A novel integral sliding mode control (ISMC) strategy combining with disturbance observer (DO) for vehicle-following systems is presented in this paper. The vehicle platoon includes a leading vehicle and multiple following vehicles subjected to unknown acceleration uncertainties. Firstly, the ISMC based on DO and the constant time headway (CTH) policy is constructed to realize the string stability of the whole vehicle platoon in finite time, under the condition of zero initial spacing errors. The uncertainties of the vehicular system can be well estimated by extending DO even if the bounds of the uncertainties are not known exactly. In order to alleviate the chattering problem of the traditional ISMC, a continuous sliding surface including sign function is designed for the proposed ISMC strategy. Then, a modified constant time headway (MCTH) policy is proposed to overcome the requirement of zero initial spacing errors. In addition, the developed MCTH policy can effectively improve the string stability and safety of the vehicle platoon system. Finally, the effectiveness and advantages of the proposed algorithm are verified by the simulations and experiments.

We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9-13 FPS detection speed on a moderate commercial GPU.

At present, digital maps can estimate the travel time of each trip’s route but cannot offer a fuel consumption estimation at the same time. In this paper, we develop a fuel consumption model based on the Vehicle Specific Power (VSP) distribution, which can connect the traffic condition prediction with the fuel consumption model to predict fuel consumption. First, the traffic condition forecasting and the trip time of each route can be obtained through the digital map Application Programming Interface (API). Secondly, the users need to provide the engine displacement of their vehicles to match the fuel consumption model. Then, the fuel consumption prediction application based on Android is developed to forecast the fuel consumption by using traffic prediction data. Finally, the fuel consumption provided by the On-Board Diagnostic (OBD) data is used to verify the proposed application, and the forecasting error is less than 20%.

Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs), how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC) fault-tolerant controller with the Conditional Value at Risk (CVaR) objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC) problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

In this work, a novel anti-saturation model-free adaptive control (AS-MFAC) algorithm is proposed for the problem of pure electric vehicle’s braking energy recovery with the uncertain dynamic external factors. In the process of energy recovery during vehicle braking, actuator saturation and error accumulation occur due to various external conditions. In AS-MFAC, the non-linear dynamics of the vehicle braking energy recovery process is firstly linearized via a novel dynamical linearization technique with a time-varying parameter pseudo partial derivative (PPD). Then, by proposing the concept of the saturation parameter, the AS-MFAC controller is designed. Consequently, the stability and safety of the braking system are guaranteed while ensuring energy recovery. The major advantages of the AS-MFAC algorithm are that the controller uses only input and output data from the regenerative braking control system and this approach addresses the actuator saturation problem as well as provides a feasible solution. Moreover, the stability of the AS-MFAC algorithm is proven by rigorous mathematical theory and its effectiveness is verified by a series of experimental simulations. Remarkably, the proposed AS-MFAC controller has the property of symmetry since the controller structure and the corresponding parameter (time-varying PPD) estimator are both inferred based on the project algorithm. Consequently, the structures of the controller and the parameters of AS-MFAC have a symmetric similarity.