This paper proposes a constrain spatial adaptive iterative learning controller (CSAILC) for the displacement-speed trajectory tracking of automatic train control system with unknown parametric/nonparametric uncertainties and speed constraints. First, the nonlinear dynamic model of train operation is transformed from temporal domain into spatial domain utilizing a spatial state differentiator. Besides, the displacement-related parametric/nonparametric uncertainties are updated in the iteration axis. Furthermore, a barrier function is involved to satisfy the speed constraint, and the corresponding convergence analysis of the proposed CSAILC for automatic train control (ATC) is derived based on the spatial composite energy function. In addition, numerical simulations of train tracking control are carried out, and simulation results indicate that the proposed CSAILC achieves good effectiveness in a high-speed train (HST) control system.

Macroscopic fundamental diagram (MFD) that describes traffic flow in an urban road network can be used to design perimeter control method to regulate the traffic flow from a macroscopic level. Most of the perimeter control algorithms are regarded as a kind of model-based feedback control method, whose performance is hardly to improve in practice due to the model uncertainty. By noticing the repetitive nature of urban traffic flow, an iterative learning control (ILC) based perimeter control method is proposed for an urban region. Since the repetitive information of the controlled system is fully utilized, an improved tracking performance is guaranteed by theoretical analysis, and simulation results verify the effectiveness of the proposed perimeter control method.

In this brief, an enhanced genetic back-propagation neural network with link switches (EGA-BPNN-LS) is proposed to address a data-driven modeling problem for gasification processes inside United Gas Improvement (UGI) gasifiers. The online-measured temperature of crude gas produced during the gasification processes plays a dominant role in the syngas industry; however, it is difficult to model temperature dynamics via first principles due to the practical complexity of the gasification process, especially as reflected by severe changes in the gas temperature resulting from infrequent manipulations of the gasifier in practice. The proposed data-driven modeling approach, EGA-BPNN-LS, incorporates an NN-LS, an EGA, and the Levenberg-Marquardt (LM) algorithm. The approach cannot only learn the relationships between the control input and the system output from historical data using an optimized network structure through a combination of EGA and NN-LS but also makes use of the networks gradient information via the LM algorithm. EGA-BPNN-LS is applied to a set of data collected from the field to model the UGI gasification processes, and the effectiveness of EGA-BPNN-LS is verified.

An enhanced genetic BP neural network with link switches (EGA-VRBPNN) is proposed in this work to address the data-driven modeling problem for the gasification process inside a UGI gasifier. During gasification processes, the online measured gas temperature is crucial but difficult to model its' dynamics via first principles because of the tremendous complexity of the gasification process, which is mainly reflected from severe changes of the gas temperature versus infrequent and small manipulations of parts of the input variables. EGA-VRBPNN, which incorporates a neural networks with link switches (NN-LS) with an enhanced genetic algorithm (EGA) and the Levenberg-Marquardt (LM) algorithm, can not only learn the relationships between control inputs and system outputs from historical data with the help of optimized network structure through combination of the EGA and NN-LS, but also overcome the drawbacks of gradient-based method and make full use of the network's gradient information to achieve a satisfactory accuracy. A set of data collected from the practical fields are applied to modeling via the EGA-VRBPNN, by which the effectiveness of the EGA-VRBPNN is verified.