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This research presents an optimized DC motor controller designed to enhance performance and efficiency in speed regulation, acknowledging the pivotal role of control strategies in modern engineering applications. The controller maximizes the capabilities of the integral linear quadratic regulator (ILQR) framework, fine-tuned using state-of-the-art particle swarm optimization (PSO) techniques and a well-defined cost function alongside other bio-inspired algorithms. Additionally, a disturbance observer is incorporated into the LQR scheme to improve the system’s resistance to external disturbances, both constant and time-varying. PSO and genetic algorithms (GA) are employed to identify appropriate LQR weighting, significantly increasing control performance. This integration produces a robust control system to improve the performance and efficiency of DC motor speed regulation. It provides an elaborate structure that can be adapted to various technical applications. Numerous simulations demonstrate the enhanced performance of the developed technique in achieving optimal speed control while maintaining high robustness.

Multi-agent systems (MAS) are composed of multiple autonomous agents that interact and collaborate to achieve common objectives. These systems are characterized by their decentralization, scalability, robustness, and the local perspectives of individual agents. In the realm of quadrotor applications, MAS can perform complex tasks such as formation flying, surveillance, search and rescue, and cooperative payload transport. The control of such systems often involves techniques like trajectory planning, consensus algorithms, formation control, and obstacle avoidance. This study introduces a multi-agent quadrotor system controlled using sliding mode backstepping control (SMBC), which combines the advantages of backstepping and sliding mode control to ensure stability and robustness. The novel aspect of this research is the transformation of the leader quadrotor into a fractional-order system (FOS) while maintaining the followers as integer-order systems, all controlled using the same SMBC method. MATLAB simulations validate that the fractional-order leader (FOL) achieves superior trajectory tracking compared to the integer-order leader. Furthermore, the followers tracking the fractional-order leader exhibit enhanced performance, with reduced tracking errors and quicker response times. These findings demonstrate the potential advantages of incorporating fractional-order dynamics into multi-agent control systems.

This paper investigates an adaptive control problem for switched cyber-physical systems subject to external disturbances, denial-of-service (DoS) attacks, and false data injection attacks. We consider attackers to be able to threaten the networked system performance, where the DoS depletes the network bandwidth and might randomly block out the transmission signals or produce a significant delay in transmission signals, while the false data injections fabricate the transmission signal as a disturbance. To handle the abrupt occurrence of cyber attacks, the power system is modeled as a switched system. The switched system responds to changing dynamic models and among the states when the system parameters are altered due to disturbances, denial-of-service, and false data injection attacks. The proposed controller unit involves a reliable estimation scheme to estimate and compensate for cyber attacks while sustaining the desired performance and robustness. A distinctive feature of the proposed controller is the introduction of a low-pass filter, which is particularly convenient for attenuating parameter fluctuations and guaranteeing a rapid adaptation rate. The simulation of the closed loop system shows that the proposed $\mathcal {L}_{1}$ controller outperforms the $H_{\infty} $ controller. A simulation study is established to illustrate the influence and robustness of the developed controller, which can enhance the transient response under different operating conditions.

Distributed generation (DG) technology arises in several applications of power supply to tackle the issues of increasing energy demands. The concept of microgrids is introduced to integrate the DG technology into utility power networks. This paper introduces a fuzzy control scheme for networked systems utilizing a quantized H∞ estimator over communication networks for DGs subject random constraints. The developed estimation algorithm is worthy for ensuring the network stability and enduring any sort of quantization, time delay, and packet drops. In these senses, adequate conditions have been mainly derived by means of linear matrix inequalities. An illustrative examples are demonstrated to confirm the effectiveness of the meant design procedure. [ABSTRACT FROM AUTHOR]