Muhanad Deyaa Hashim Mahmmud Almawlawe

The present study explores the guidance of a robotic arm along a predefined path by implementing an optimal fuzzy fractional order PID controller-based control strategy. This method serves as a means to address the nonlinearity and unpredictability of the robotic manipulator, contingent upon the fuzzy logic controller's specifications and the employment of a clonal selection algorithm. The dynamic equation of the manipulator was considered as an initial point, followed by designing a fuzzy controller for this purpose. To validate the effectiveness of this approach, it was compared to other techniques, such as Fuzzy, Fuzzy-PID, and fuzzy-FOPID controllers, with PID and FOPID controller parameters optimized using clonal selection algorithms. Simulation results reveal that the fuzzy-FOPID variant outperformed other methods under varying load conditions and model uncertainties, using SIMULINK/MATLAB 2014a.

Controlling a system is a complicated job, especially when we talk about the nonlinearity of the system introduced by the external changes. This paper presents the procedure of designing, analysis, and verification of nonlinear autoregressive moving average controller (NARMA L2) as an artificial intelligence technique to track the output voltage of a Buck dc/dc converter in comparison with PID controller, digitalized sliding mode controller so as to reduce the ripples in output voltage and to suppress the transient overshoots, or in other words, enhance the transient response diversity of the plant in the case of load and line changes. In this technique, a back-propagation learning algorithm is derived to increase the effectiveness of the proposed controller. Finally, the proposed method of control using a neural network controller is designed using MATLAB/SIMULINK and the results of the converter for the Neuro …

Converter is electric circuit used to control transfer of electric power between electric source and consumer [19]. Increasing of number and complexity of electric devices caused evolvement of wide range of different converters. Level of possible currents varies between few 𝑚𝐴 and several hundreds of 𝐴. Converters differ by working principle, construction, energetic efficiency, dimensions, accuracy of regulation, transient state response and price. Very often, converters have to perform additional function of protecting load and system in the case of a component failure

This paper presents a design and simulation of simplified method for designing a proportional–integral–derivative (PID) controller operating in continuous conduction mode for the Buck-Boost converter, this method provides good voltage regulation and is suitable for Buck-Boost Dc-Dc converter, it is exposed to significant variations which may take this system away from nominal conditions caused by the line change and parameters variation at the input. Simulation results shows that this PID controller provides good voltage regulation and is suitable for the Buck-Boost purposes. The obtained results prove the robustness of proposed Controller against variation of the input voltage, load resistance and the referent voltage of the studied converter.

This paper presents a comparative analysis of two techniques for controlling DC-DC buck converters: the analogue technique that is based on the proportional-integral-derivative (PID) sliding mode (SM) voltage control, and the digital one using quasi-sliding mode based generalized minimum variance (QSMGMV) control. Both converters provide good voltage regulation and exhibiting robustness to parameter and load variations. The voltage regulation in the converter with QSMGMV controller is achieved by measuring only the sensed output voltage, whereas with PID SM controller, the use of current sensor is mandatory. The former convertor has also smaller output voltage ripple in comparison with the latter one. On the other hand, the acceptable performance in the converter with PID SM control is reached by tuning only three controller parameters in familiar way.

This paper presents a new method for designing adaptive neuro-fuzzy inference systems (ANFIS). Improvements are made by introducing specially developed orthogonal functions into the very structure of ANFIS, specifically, into the layer that imitates Sugeno stile defuzzification. These functions are specially tailored for analysis and synthesis of dynamic systems and they also contain an adaptive measure of the variability of the systems operating in a real environment, which can be implemented inside the ANFIS as hormonal effect.

The paper deals with design of DC-DC buckboost converter controlled by the combination of digital sliding mode control and generalized minimum variance control. This approach can also be implemented for different converter topologies operating in the continuous conduction mode. The converter switch is driven by PWM signal, generated by controller using only sensed output voltage. Simulation results shows that the proposed digital control algorithm provides a small voltage ripple in steady-state and good dynamic performances under different operation conditions, which include intensive load and parameter variations.

The paper presents the combination of generalized minimum variance control and discrete time quasi-sliding control applied to control a DC-DC boost converter that provides stable output voltage. The control algorithm is realized by measuring only sensed output voltage and comparing it with the reference voltage in order to achieve zero error signal. The shortcomings of generalized minimum variance control are significantly alleviate, while the implementation of quasi-sliding control based on input/output plant model results in high output voltage accuracy in the presence of parameters perturbations. The proposed control concept is verified by digital simulation.

The paper considers a realization of quasi-sliding mode control for DC–DC boost-type converter on ATmega8 microcontroller. The proposed control law represents a combination of discrete-time sliding mode and generalized minimum variance control techniques. The control design is based on input–output converter model and only the sensed output voltage is used for generating control signal. This approach simplifies the practical realization of boost converter since there is no need for current sensors. By introducing an additional discrete-time integrator in control, the converter accuracy in steady-state under load and input voltage variations is enhanced. The experimental prototype is developed and several experiments are conducted to validate the functionalities of the proposed solution. The maximum load and line regulation errors of the proposed converter are 1.55% and 2.9%, respectively.