# Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter
## 1 Introduction
Other global optimization algorithms have also been suggested for the application of nonlinear systems, such as genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), and ant colony optimization (ACO) [60; 61; 62; 63; 64; 65; 66; 67]. However, these methods still have hidden shortcomings, for example, they are prone to premature convergence to the local optimum. In recent years, under the trend of rapid development of artificial intelligence, an improved version of PSO, that is the chaos PSO (CPSO) intelligent technology has been successful used in many different scientific occasions [68; 69; 70; 71; 72; 73]. It not only achieves high-quality global solutions in a short calculation time, but also does not prematurely converge to a local extreme. As a result, the CPSO can find optimal values for NFCTA control parameters, thereby significantly improving control performance and avoiding complicated testing and trial-and-error adjustments of parameters. Compared to a typical TA or SMC without optimization algorithms or intelligent solutions, the proposed strategy can provide DC-AC converter control designers with another reference choice and constructive advice. Although the definitive steady-state tracking response of the improved closed-loop system is not the best in the latest THD percentage results of the previous work, the proposed strategy successfully strengthens the TA approach and helps to determine the optimal adjustment of controller parameters. Combining the proposed NFCTA and CPSO prevents the system state from falling into local optimization untimely, and improves the global search capability. Therefore, the proposed strategy generates a high-performance full-bridge DC-AC converter system with lower THD percentage under rectifier loads and faster response under step load changes. Simulation results obtained using the MATLAB/Simulink environment for the proposed algorithm have been performed for the DC-AC converter, and experimental results based on the implementation of a digital signal processor (DSP) also verify the usability of the proposed strategy. It is worth mentioning that the proposed strategy is a single-objective optimization in this paper. It aims at establishing a high-quality AC output for a full-bridge DC-AC converter in the face of parametric changes and load interferences. In contrast to this paper, the publications [74; 75] provide a good reference for future PV system applications. They adopt the DC-DC converter with optimal algorithm to achieve solar cells operating at maximum power point, even when the PV array is partially shaded and then its power-voltage curve appears under multi-peak condition. The notable contributions to this paper include: The proposed circuit only uses a voltage feedback with the NFCTA, to achieve the uncomplicated precisely trajectory-tracking and the smallest possible THD. By employing CPSO, the design difficulty of NFCTA parameters and the trembling effect can be mitigatedsimultaneously. The proposed strategy develops advanced SMC technology, namely the NFCTA and CPSO, for efficient tracking error reduction of full-bridge DC-AC converter when confronted with transient and steady-state interferences. This paper is organized as follows: The dynamic modeling of the full-bridge DC-AC converter is illustrated in Section 2. Section 3 is devoted to the design of NFCTA and CPSO in order to reduce the trembling and optimize the system performance. Simulated and experimental results are furnished to authenticate this proposed strategy in Section 4. Afterward, the paper is concluded in Section 5.