The field of control systems is moving towards the development of more sophisticated and robust control algorithms that can handle uncertainty and nonlinearity. Researchers are exploring the use of advanced optimization techniques, such as Whale Optimization Algorithms and stochastic optimal control, to improve the performance of control systems. The integration of safety constraints and online learning mechanisms is also becoming increasingly important, particularly in safety-critical systems. Furthermore, the simultaneous improvement of control and estimation is being investigated, with applications in areas such as battery management systems. Noteworthy papers in this area include:

• A study that demonstrates the effectiveness of a fractional order fuzzy PID controller optimized by Whale Optimization Algorithms for Depth of Anesthesia control.
• A paper that presents a learning-based optimal control framework for uncertain systems with a self-triggered mechanism, ensuring both safety and control performance.
• A research that employs a stochastic optimal control approach to simultaneously improve control and estimation in battery management systems.