To address the problem of the sparrow search algorithm (SSA) has poor global search ability, weak local development ability, and easily falls into the local optimal solution, a multi-strategy improved evolutionary sparrow search algorithm (MSSA) is proposed. The introduction of the tent chaotic map improves the diversity of the initialization population, accelerates algorithm convergence, and improves convergence accuracy. Endow sparrow finders with a random search ability to coordinate the balance between global search and local development. To discover dangerous sparrow individuals, the mutation evolution operation is completed, and a greedy strategy is combined to improve the processing ability of the algorithm for local optimal solutions and make full use of each sparrow individual. Six benchmark functions were used to comprehensively verify the feasibility of the proposed algorithm based on four aspects: optimization ability, robustness, convergence ability, and optimization trajectory. These results indicate that the proposed algorithm is superior. Finally, through the comparison and analysis of the parameter identification and control strategies of the two servo systems in practical application, on the one hand, the advantages of the proposed algorithm in practical engineering applications are illustrated. In addition, a fuzzy PID control strategy based on MSSA is proposed. By adding step, sinusoidal, triangular wave and disturbance signals, simulation experiments show that the control strategy can significantly improve the dynamic and steady performance of the servo system.
In a high-precision servo system, the nonlinear friction link is the key factor affecting the system performance. Reasonable solving of the friction link in servo systems has become a focus of current research. This paper summarizes the friction nonlinearity that affects the control performance of servo systems. First, the characteristics of friction are summarized, and the advantages and disadvantages of typical friction models in recent years are analyzed. Subsequently, existing friction model parameter identification methods are introduced and evaluated. On this basis, the development level of the friction nonlinear control strategy is analyzed from three aspects: friction model-based control, friction model-free control, and compound control. Finally, the objective advantages and disadvantages of the existing technology are summarized, and the future development direction of the friction model and selection reference for the nonlinear friction control strategy are comprehensively discussed.
Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.
A valve-controlled hydraulic cylinder system has the characteristics of uncertainty and time-variance, and the electro-hydraulic servo unit encounters shock, vibration, and other external interference when working, which seriously affect the stability of the valve-controlled hydraulic cylinder system. Therefore, it is necessary to introduce an active disturbance rejection controller (ADRC) into the electro-hydraulic servo control. However, there are many ADRC parameters, and it is difficult to set these only with expert experience. Therefore, we propose applying the gray wolf optimization algorithm (GWO) to the ADRC, to auto-tune the parameters and find the optimal solution. In addition, the advantages of the GWO in ADRC parameter tuning are proven and analyzed. The simulation and experimental results showed that the GWO algorithm had a faster mean time for parameter tuning and the smallest fitness value (integrated time and absolute error), compared to the particle swarm optimization algorithm and genetic algorithm. Moreover, a valve-controlled cylinder system, after parameter tuning by the gray wolf optimization algorithm, could accurately adjust the parameters of the auto-disturbance rejection controller, with a faster response speed, smaller overshoot, and better anti-disturbance ability.
Purpose This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications. Design/methodology/approach This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters. Findings The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short; there is no overshoot; the tracking process is relatively stable; the anti-interference ability is strong; and the optimization effect is better. Originality/value The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.
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