Time‐optimal control of DC‐DC buck converter using single‐input fuzzy augmented fractional‐order PI controller

Abstract

Summary This paper presents a robust and optimal output‐voltage tracking controller for a DC‐DC buck converter. A fractional‐order proportional‐integral (FPI) controller is primarily used to eliminate the steady‐state error and damp the oscillations. In order to improve the error convergence rate of the response, the FPI controller is augmented with a single‐input fuzzy‐logic based precompensator stage. The fuzzy precompensator aggregates the information regarding the error and error derivative using the signed‐distance method. The derived aggregated signal is used to infer logical control decisions based on a predefined one‐dimensional rule base. The simplification yields shorter computation time and a piecewise linear control surface that improves the system's immunity against exogenous disturbances and unprecedented nonlinearities. The error derivative is indirectly computed by measuring the capacitor current in real time. This modification further enhances the computational efficiency, offers minimum‐time transient recovery, and compensates the hysteresis effect rendered by parasitic impedances. The controller gains are optimally selected using the particle swarm optimization algorithm. Credible hardware‐in‐the‐loop experiments are conducted to validate the time optimality and robustness of the proposed controller. The experimental results clearly demonstrate the significant improvement rendered by the proposed controller in the system's reference tracking performance, disturbance‐rejection capability, and transient recovery time.

Self‐adaptive fractional‐order LQ‐PID voltage controller for robust disturbance compensation in DC‐DC buck converters

“…where b 1 , b 2 are the cognitive‐coefficients, r 1 , r 2 are random real numbers between 0 and 1, and w is the inertia weight. The cognitive coefficients are selected such that their sum is slightly greater than 4 . After initialization, the algorithm iteratively computes and records the fitness of the particles using the quadratic cost function given in Equation 37. …”

“…Reasonable value of N and j max are empirically selected to prevent excessive computational burden on embedded processor. The parameters associated with the optimizer are clearly identified in Table . The controller parameters are optimized offline by applying a step input.…”

“…The cognitive coefficients are selected such that their sum is slightly greater than 4. 44 After initialization, the algorithm iteratively computes and records the fitness of the particles using the quadratic cost function given in Equation 37.…”

Experimental Validation of Voltage Regulation in Buck Converters through Fractional-Order PID Approximation

“…Robustness in presences of uncertainties, unmodeled dynamics and non-linear loads, steady-state error reduction and perturbation rejection were the improvements. In (Saleem et al, 2019) and (Farsizadeh et al, 2019) fractional-order PI/PID controllers in combination with fuzzy-logic approach were suggested. The control strategy achieved voltage regulation via capacitor current to improve tracking performance, disturbance rejection and stability.…”

Fractional-Order Approximation and Synthesis of a PID Controller for a Buck Converter

“…The fractional-order controller improved the steady state performance. In [19], a fractional-order PI controller is combined with fuzzy-logic precompensators to control a Buck converter via capacitor current. The combination improved the system disturbance-rejection capability and reference tracking performance.…”