The field of control systems and robotics is witnessing significant developments, with a focus on improving the stability, robustness, and performance of control algorithms. Researchers are exploring innovative approaches to address challenges such as collision avoidance, set-point tracking, and disturbance rejection. Notably, the use of semi-infinite programming, neural-network-based controllers, and robust model predictive control is gaining traction. These advancements have the potential to enhance the control of complex systems, including nonlinear and perturbed systems.

Noteworthy papers include: Design and Analysis of Curved Electrode Configurations for Enhanced Sensitivity in 1-Axis MEMS Accelerometers, which presents a comprehensive study on curved electrode geometries for improving the sensitivity of MEMS accelerometers. Synthesis and SOS-based Stability Verification of a Neural-Network-Based Controller for a Two-wheeled Inverted Pendulum, which establishes the feasibility of a sum of squares-based stability verification procedure for neural-network-based controllers. Robust tracking MPC for perturbed nonlinear systems, which presents a novel robust predictive controller for constrained nonlinear systems that can track piece-wise constant setpoint signals.