The objective of the present investigation is to introduce a novel adaptive fractional‐order proportional‐integral‐derivative controller, which is characterized by the online tuning of its parameters by utilizing five distinct multilayer perceptron neural networks employing the extended Kalman filter. Utilizing the backpropagation algorithm in training a multilayer perceptron neural network is deemed effective in identifying the structural system and estimating the plant. The controller is applied using the Jacobian derived from the online estimated model. The utilization of adaptive interval type‐2 fuzzy neural networks in conjunction with the extended Kalman filter tuning method and feedback error learning strategy results in enhanced stability and robustness of the controller in the face of estimation error, seismic disturbances, and unknown nonlinear functions. The study aims to validate the efficacy of the proposed controller by examining its performance on a 20‐story nonlinear building. The numerical results show that including a compensator enhances the performance of the adaptive fractional‐order proportional‐integral‐derivative controller. The results show that the proposed adaptive fractional‐order proportional‐integral‐derivative controller has a better performance than other controllers and that the interstory drift ratio criterion under the El Centro earthquake with a magnitude of 1.5 times experienced an improvement of up to 65% compared to other controllers, and this amount in the Kobe earthquake reached more than 58%. Other criteria have also experienced significant improvement using the proposed controller.

This paper presents a novel adaptive neurochaotic fuzzy control system based on type‐2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers under near‐field and far‐field earthquakes. In this proposed control system, the whole parameters of the plant are assumed to be completely unknown, the structural model is estimated using a multilayer perceptron neural network, and the system’s Jacobian is extracted. The online estimation model is used, and the controller parameters are adaptively trained using the extended Kalman filter and error back‐propagation method. Subsequently, the control force is applied to the active tuned mass damper, and the control objectives are met. The adaptive controller does not require initial settings, and a fractional‐order proportional‐integral‐derivative controller is added to maximize stability and robustness against seismic vibration. A simple adaptive controller optimized by a particle swarm is also presented as an innovation. Comparing the performance of the improved simple adaptive controller and adaptive neurochaotic fuzzy controller, the proposed controllers appear more efficient and accurate. However, the superiority of the novel adaptive neurochaotic fuzzy over the improved simple adaptive controller in reducing maximum displacement, acceleration, drift, and base shear while maintaining acceptable performance under parametric uncertainties and seismic conditions is substantial.