Circadian misalignment increases mood vulnerability in simulated shift work

Night shift work can associate with an increased risk for depression. As night workers experience a ‘misalignment’ between their circadian system and daily sleep–wake behaviors, with negative health consequences, we investigated whether exposure to circadian misalignment underpins mood vulnerability in simulated shift work. We performed randomized within-subject crossover laboratory studies in non-shift workers and shift workers. Simulated night shifts were used to induce a misalignment between the endogenous circadian pacemaker and sleep/wake cycles (circadian misalignment), while environmental conditions and food intake were controlled. Circadian misalignment adversely impacted emotional state, such that mood and well-being levels were significantly decreased throughout 4 days of continuous exposure to circadian misalignment in non-shift workers, as compared to when they were under circadian alignment (interaction of “circadian alignment condition” vs. “day”, mood: p < 0.001; well-being: p < 0.001; adjusted p-values). Similarly, in shift workers, mood and well-being levels were significantly reduced throughout days of misalignment, as compared to circadian alignment (interaction of “circadian alignment condition” vs. “day”, mood: p = 0.002; well-being: p = 0.002; adjusted p-values). Our findings indicate that circadian misalignment is an important biological component for mood vulnerability, and that individuals who engage in shift work are susceptible to its deleterious mood effects.

Iterative learning control for trajectory tracking of a parallel Delta robot

This paper proposes an iterative learning controller (ILC) under the alignment condition for trajectory tracking of a parallel Delta robot, that performs various repetitive tasks for palletization. Motivated by the high cadence of our application that leads to significant coupling effects, where the traditional PD/PID fail to satisfy the requirements performances. A PD-type ILC is combined with a PD controller in order to enhance the performance through iterations during the whole operation interval. The traditional resetting condition is replaced by the practical alignment condition, then the convergence of the tracking error is derived based on the Lyapunov’s theory. We definitely point out that the position and velocity errors decrease as the number of iterations increases. Experiments are carried out to demonstrate the effectiveness of the proposed controller.

Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.