The LLC converter, known for its soft-switching properties, is widely used in industry. To balance the best steady-state and dynamic performance and to facilitate controller design, the small-signal model is critical. Recently, based on the time domain trajectory under perturbation and the extended description function, Y. -H. Hsieh proposed an accurate modeling method of bang-bang charge-controlled LLC converter. However, it did not consider the impact on the model of adding ramp compensation, which is commonly introduced in industrial applications to improve the stability of charge control. In addition, the modeling required consistent control logic between positive and negative half-cycles, i.e., the symmetric charge control. When dealing with asymmetric charge control, such as current-integrating charge control, it becomes inaccurate. This paper explores the extension of this modeling approach to LLC converters adopting asymmetric charge control with the consideration of ramp compensation. The small signal model is subsequently established, and both simulation and experimental results validate its accuracy.

In practical applications, effectively regulating the amplitude of chaotic signals and maintaining the chaotic nature of the system are extremely critical to ensure system stability and prevent failures. However, traditional amplitude control methods usually change the bifurcation threshold or attractor geometry, impairing the integrity of chaos and increasing the risk of system instability, thus struggling to achieve effective control over complex chaotic signals. Given the rapid advancement in brain-inspired intelligence technology, it has become imperative to investigate new control techniques based on memristors to overcome the limitations of conventional approaches. To address these challenges, in this paper, a novel dual memristor-coupled Hopfield Neural Network (DMCHNN) is established, where one memristor represents external electromagnetic radiation and the other mimics synaptic connections. Two independent amplitude controllers are devised for signal rescaling, being capable of adjusting signal amplitudes in various modes, such as single-scroll, double-scroll, multi-double-scroll and coexisting homogeneous multi-scroll attractors induced by initial offset boosting. Simulations indicate that the parameter operating range of the amplitude controllers can reach up to 105or beyond. Furthermore, the performance of the amplitude controllers is additionally verified through the implementation based on the CH32 microcontroller. Rescaled chaotic signals are evaluated to determine their robust effectiveness in the deployment of pseudo-random number generators (PRNG). Eventually, the multi-scroll chaotic data with different amplitudes generated from DMCHNN is fed into the optimization algorithms for neural network optimization, which is utilized for medical image classification.

Evidences have manifested that unidirectional ring neural networks lack the ability to generate desired chaos. This paper formulates a novel memristor regulation (MR) approach to constructing a no-equilibrium bi-memristor unidirectional ring neural network (BMURNN), in which two distinct memristors are incorporated into a unidirectional ring neural network derived from the Hopfield neural network, with enhanced chaotic complexity, whereas one serving as a memristive synapse and the other as an emitter of electromagnetic radiation. Numerical simulations reveal that any desired number of multi-scroll hidden chaotic attractors can be generated from the BMURNN via the non-ideal multi-piecewise nonlinear memristor, while the time-controlled multi-scroll attractor growth is output from the periodic function memristor, demonstrating that the memristors can enhance the chaos complexity of the original unidirectional ring neural network. Additionally, diverse coexisting hidden attractors, that is, hidden heterogeneous/homogeneous multistability evoked by the memory attributes of memristors, can be dynamically regulated by varying the initial conditions. Finally, a digital circuit is designed and implemented based on CH32 to validate the numerical simulations and theoretical analyses, and a new pseudorandom number generator is devised to explore the BMURNN for practical applications. Performance analyses demonstrate its superiority and high randomness, providing further proof for the effectiveness of the proposed MR method.

Discrete memristors have been employed in discrete maps for the purpose of chaos generation and regulation. In this paper, a novel universal model for discrete memristors is proposed to generate multi-attractors. The classical Hénon map and Rulkov neuron are chosen as two examples to verify the effectiveness of the proposed memristor. Coexisting homogeneous attractors are identified in the phase space by memristor-induced offset boosting. An arbitrarily desired number of coexisting attractors is extracted by the appropriate feedback strength of the memristor. What adds further interest to this case is that the amplitude is rescaled by a memristor-related parameter that works well over an infinite range. Number-related parameters are extracted to rescale the oscillation range of the chaotic signals. Moreover, CH32-based circuit implementation is built, which aligns with numerical simulation results. Finally, coexisting homogeneous chaotic signals are tested to explore their robust performance in the application of pseudo-random number generator.

The Star-connected Cascaded H-Bridge (SCHB) converter is one of the most attractive multilevel topologies for medium/high-voltage STATCOM. Under negative current compensation status, cluster voltage balance control is of great importance and requires extra unbalanced active power transfer pathway. Zero- sequence voltage injection (ZSVI) is a normal method to exchange the active power but the large ZVSI will seriously restrict the negative sequence current range. Hybrid Cascaded STATCOM, with two-level three- phase module is proposed to reduce the value of ZSVI, and the phasor control is designed by calculating the coordinate position, which reduces the calculation complexity and extend the negative sequence current compensation range greatly. Finally, the proposed control strategy within the derived negative sequence current range is verified by the experimental results on a 400V/7.5kVar hybrid cascaded STATCOM.

To achieve low cost and high power density, low dc-link capacitance is preferred in the Cascaded H-Bridge (CHB) STATCOM. However, low dc-link capacitance will result in the increase of dc-link ripples, which will introduce voltage harmonics through modulation. According to the operation principle of the inverter, the output voltage will not exceed the input voltage at any time. Hence, the output voltage range of STATCOM will be affected by large voltage oscillation across the DC link, leading to affect the output capacity of STATCOM. Thus, to guarantee operation range of STATCOM, system capacity should be a key design consideration in the design of low dc-link capacitance, which has not been taken into consideration in the existing design methods. To address this issue, this paper establishes the relationship between the range of compensable current and the dc-link capacitance based on the Kirchhoff's law. Based on this relationship, a new design consideration is proposed to achieve the minimum dc-link capacitance, which is as small as possible under certain capacity of STATCOM. The designed capacitance can satisfy the requirements of system capacity and support the proper operation of STATCOM. Meanwhile, the designed capacitance is small enough to achieve low cost and high power density. A 400V/7kVA STATCOM prototype is built to verify the derived relationship and the proposed design method.