Plug-in direct particle swarm repetitive controller with a reduced dimensionality of a fitness landscape – a multi-swarm approach

Abstract

Abstract. The paper describes a modification to the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) for the sine-wave constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI). The original PDPSRC algorithm assumes that the particle swarm optimizer (PSO) takes into account a performance index defined over the whole reference signal period. Each particle stores all the samples of the control signal, e.g. α = 200 samples for a controller working at 10 kHz and the reference frequency equal to 50 Hz. Therefore, the fitness landscape (i.e. the performance index) is α-dimensional (αD), which makes optimization challenging. That solution can be categorized as the single-swarm one. It has been previously shown that the swarm controller does not suffer from long-term stability issues encountered in the classic iterative learning controllers (ILC). However, the convergence of the swarm has to be kept at a relatively low rate to enable successful exploitation in the αD search space, which in turn results in slow responsiveness of the PDPSRC. Here a multi-swarm approach is proposed in which we divide a dynamic optimization problem (DOP) among less dimensional swarms. The reference signal period is segmented into shorter intervals and the control signal is optimized in each interval independently by separate swarms. The effectiveness of the proposed approach is illustrated with the help of numerical experiments on the CACF VSI with an output LC filter operating under nonlinear loads.

Operation Efficiency Optimization for Permanent Magnet Synchronous Motor Based on Improved Particle Swarm Optimization

“…where  is the diversity constant, Ddir denotes the diversity threshold, xmax(i) and xmax(j) represent the maximum and minimum d-axis current values found in each iteration respectively, and Dthreold denotes a threshold that controls the range of particle swarm diversity [33].…”

A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

“…Therefore, the majority of the particles fall into, and cannot easily jump out of, local optimal points, called premature convergence and evolutionary stagnation. To overcome these problems, a method based on DDT [32] is used to change the velocity update of each particle. DDT is embedded into velocity updating when the velocity of a particle becomes too low or when its position stays unchanged in the middle and final generations.…”

On the similarity and challenges of multiresonant and iterative learning current controllers for grid converters and why the disturbance feedforward matters

“…It is worth noticing at this point that the IMP can be used explicitly as described above to determine the structure of the controller using a generating polynomial, as well as implicitly to introduce an internal model of the reference or disturbance signals in forms other than generating polynomials. Such implicit models of signals are constructed iteratively in the classic iterative learning controllers [8,9], as well as in the dynamic optimization based ones [10,11,12,13]. The former ones are to be compared here in computer simulations to the explicit multiresonant ones.…”

“…To facilitate the comparison presented two sections forward, it should be noted that introducing a delay to (10) or (11) does not change their ability to generate a sinusoidal signal. Also, a negative gain does not render the element impractical, as the overall phase is the key point here -not just a sign.…”