are proposed and developed to enhance the performance of the various underactuated systems, particularly inverted pendulum. This paper presents the dual-mode fractional-order control with a reference model for pitch and angle control of an inverted pendulum. An inertia weighted PSO is utilized for optimal tuning of the FOPID parameters to ensure an optimal balance between local and global search. A cost function of this algorithm is framed based on the error between the reference model output and actual system output along with time-domain performance criteria. The reference model-based tuning improves the performance of the controller and steady-state error tracking. In addition, an optimal state feedback LQR is implemented using pole placement design in the feedback to stabilize and improve the robustness of the inverted pendulum. Compared to the conventional PID, the proposed structure illustrates the FOPID structure has significant performance improvement in both with and without disturbance conditions.

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“…To the authors' knowledge, for the engineering problems, the applications of fractional-order calculus fall into two main categories. On the one hand, the closed-loop characteristics of the system are affected or changed by introducing fractional-order calculus into the control system, so as to improve the control effect and robustness of the system [12,13]. The fractional-order derivative, on the other hand, is often used to simulate the constitutive relations of engineering materials with memory properties.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Characteristics of a Fractional-Order Spur Gear Pair With Internal and External Excitations

Hou

Yang

et al. 2021

Journal of Computational and Nonlinear Dynamics

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The nonlinear frequency response characteristics of a spur gear pair with fractional-order derivative under combined internal and external excitations are investigated based on the incremental harmonic balance (IHB) method. First, a pure torsional vibration model is proposed that contains various complex factors, such as the time-varying mesh stiffness, transmission error, the fluctuation of input torque, backlash. Then, the IHB method is developed to calculate the higher-order approximate solution of the system, and the correctness of the results is verified by comparing with numerical simulation results obtained by the power series expansion (PSE) method. Furthermore, the types of various impact situations and their judgment conditions are discussed, and the different impact behaviors are analyzed in detail when ω∈[0, 1.5] by using phase diagrams and amplitude–frequency response curves. The influence of important parameters on the dynamic characteristics of gear pair is analyzed at last. The results indicate that the analytical solution derived by IHB method is sufficiently precise. Significantly, the dynamic characteristics of the system could be effectively controlled by adjusting time-varying mesh stiffness coefficient, the order and coefficient of fractional-order term, and the amplitude of internal excitation or external excitation. As a part of the theory of fractional-order mechanical system, the impact performance of fractional-order gear pair is approached for the first time by analytical method.

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Development of neural fractional order PID controller with emulator

Cited by 31 publications

References 33 publications

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

Design of State Feedback LQR Based Dual Mode Fractional-Order PID Controller using Inertia Weighted PSO Algorithm: For Control of an Underactuated System

Analysis of Dynamic Characteristics of a Fractional-Order Spur Gear Pair With Internal and External Excitations

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