A Fluid Catalytic Cracking Unit (FCCU) is always considered to be a benchmark problem in the petroleum refinery industry. The process is interactive, with each process depending on the other. In this study, the control of the reactor and regenerator temperature in the FCCU involves the utilization of Adaptive Traditional Fuzzy (ATF) logic controllers and Adaptive Mixed Fuzzy (AMF) logic controllers. The purpose of designing the AMF controller is to offer solutions for minimizing the coupling effect resulting from the interaction of the FCCU. The development of this controller involves a combination of ATF and Coupling Fuzzy (CF) controller elements across different degrees of freedom. To optimize the scale factors for the ATF and CF control schemes, a Real-coded Genetic Algorithm (RGA) is employed. The results show that the RGA-tuned AMF controller tracks the setpoint better than the ATF controller, with less interaction effect.

The use of high voltage direct current (HVDC) transmission lines in power systems not only increase the capacity of electrical power transmission systems, but also strengthen the stability of the power network. In order to optimize the HVDC influences on voltage-frequency stability, it is necessary to design supplementary controllers in the most optimal path between input-output signals of the whole power system. The supplementary controllers are added to the local control loop of HVDC to improve active-reactive power flow. In this paper, an optimized method based on the controllability concept is proposed for the coupling of the input-output (IO) signals of the power system equipped with voltage source converter (VSC)-based HVDC. Then, the optimal path is used for supplementary damping controller design based on a novel adaptive recurrent neural network (ARNN). The ARNN is trained online Using a new training algorithm. The simulation results, which are carried on using MATLAB software, show the effectiveness of the control strategy to improve the voltage profile and dynamic stability of the power system.

In this paper, a Lyapunov-based adaptive 2nd-order sliding mode controller is proposed to control the current in an active power filter (APF). The penetration of APFs has been exponentially increased because of their high flexibility and fewer resonance problems. Moreover, they can compensate high range of current harmonics and reactive power. The voltage and current control loops have always been interesting areas for researchers since the satisfactory performance of the APF is highly dependent on these control loops. A sliding mode controller (SMC) is a mighty controller when uncertain conditions are considered. However, in order to reduce the chattering- high-frequency switching- and improve the steady state operation, stability, and robustness of the controller, it is usually decided to adaptively tune the gains of the controller. In this paper, a simple-structure adaptive SMC (ASMC) is proposed which can be implemented easily. This ASMC is shown to be stable using the Lyapunov theorem and proved with SIMULINK simulation that it has less steady state error, less chattering, and faster dynamic response compared to the conventional SMC.

The purpose of designing the active suspension systems is providing comfort riding and good handling in different road disturbances. In this paper, a novel control method based on adaptive neuro fuzzy system in active suspension system is proposed. Choosing the proper database to train the ANFIS has an important role in increasing the suspension system’s performance. The database used to train the proposed ANFIS system is extracted from the outputs of fuzzy, LQR and sliding mode controllers. A quarter-car model is considered to study the performance of the proposed controller. Performance of this controller is compared with the passive system, and active suspension systems with fuzzy and LQR controllers. The results demonstrate that proposed ANFIS controller is better than passive suspension system and active fuzzy and LQR based suspension systems in suspension deflection, body acceleration, settling time and also control force.

The control of Antilock Braking Systems (ABS) is a difficult problem, because of their nonlinearities and uncertainties appearing in their dynamics and parameters. To overcome these issues, this paper proposes a new adaptive controller for the next generation of ABS. After considering a complex vehicle dynamic, a triple adaptive fuzzy control system is presented. Important parameters of the vehicle dynamic include two separated brake torques for front ands rear wheels, as well as longitudinal weight transfer which is caused by the acceleration or deceleration. Because of the nonlinearity of the vehicle dynamic model, three fuzzy-estimators have been suggested to eliminate nonlinear terms of the front and rear wheels’ dynamic. Since the vehicle model parameters change due to variations of road conditions, an adaptive law of the controller is derived based on Lyapunov theory to adapt the fuzzyestimators and stabilize the entire system. The performance of the proposed controller is evaluated by some simulations on the ABS system. The results demonstrate the effectiveness of the proposed method for ABS under different road conditions.