Parameter tuning of ADRC and its application based on CCCSA

Active disturbance rejection controller can effectively adjust uncertain factors and the disturbance of the model by compensation. However, this advantageous controller has met with a technical bottleneck in its extensive use of engineering application because of its multiplicity of the parameters and the difficulty of integrating them. This paper proposes a chaotic cloud cloning selection algorithm by referring to cloud model and immune cloning selection, generating initial population by employing chaotic initialization to improve the quality of the initial antibodies, realizing mutation with basic normal cloud generator to improve the diversity of antibodies. Then the chaotic cloning selection algorithm is applied to the optimization of parameter integration. This new algorithm is tested and verified as to its convergence speed, convergence precision, and robustness with classic function. The results of simulation experiments for a time-delay system demonstrate that the optimized control system not only possesses excellent control performance but also exhibits strong robustness and anti-interference ability.