Real-time measurements of key effluent parameters play a highly crucial role in wastewater treatment. In this research work, we propose a soft sensor model based on deep learning which combines stacked autoencoders with neural network (SAE-NN). Firstly, based on experimental data, the secondary variables (easy-to-measure) which have a strong correlation with the biochemical oxygen demand (BOD5) are chosen as model inputs. Moreover, stochastic gradient descent (SGD) is used to train each layer of SAE to optimize weight parameters, while a strategy of genetic algorithms to identify the number of neurons in each hidden layer is developed. A soft sensor model is studied to predict the BOD5 in a wastewater treatment plant to evaluate the proposed approach. Interestingly, the experimental results show that the proposed SAE-NN-based soft sensor has a better performance in prediction than the current common methods.

Based on the discrete event-triggered communication scheme (DETCS), a codesign method of dual security control and communication for the non-linear cyberphysical system (CPS) with denial-of-service (DoS) attack and actuator fault is studied under multi-objective constraints. First, to effectively distinguish DoS attacks with different energy levels, the DoS attack detection method is established based on the maximum allowable delay of the system. Second, a non-linear CPS dual security control framework that integrates active-passive attack tolerance for DoS attack and active fault tolerance for actuator fault is constructed. Third, the low-energy DoS attack is regarded as a special kind of time delay, and a robust observer for the estimation of system states and actuator fault is designed, and a codesign method of passive attack tolerance, active fault tolerance, and communication is proposed. Furthermore, a correction compensation algorithm of control quantity is proposed for high-energy DoS attack based on the PID idea to make active-passive attack tolerance more effective. Finally, the effectiveness of the proposed method is verified by the simulation of a quadruple-tank model.

Considering the actuator saturation, the problem of multiobjective constraint fault-tolerant control is studied for networked Takagi-Sugeno (T-S) fuzzy system with nonuniform transmission period under the discrete event-triggered communication scheme (DETCS). A closed-loop T-S fuzzy model, which includes numerous factors such as actuator saturation, actuator failure, event-triggered condition, nonuniform transmission period, and time-delay, is established by transferring the effect caused by the nonuniform update period on the system performance into the effect on the system time-delay. Two stability criterions and two codesign methods for the networked closed-loop failure T-S fuzzy system are developed based on a discontinuous Lyapunov-Krasovskii function, a kind of reciprocally convex technology, a kind of linear convex combination, Jessen inequality, and Wirtinger’s inequality. A feasible experimental solution is presented to verify the effectiveness of the proposed method.

With the wide application of quadrotor unmanned aerial vehicles (UAVs), the requirements for their safety and reliability are becoming increasingly stringent. In this paper, based on the feedback of airframe performance health perception information and the predictive function control strategy, the autonomous maintenance of a quadrotor UAV with multi-actuator degradation is realised. Autonomous maintenance architecture is constructed by the predictive maintenance (PdM) idea and the Laguerre function model predictive pontrol (LF-MPC) strategy. Using the two-stage Kalman filter (TSKF) method, based on the established UAV degradation model, the aircraft state and actuator degradation state are predicted simultaneously. For the predictive perception of system health, on the one hand, the system health degree (HD) based on Mahalanobis distance is defined by the degree of airframe state deviation from the expected state, and then the failure threshold of the UAV is obtained. On the other hand, according to the degradation state of each actuator, a comprehensive degradation variable fused with different weight coefficients of multiple actuators degradation is used to obtain the probability density function (PDF) of remaining useful life (RUL) prediction. For the autonomous maintenance of system health, the LF-MPC weight matrixes are adjusted adaptively in real-time based on the HD evaluation, to achieve a compromise balance between UAV performance and control effect, and greatly extend the working time of UAV. Simulation results verified the effectiveness of the proposed method.

This paper investigated the co-design problem of less conservative integrated security control and communication for a nonlinear cyber-physical system (CPS) with an actuator fault and false data injection (FDI) attacks. Firstly, considering the efficient utilisation and allocation of computing and communication resources, an integrated framework was proposed from the perspective of active defence against FDI attacks. Secondly, the actuator fault and FDI attacks were augmented as a vector, and a robust observer was proposed to estimate the system state, actuator fault and FDI attacks. Furthermore, based on the obtained estimation results and the location of the FDI attack in the dual-end network, we designed an integrated security control strategy of active attack tolerance and active fault tolerance and, by constructing Lyapunov–Krasovskii functions and using time-delay system theory and the affine Bessel–Legendre inequality, a less conservative co-design method for integrated security control and network communication resource saving was developed. Finally, a simulation experiment of a quadruple tank was carried out to demonstrate the effectiveness of the proposed method.