This paper presents a comparative study between Adaptive Quantized Control (AQC) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning for uncertain linear systems with input quantization over communication channels subject to packet loss. The considered setting also includes dynamic switching from a nominal unstable system to a more unstable one during operation. The AQC is designed for unknown system dynamics using acknowledgment messages to compensate for packet losses, whereas the DDPG controller is trained using the nominal system model without acknowledgment messages. Numerical results show that the DDPG controller achieves faster transient responses and improved damping within its training environment. However, under model uncertainty, packet loss, and dynamic switching, the AQC consistently demonstrates superior robustness owing to its rigorous Lyapunov stability guarantees. These results highlight the trade-off between data-driven performance and model-based robustness, and provide insight into the applicability of reinforcement learning and adaptive control for networked uncertain systems.
This paper presents a learning-based control framework that integrates feedback linearization with reinforcement learning for the adaptive control of nonlinear mechatronic systems. The control law is derived using Lyapunov stability analysis, ensuring closed-loop stability in the…
Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control. This work illustrates a two-stage learning pip…
We propose a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties. Unlike existing methods, during the optimized backstepping design process, we use the actor-critic-identifier structure. The acto…
Unknown linear time-varying (LTV) systems require the control policy to adapt from online closed-loop data as dynamics evolve. Existing methods usually update the policy by solving a one-shot optimization problem, which can be computationally demanding and sensitive to noisy mode…
Control co-design considers the physical system and its controller together, enabling the strong coupling between system design and control to be uncovered and exploited. This is especially relevant in aeroelastic flight systems, where structural, aerodynamic, and control design…
Bilateral teleoperation systems that include joint flexibility better reflect real robotic systems used in surgery, space, and rehabilitation. However, joint flexibility together with time-varying communication delays makes it difficult to maintain stable and coordinated motion b…