Design of optimal fractional order fuzzy PID controller based on cuckoo search algorithm for core power control in molten salt reactors

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.

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

In this study, enhanced equilibrium optimization (E2O) algorithm was proposed by developing stochastic processes in equilibrium optimization (EO) method with fractional order chaotic (FOC) system models FOC model was firstly used in an optimization algorithm in this study System responses of the fractional order chaotic models were used instead of random coefficients in the basic EO method The performance of many fractional order chaotic system models was tested on benchmark functions It was experimentally determined fractional order chaotic models of Genesio Tesi, Chua Memristor and cellular neural network which were convenient for the EO method Model coefficients and initial conditions of corresponding fractional order chaotic models were obtained for benchmark functions to find suitable models for E2O algorithm In order to present engineering application performance of the proposed E2O method, controller parameters were optimized for liquid level control that was decoupled two-input and two-output (TITO) tank system Fractional and integer order PI and PID controllers’ parameters were tuned according to the reference input signals for TITO tank system Multi-objective function was defined with mean square error (MSE) definition as system’s overall objective function Proposed multi-objective function was minimized during to optimization process E2O algorithm results were compared with each other and existing literature studies results In this way, it was shown comparatively that usage of fractional order chaotic models in the proposed E2O algorithm affected optimization algorithm performance and produced better results

Lezhi Chen

Papers

An IMC-PID controller with Particle Swarm Optimization algorithm for MSBR core power control