Predicting the Metro Scenario-based Spatiotemporal Evolution of Land Use Using CA-Kalman Filter Model: a Case Study of Nanjing City, China

The expansion of metro system can bring varying degrees of impact to the surrounding environment. To study this complex system problem, this paper discusses the temporal and spatial impact by metro system from the perspective of land use change simulation and scenario analysis. The traditional cellular automata (CA) model can realize the simulation of land use change under various scenarios through system dynamics or Markov chain to control the long-term demand forecasting. However, this type of model ignores the filtering of noise data from imageries and increases uncertainty of the system. Therefore, based on the Future Land Use Simulation (FLUS) model, this paper integrates Kalman filter to control the stochastic process of the state-space system, and predicts the spatio-temporal evolution of land use change impacted by metro system in Nanjing from 2019 to 2035. The results show that: (1) The proposed CA-Kalman filter model can realize the optimized simulation of land use change with good accuracy; (2) Urban patches impacted by metro system will emerge from the existing urban boundaries at the cost of occupation of cultivated land, although there is still significant expansion of urban land and construction land, it will reach the upper limit in 2050.

Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method

This study formulates a dynamical system for the control of a single product inventory system in accordance with the random value of demand and the percentage of damaged product during the delivery process. The formulated model has the form of a linear state-space system comprising of two disturbances, which represents the random value of demand and the percentage of the damaged product during delivery. The optimal value of the product amount ordered to the supplier is properly calculated by using the linear quadratic gaussian (LQG) method. The controller is used by the manager to make inventory level decisions under the uncertainty of demand and damaged items during the product delivery process. The result showed that the optimal product order for each review time was achieved, and the inventory level was used to obtain the right set point properly. Moreover, based on comparison with other research results, the proposed model was well performed.

Inverse Optimal Control in State Derivative Space System with Applications in Motor Control

This paper mathematically explains how state derivative space (SDS) system form with state derivative related feedback can supplement standard state space system with state related feedback in control designs. Practically, inverse optimal control is attractive because it can construct a stable closed-loop system while optimal control may not have exact solution. Unlike the previous algorithms which mainly applied state feedback, in this paper inverse optimal control are carried out utilizing state derivative alone in SDS system. The effectiveness of proposed algorithms are verified by design examples of DC motor tracking control without tachometer and very challenging control problem of singular system with impulse mode. Feedback of direct measurement of state derivatives without integrations can simplify implementation and reduce cost. In addition, the proposed design methods in SDS system with state derivative feedback are analogous to those in state space system with state feedback. Furthermore, with state derivative feedback control in SDS system, wider range of problems such as singular system control can be handled effectively. These are main advantages of carrying out control designs in SDS system.

Time-Domain Analysis of Fractional Electrical Circuit Containing Two Ladder Elements

The paper is devoted to the theoretical and experimental analysis of an electric circuit consisting of two elements that are described by fractional derivatives of different orders. These elements are designed and performed as RC ladders with properly selected values of resistances and capacitances. Different orders of differentiation lead to the state-space system model, in which each state variable has a different order of fractional derivative. Solutions for such models are presented for three cases of derivative operators: Classical (first-order differentiation), Caputo definition, and Conformable Fractional Derivative (CFD). Using theoretical models, the step responses of the fractional electrical circuit were computed and compared with the measurements of a real electrical system.

System Identification Guidance for Multirotor Aircraft: Dynamic Scaling and Test Techniques

State-space system identification was performed to extract flight dynamic models for hovering flight of a 55 cm, 1.56 kg hexacopter unmanned aerial vehicle. Different input excitation techniques were tested to determine which maneuvers provided high-quality system identification results for small-scale multirotor vehicles. These input excitation techniques included automated frequency sweeps, varying in amplitude, and multisine sweeps. Coherence, Cramer–Rao bounds, and insensitivities were used as metrics for comparing the system identification results. A parametric variation of frequency sweep amplitudes were performed in all axes (roll, yaw, pitch, and heave) to provide guidance on frequency sweep amplitude for small-scale multirotor unmanned aerial systems. The dynamics of the 55 cm hexacopter were used to estimate the dynamics of a larger 127-cm hexacopter via Froude scaling based on hub-to-hub distance as the characteristic length. The scaled results were compared to an actual system identification model of a 127-cm hexacopter.