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DC motors are widely used in industrial application for its different advantage such us high efficiency, low costs and flexibilities. For controlling the speed of DC motor, conventional controller PI and PID were the most widely used controllers. But due to empirically selected parameters , , and limitation of convention PID controller to achieve ideal control effect for higher order systems, a Fractional order Proportional-IntegralDerivative PID (FOPID) based on optimization techniques was proposed in this paper. The aim of this paper is to study the tuning of a FOPID controller using intelligent soft computing techniques such as Differential Evolution (DE) and Particle Swarm Optimization (PSO) for designing fractional order PID controller. The parameters of FOPID controller are determined by minimizing the Integral Time Absolute Error (ITAE) between the output of reference model and the plant. The performance of DE and PSO were compared with several simulation experiments. The simulation results show that the DE-based FOPID controller tuning approach provides improved performance for the setpoint tracking, error minimization, and measurement noise attenuation.

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Abstract:In this paper, a Fractional Adaptive Fuzzy Logic Control (FAFLC) strategy based on active fractional sliding mode (FSM) theory is considered to synchronize chaotic fractional-order systems. Takagi-Sugeno fuzzy systems are used to estimate the plant dynamics represented by unknown fractional order functions. One of the main contributions in this work is to combine an adaptive fractional order PI λ control law with the fractional-order adaptive sliding mode controller in order to eliminate the chattering action in the control signal. Based on Lyapunov theory, the stability analysis of the proposed control strategy is performed for an acceptable synchronization error level. Numerical simulations illustrate the efficiency of the proposed fractional fuzzy adaptive control scheme through the synchronization of two different fractional order chaotic Duffing systems. We show that the introduction of the additional fractional adaptive PI λ control action is able to eliminate the chattering phenomena in the control signal.

In this paper, a low-order approximation (LOA) of fractional order PID (FOPID) for an automatic voltage regulator (AVR) based on the modified artificial bee colony (ABC) is proposed. The improved artificial bee colony (IABC) high-order approximation (HOA)-based fractional order PID (IABC/HOA-FOPID) controller, which is distinguished by a significant order approximation and by an integer order transfer function, requires the use of a large number of parameters. To improve the AVR system’s performance in terms of transient and frequency response analysis, the memory capacity of the IABC/HOA-FOPID controller was lowered so that it could fit better in the corrective loop. The new robust controller is named the improved artificial bee colony (IABC) low-order approximation (LOA)-based fractional order PID (IABC/LOA-FOPID). The performance of the proposed IABC/LOA-FOPID controller was compared not only to the original ABC algorithm-tuned PID controller, but also to other controllers tuned by state-of-the-art meta-heuristic algorithms such as the improved whale optimization algorithm (IWOA), particle swarm optimization (PSO), cuckoo search (CS), many optimizing liaisons (MOL), genetic algorithm (GA), local unimodal sampling (LUS), and the tree seed algorithm (TSA). Step response, root locus, frequency response, robustness test, and disturbance rejection abilities are all compared. The simulation results and comparisons with the proposed IABC/LOA-FOPID controller and other existing controllers clearly show that the proposed IABC/LOA-FOPID controller outperforms the optimal PID controllers found by other algorithms in all the aforementioned performance tests.