Shanhe Jiang

To address actuator faults and external disturbances in nonlinear interconnected systems, this paper proposes a reduced-order sliding mode control method with H_∞ performance guarantees. An adaptive reduced-order sliding mode controller is first developed via system decoupling to achieve adaptive estimation of the upper bounds of actuator faults and external disturbances, while guaranteeing asymptotic convergence of the system states to the sliding surface. To further attenuate external disturbances, the designed sliding mode controller is combined with an H_∞ optimization scheme, ensuring that the influence of disturbances on control accuracy is suppressed below a prescribed level γ. Simulation results show that the proposed control scheme outperforms the compared control schemes.

The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.

Eddy-current displacement sensor (ECS) has been applied widely to the production of modern industry by reason of its characteristics of high sensitivity, good reliability, powerful anti-interference capacity, and noncontact measurement. However, it cannot be used when severe temperature drift occurs at high temperature. Some traditional compensation methods are difficult to achieve good performance with neglecting the nonlinearity. Hence, it is essential to propose a better method for temperature compensation. A novel temperature compensation approach for ECS problem using an improved sparrow search algorithm (ISSA) and radial basis function neural network (RBFNN) is proposed in this article. In the ISSA, a chaos strategy is introduced in the algorithm for avoiding local optimal point, and an elite opposition-based learning strategy is integrated to promote global search ability of ISSA with high efficiency. RBFNN is elected to model the temperature drift, and its parameters are determined by the proposed ISSA. The proposed method compensates the significant deterministic errors caused by temperature variation within a wide temperature range. The experimental schemes were designed to the effectiveness of the proposed method according to data fusion technology. The various test results obtained confirm the potential and effectiveness of the proposed approach compared to some other traditional temperature compensation methods presented in the literatures.

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet.

In this paper, a novel hybrid optimization approach, namely, gravitational particle swarm optimization algorithm (GPSOA), is introduced based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) to solve combined economic and emission dispatch (CEED) problem considering wind power availability for the wind‐thermal power system. The proposed algorithm shows an interesting hybrid strategy and perfectly integrates the collective behaviors of PSO with the Newtonian gravitation laws of GSA. GPSOA updates particle’s velocity caused by the dependent random cooperation of GSA gravitational acceleration and PSO velocity. To describe the stochastic characteristics of wind speed and output power, Weibull‐based probability density function (PDF) is utilized. The CEED model employed consists of the fuel cost objective and emission‐level target produced by conventional thermal generators and the operational cost generated by wind turbines. The effectiveness of the suggested GPSOA is tested on the conventional thermal generator system and the modified wind‐thermal power system. Results of GPSOA‐based CEED problems by means of the optimal fuel cost, emission value, and best compromise solution are compared with the original PSO, GSA, and other state‐of‐the‐art optimization approaches to reveal that the introduced GPSOA exhibits competitive performance improvements in finding lower fuel cost and emission cost and best compromise solution.