Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

This article investigates the adaptive fuzzy tracking control design for uncertain nonstrict-feedback nonlinear systems with time-varying constraints and asymmetric input saturations. It is known that time-varying output constraint, time-varying error constraints, and input saturations are commonly seen issues in many practical engineering systems due to inherent physical limitations and performance requirements. To deal with these problems and improve the convergence rate of the control system, a novel fixed-time convergent adaptive fuzzy control scheme is proposed for the considered uncertain nonlinear systems via the time-varying barrier Lyapunov function (BLF) technique. First, to deal with the unknown nonlinearities and uncertainties in the system dynamic model, fuzzy logic systems are utilized to approximate the unknown functions of the considered model. Then, the problem of asymmetric input amplitude and rate saturations is handled by constructing a unified smooth characterizing function and an innovative auxiliary design signal system. Subsequently, considering physical limitations and performance requirements, a novel time-varying BLF-based adaptive fuzzy backstepping control scheme is designed for the uncertain nonstrict-feedback nonlinear systems to realize superior tracking performances and keep the states staying in predefined time-varying compact regions during operations. Further, rigorous theoretical analyses have been conducted to show that the proposed control scheme achieves fixed-time convergence of all signals in the closed-loop control system. Finally, representative simulation results verify the effectiveness of the proposed control scheme.

Generating collision-free, time-efficient paths in an uncertain dynamic environment poses huge challenges for the formation control with collision avoidance (FCCA) problem in a leader-follower structure. In particular, the followers have to take both formation maintenance and collision avoidance into account simultaneously. Unfortunately, most of the existing works are simple combinations of methods dealing with the two problems separately. In this article, a new method based on deep reinforcement learning (RL) is proposed to solve the problem of FCCA. Especially, the learning-based policy is extended to the field of formation control, which involves a two-stage training framework: an imitation learning (IL) and later an RL. In the IL stage, a model-guided method consisting of a consensus theory-based formation controller and an optimal reciprocal collision avoidance strategy is designed to speed up training and increase efficiency. In the RL stage, a compound reward function is presented to guide the training. In addition, we design a formation-oriented network structure to perceive the environment. Long short-term memory is adopted to enable the network structure to perceive the information of obstacles of an uncertain number, and a transfer training approach is adopted to improve the generalization of the network in different scenarios. Numerous representative simulations are conducted, and our method is further deployed to an experimental platform based on a multiomnidirectional-wheeled car system. The effectiveness and practicability of our proposed method are validated through both the simulation and experiment results.

This paper presents a novel fixed-time sliding mode disturbance observer (SMDO)-based robust backstepping cruise tracking control scheme with closed-loop finite-time convergence for flexible air-breathing hypersonic vehicles (FAHVs). In order to enhance the control system's robustness, a fixed-time SMDO is designed to compensate for the flexibility effects, model uncertainties, and external disturbances in FAHVs. In consequence, fixed convergence time of disturbance observation is achieved independently of initial estimation errors. Furthermore, velocity and altitude continuous finite-time tracking controllers are constructed by incorporating the SMDO and nonsmooth backstepping technique. To solve the problem of ``explosion of complexity'' in the conventional backstepping approach, nonsmooth filters are specifically constructed to generate the derivatives of virtual control laws. A Lyapunov-based stability analysis is conducted to show the finite-time convergence of the closed-loop FAHV control system. Finally, several representative numerical simulations are given to illustrate the effectiveness and superiority of the proposed control strategy.

Uncertainty and measurement noise are main obstacles that limit the tracking control performances of flexible air-breathing hypersonic vehicles (FAHVs). In this paper, we propose a novel fixed-time convergent nonsmooth backstepping control scheme for FAHV via augmented sliding mode observers (ASMOs) to overcome these obstacles. The ASMOs are first designed for the FAHV dynamics by employing the measured states corrupted by noises as inputs. On one hand, the ASMOs can simultaneously estimate the uncertainties and filter out the measurement noises. On the other hand, the observation error of each ASMO can be convergent within a fixed time independent of its initial observation error. Then, based on the estimation results, the altitude and velocity tracking controllers are developed by using fixed-time nonsmooth backstepping technique. Afterwards, a Lyapunov-based stability analysis is given to illustrate the fixed-time convergence of the closed-loop signals of the FAHV control system. Finally, comparative simulations are conducted to illustrate the superiority of the proposed control scheme.

In this article, a novel method, called attention enhanced reinforcement learning (AERL), is proposed to address issues including complex interaction, limited communication range, and time-varying communication topology for multi agent cooperation. AERL includes a communication enhanced network (CEN), a graph spatiotemporal long short-term memory network (GST-LSTM), and parameters sharing multi-pseudo critic proximal policy optimization (PS-MPC-PPO). Specifically, CEN based on graph attention mechanism is designed to enlarge the agents’ communication range and to deal with complex interaction among the agents. GST-LSTM, which replaces the standard fully connected (FC) operator in LSTM with graph attention operator, is designed to capture the temporal dependence while maintaining the spatial structure learned by CEN. PS-MPC-PPO, which extends proximal policy optimization (PPO) in multi agent systems with parameters’ sharing to scale to environments with a large number of agents in training, is designed with multi-pseudo critics to mitigate the bias problem in training and accelerate the convergence process. Simulation results for three groups of representative scenarios including formation control, group containment, and predator–prey games demonstrate the effectiveness and robustness of AERL.