Active disturbance rejection control (ADRC) is widely applied in the motion control and the industrial process control for its merits of the uncertainty rejection, efficacy and ease of use. However, most process control models exist time-delay, which greatly deteriorates the closed-loop control performance of ADRC. Moreover, the controller gain tuning of ADRC is complex, laborious and time-consuming. Considering the above two difficulties, the key innovation of the paper is that aiming at the practical wet electrostatic precipitator (WESP) system with time-delay, a simple yet robust TD-ADRC gain tuning method is developed, which is mainly constructed by artificial time-delay block, observer and feedback control loop. The proposed gain tuning method is based on the bandwidth-parameterization technique, further using the scaling skill to simplify the gain tuning rule and stability analysis. Additionally, the exhaustive robustness metrics of closed-loop system are analyzed, which ensures that the gain tuning method maintains the controlled time-delay system with qualified robustness and well control performance. Alternatively, another robust stable region of TD-ADRC controller parameters are explored through the distinctively rigorous stability proof. Finally, plenty of simulations of WESP based on the practical operation data validate the effectiveness of the proposed method. Note to Practitioners— This paper was focused on gain tuning problem of the time-delay active disturbance rejection control (TD-ADRC) method. The controlled system is the first order plus time-delay (FOPTD) wet electrostatic precipitator (WESP) system. Thus, the novelties can be viewed from perspectives of method research and the combination of method and WESP model. From the perspective of the method research, time-delay in the process model is the obstacle to the most ADRC design or other kinds of controller design, and it always brings out lots of complexities to the gain tuning. In order to deal with the aforementioned problems, proposed TD-ADRC controller is designed for the purpose of simpler controller structure compared to the existing TD-ADRC method. Consequently, the simple gain tuning rule of proposed TD-ADRC method is explored by the exhaustive robustness analysis, which is under the pillar of the rigorous stability proof. Therefore, the engineer can avoid the laborious and time-consuming gain tuning procedure at the field and have the comprehensive hold to the robustness metric of time-delay system. From another perspective, proposed method can robustly regulate the WESP output in the presence of time-delay and disturbances in simulation, whose model is based on the practical data from the step response test. By the way, the proposed TD-ADRC is universal for higher order system which has the potential to apply in other system. It is promising that the proposed method could apply to the coal-fired station in the mid-term. In the future, more detailed researches about the versatile of the proposed method for the higher order system should be taken in an effort.

With a goal to improve transportation safety, this paper proposes a collaborative driving framework based on assessments of both internal and external risks involved in vehicle driving. The internal risk analysis includes driver drowsiness detection and driver intention recognition that helps to understand the human driver's behavior. Steering wheel data and facial expression are used to detect the driver's drowsiness. Hidden Markov models are adapted to recognize the driver's intention using the vehicle's lane position, control, and state data. For the external risk analysis, a co-pilot utilizes a collision avoidance system to estimate the collision probability between the ego vehicle and other nearby vehicles. Based on the risk analyses, we design a novel collaborative driving scheme by fusing the control inputs from the human driver and the co-pilot to obtain the final control input for the ego vehicle under different circumstances. The proposed collaborative driving framework is validated in an assisted-driving testbed, which enables both autonomous and manual driving capabilities.

This paper proposes a driver assistance system with a collaborative control framework between the human driver and a Collision Avoidance System (CAS) for vehicles on the road while considering the driver drowsiness status. Driver drowsiness detection is performed with two inputs: driver's facial data from a camera mounted in front of the driver and steering wheel data from the car controller system. We use the driver's drowsiness state as an input to the collaborative control framework in which the CAS algorithm runs in parallel with the human control and only intervenes under certain situations to assist the human driver. Experiments were performed on a simulated vehicle driving testbed to evaluate our drowsiness detection system and demonstrate the effectiveness of the proposed collaborative control framework.