The design of a Fractional‐Order fuzzy‐based PID (FO‐F‐PID) control structure is presented for Buck converter in the presence of harmful disturbances. A fractional‐order proportional‐integral‐derivative (FO‐PID) control scheme is utilized initially to damp the oscillations and remove the steady‐state error. To increase the tendency rate of the error to zero, the FO‐PID method is applied to a fuzzy‐logic‐based compensatory stage. At the same time, the fuzzy part gathers the data based on the error and error derivative. The FO‐PID control scheme has the capability to enhance the robustness of the control technique against disturbances and parametric variations. Furthermore, to optimize the control parameters, an efficient algorithm so‐called Antlion Optimization (ALO) algorithm, is used. Utilizing the ALO algorithm for tuning the FO‐PID gains depicts more accurate responses in solving constrained problems with diverse search spaces. Considering numerous disturbances on DC‐DC converters, an FO‐F‐PID controller can be an appropriate alternative since it is more robust against load variations and noise. Moreover, PSO‐PID and FO‐PSO‐PID controllers are designed to drive a comparison between them. Finally, the merits of the presented controller are validated for various scenarios. It can be seen that the FO‐F‐ALO‐PID method provides much better results with faster dynamics. Matlab‐Simulink environment is used for the simulations, and the experimental results are tested by the micro‐processor to validate the superiority of the proposed method.

The DC/DC Boost converter exhibits a non-minimum phase system with a right half-plane zero structure, posing significant challenges for the design of effective control approaches. This article presents the design of a robust Proportional-Integral (PI) controller for this converter with an online adaptive mechanism based on the Reinforcement-Learning (RL) strategy. Classical PI controllers are simple and easy to build, but they need to be more robust against a wide range of disturbances and more adaptable to operational parameters. To address these issues, the RL adaptive strategy is used to optimize the performance of the PI controller. Some of the main advantages of the RL are lower sensitivity to error, more reliable results through the collection of data from the environment, an ideal model behavior within a specific context, and better frequency matching in real-time applications. Random exploration, nevertheless, can result in disastrous outcomes and surprising performance in real-world settings. Therefore, we opt for the Deterministic Policy Gradient (DPG) technique, which employs a deterministic action function as opposed to a stochastic one. DPG combines the benefits of actor-critics, deep Q-networks, and the deterministic policy gradient method. In addition, this method adopts the Snake Optimization (SO) algorithm to optimize the initial condition of gains, yielding more reliable results with faster dynamics. The SO method is known for its disciplined and nature-inspired approach, which results in faster decision-making and greater accuracy compared to other optimization algorithms. A structure using a hardware setup with CONTROLLINO MAXI Automation is built, which offers a more cost-effective and precise measurement method. Finally, the results achieved by simulations and experiments demonstrate the robustness of this approach.

Adaptive neuro‐fuzzy inference system (ANFIS) technique is a significant alternative of research which is structured with a combination of two soft‐computing strategies of fuzzy logic and artificial neural network. The design of ANFIS controller for a single‐phase full‐bridge inverter with pulse width modulation is demonstrated here in the presence of different disturbances. Moreover, an LC filter is designed to decrease the disturbing harmonics which the stability of the filter can be noted as an important issue. Based on the fuzzy C‐mean clustering method used for decreasing fuzzy rules, the computational burden has been improved resulting in faster dynamic performance. This method considers the system as a black‐box structure which omits the need for an exact model of system and can be an appropriate technique for ill‐defined systems. Additionally, to deal with the variations of supply DC voltage, a fractional‐order proportional‐integral‐derivative controller is designed which is tuned by particle swarm optimiser algorithm and can generate a sinusoidal reference for the system input. This double‐loop control technique is known as cascade control strategy. It can be seen that ANFIS scheme provides appropriate results with less computational burden and simple structure with optimised responses in challenging conditions. The capability of the proposed method is validated for different operating conditions through simulation and experimental results.

An adaptive backstepping method is presented by this paper for a DC–DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black‐box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least‐squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm‐based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

The design of a cascade controller is demonstrated for a buck–boost converter that is combined with two control loops consisting of inner and outer controllers. The outer loop is implemented by a fractional‐order proportional‐integrated‐derivative (FO‐PID) controller that works as a voltage controller and generates a reference current for the inner control loop. To provide faster dynamic performance for inner loop, a self‐tuning regulator adaptive controller, which tries to regulates the current with the help of a novel improved exponential regressive least square identification in an online technique, is designed. Moreover, in the outer loop, to tune the gains of the FO‐PID controller, a novel algorithm of antlion optimizer algorithm is used that offers many benefits in comparison with other algorithms. The system provided by the boost mode is a non‐minimum phase system, which creates challenges for designing a stable controller. In addition, a single loop controller is proposed based on a PID controller tuned by a particle swarm optimization algorithm to be compared with the cascade controller. Cascade loop can present significant benefits to the controller such as better disturbance rejection. Finally, the strength of the presented cascade control scheme is verified in different performing situations by real‐time experiments.