Jin Huang

Robust Control for Fuzzy Dynamical Systems: Uniform Ultimate Boundedness and Optimality

We propose a new approach for the control design of fuzzy dynamical systems. The system may contain uncertainty, which includes unknown parameter and input disturbance. The uncertainty lies within a prescribed fuzzy set. The control structure is deterministic, and, hence, not if-then rule-based. The desired controlled system performance includes uniform boundedness and uniform ultimate boundedness. In addition, we propose a quadratic cost index, which reflects the fuzzy system performance. We then formulate a control parameter design problem as a constrained optimization problem. It is proven that the global solution to this problem always exists and is unique. The closed-form solution and the closed-form minimum cost are derived.

An Energy-Efficient Train Control Framework for Smart Railway Transportation

Railway transportation systems are the backbone of smart cities. With the rapid increasing of railway mileage, the energy consumption of train becomes a major concern. The uniqueness of train operations is that the geographic characteristics of each route is known a priori. On the other hand, the parameters (e.g., loads) of a train varies from trip to trip. Such a specialty determines that an energy-optimal driving profile for each train operation has to be pursued by considering both the geographic information and the inherent train conditions. The solution of the optimization problem, however, is hard due to its high dimension, nonlinearity, complex constraints and time-varying characteristics of a control sequence. As a result, an energy-saving solution to the train control optimization problem has to address the dilemma of optimization quality and computing time. This work proposes an energy-efficient train control framework by integrating both offline and onboard optimization techniques. The offline processing builds a decision tree based sketchy solution through a complete flow of sequence mining, optimization and machine learning. The onboard system feeds the train parameters into the decision tree to derive an optimized control sequence. A key innovation of this work is the identification of optimal patterns of control sequence by data mining the driving behaviors of the experienced train drivers and then apply the patterns to online trip planning. The proposed framework efficiently find an optimized driving solution by leveraging the training results derived with a compute-intensive offline learning flow. The framework was already testified in a smart freight train system. It was demonstrated an average of

Toward Robust Vehicle Platooning With Bounded Spacing Error

9.84

An Adaptive Metamodel-Based Optimization Approach for Vehicle Suspension System Design

percent energy-saving can be achieved.

Novel Approach to Multibody System Modeling: Cascading and Clustering

Intelligent transportation has become an essential field of cyber-physical systems. Among various intelligent transportation technologies, the automated highway system (AHS) has its unique advantage of being able to coordinate a platoon of vehicles as a whole unit. The major challenge of building a robust AHS is the nonlinear and (potentially) fast time-varying uncertainty induced by parameter variations and external disturbances. Finally, reflected as the spacing between neighboring vehicles, such uncertainties can be a serious concern for maintaining safety. This paper addresses the problem by proposing a mathematical transformation scheme to bound the spacing error and build a distributed control algorithm on such a basis. The propose algorithm achieves a spacing error satisfying both uniformly boundedness and uniformly ultimate boundedness. Our decentralized algorithm is communication efficient in the sense that it only requires the state information of the preceding car and the acceleration feedback and does not need to communicate with all other cars.

Human experience knowledge induction based intelligent train driving

The performance index of a suspension system is a function of the maximum and minimum values over the parameter interval. Thus metamodel-based techniques can be used for designing suspension system hardpoints locations. In this study, an adaptive metamodel-based optimization approach is used to find the proper locations of the hardpoints, with the objectives considering the kinematic performance of the suspension. The adaptive optimization method helps to find the optimum locations of the hardpoints efficiently as it may be unachievable through manually adjusting. For each iteration in the process of adaptive optimization, prediction uncertainty is considered and the multiobjective optimization method is applied to optimize all the performance indexes simultaneously. It is shown that the proposed optimization method is effective while being applied in the kinematic performance optimization of a McPherson suspension system.

Optimal robust control for generalized fuzzy dynamical systems: A novel use on fuzzy uncertainties

AbstractA novel approach for discrete multibody system modeling is presented. The approach segments the multibody system into a series of subsystems with kinematic constraints. The equations of motion of the unconstrained subsystems are either easier to obtain or already exist. The Udwadia-Kalaba equation is then introduced to calculate the constraint forces due to the constraints. The approach does not require any auxiliary variables, such as Lagrange multipliers, and does not need to use any projection. It can systematically reach the model via clustering of the segments.

From offline to onboard system solution for a control sequence optimization problem

As the most sustainable means of modern transportation, the railway trains are eagerly approaching autonomous driving due to their congenital advantages on operating environments compare to, e.g., road traffics. The intelligent automatic train driving aims at train control with a goal of energy efficiency, punctuality and safety. The derivation of an optimized train driving solution by taking advantage of the undulating terrains along a route, however, proves to be a significant challenge due to the high dimension, nonlinearity, complex constraints, and time-varying characteristic of the problem. To tackle the problem, we propose a two-level human driving experience learning framework and employ the fuzzy rule induction method for online generation of the optimized driving solutions. Based on the records of experienced human drivers, a FURIA model was built to learn the driving rules indicating the correlation between the specified features to the decision of a driving sequence. The fuzzy rules can generally find the best-match driving operation under certain running circumstances. The learned model can be used to determine an optimized driving operation in real-time. Validation experiments show that the energy consumption of the proposed solution is around 8.93% lower than that of average human drivers.

Udwadia-Kalaba Approach for Parallel Manipulator Dynamics

A novel approach for optimal robust control of a class of generalized fuzzy dynamical systems is proposed. This is a novel use of fuzzy uncertainty in doing dynamical system control. The system may have nonlinear nominal terms and the other terms with uncertainty, including unknown parameters and input disturbances. The Fuzzy sets theory is creatively employed in presenting the system parameter and input uncertainty, and then the control structure is deterministic (versus if-then rule-based as is typical in Mamdani-type fuzzy control). The desired controlled system performance is also deterministic, with guaranteed performances of uniform boundedness and uniform ultimate boundedness. Fuzzy informations on the uncertainties are used in searching optimal control gain under a proposed LQG-like quadratic cost index. The control gain design problem is formulated as a constrained optimization problem with the solution be proved to be always existed and unique. Systematic procedure is summarized for such control design.

New Attempts on Vehicle Suspension Systems Modeling and Its Application on Dynamical Load Analysis

The control sequence optimization problem is difficult to solve due to its high nonlinearity, various constraints and the possible changes in the sequence of comprising elements at any instant of time. The optimization of train trip running profile is a typical control sequence optimization problem, whose optimization object is to minimize the energy consumption as well as the time deviation under various constraints. Engineers always have to face the trade-off between the optimization performance and calculation time for an onboard control system for such problems. This paper mainly proposed a framework of an offline to onboard system solution for control sequence optimization problems, specifically using on the train trip profile optimization problems. The framework choose the parameter-decision tree solution for the onboard control system, and then a series of offline procedures including sequence mining, optimal computation, and machine learning is proposed for getting the parameter-decision tree. The framework inherits the good optimization performance of offline systems, as well as guaranteed the onboard calculation time for real-time control. Performance on using such a framework for solving train trip profile optimization problems is shown in the literature, which shows the potentials of using such frameworks on solving related control sequence optimization problems.