Optimal comfortability control of hybrid electric powertrains in acceleration mode

This paper presents a control design approach for optimizing the comfortability of hybrid electric powertrains in acceleration mode.A parallel hybrid electric v...     

综合的访问控制策略及其应用研究

提出了一种综合的访问控制(HAC)策略,它继承和汲取了几种主流的访问控制技术的优点,有效地解决了信息安全领域的访问控制问题和有效地保护数据的保密性和完整性,保证授权主体能访问客体和拒绝非授权访问.HAC具有良好的灵活性、可维护性、可管理性、更细粒度的访问控制性和更高的安全性,给信息系统设计人员和开发人员提供了访问控制安全功能的解决方案.举例说明了HAC在信息安全中的应用,结果表明HAC是实用的、有效的.     

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split algorithm. The presented strategy is adaptive for vehicle mass and road elevation, and is implemented on a standard Electronic Control Unit of a parallel hybrid electric truck. The implemented strategy is experimentally validated on a chassis dynamo meter. The fuel consumption is measured on 12 different trajectories and compared with a heuristic and a non-hybrid strategy. The optimal control strategy has a fuel consumption lower (up to 3%) than the heuristic strategy on all trajectories that are evaluated, except one. Compared to the non-hybrid strategy the fuel consumption reduction ranged from 7% to 16%.     

Integrated modeling and analysis of dynamics for electric vehicle powertrains

Powertrain of an electric vehicle (EV) is a compound system with an electrical sub-system, such as batteries, inverters, and electrical motors, as well as a mechanical sub-system, including transmissions, differential, and wheels. Since the electrical systems directly affect the vehicle driving performance and dynamics of an EV, integrated modeling considering both the mechanical and electrical systems is essential to assess ultimate kinetic and dynamic characteristics of an EV in terms of input electrical quantities. In this paper, an entire analytic model for the powertrain of EVs is developed to describe EV dynamics with respect to electrical signals, in consideration of both mechanical and electrical systems. Theoretical models based on mathematical expressions, combining the mechanical power system and the electrical power systems, are derived for predicting the final vehicle driving performance as a function of electrical quantities. In addition, a Matlab model of an EV is developed to verify the derived mathematical analysis model. Based on the theoretical model of the powertrain, a variety of relationships between electrical quantities and vehicle dynamics, such as velocity, acceleration, and forces of the EVs, are finally investigated and analyzed.     

Fuel efficient power management strategy for fuel cell hybrid powertrains

A real time control strategy for fuel cell hybrid vehicles is proposed. The objective is to reduce the hydrogen consumption by using an efficient power sharing strategy between the fuel cell system (FCS) and the energy buffer (EB). The energy buffer (battery or supercapacitor) is charge-sustained (no plug-in capabilities). The real time control strategy is derived from a non-causal optimization algorithm based on optimal control theory. The strategy is validated experimentally with a hardware-in-the-loop (HiL) test bench based on a 600 W fuel cell system.     

The optimal robust control policy for uncertain semi-Markov control processes

The optimization problems of Markov control processes (MCPs) with exact knowledge of system parameters, in the form of transition probabilities or infinitesimal transition rates, can be solved by using the concept of Markov performance potential which plays an important role in the sensitivity analysis of MCPs. In this paper, by using an equivalent infinitesimal generator, we first introduce a definition of discounted Poisson equations for semi-Markov control processes (SMCPs), which is similar to that for MCPs, and the performance potentials of SMCPs are defined as solution of the equation. Some related optimization techniques based on performance potentials for MCPs may be extended to the optimization of SMCPs if the system parameters are known with certainty. Unfortunately, exact values of the distributions of the sojourn times at some states or the transition probabilities of the embedded Markov chain for a large-scale SMCP are generally difficult or impossible to obtain, which leads to the uncertainty of the semi-Markov kernel, and thereby to the uncertainty of equivalent infinitesimal transition rates. Similar to the optimization of uncertain MCPs, a potential-based policy iteration method is proposed in this work to search for the optimal robust control policy for SMCPs with uncertain infinitesimal transition rates that are represented as compact sets. In addition, convergence of the algorithm is discussed.     

Dynamic state feedback and its application to linear optimal control

We investigate the linear optimal control problems in the context of dynamic state feedback configuration. The dynamic state feedback is a dual structure of the dynamic observer. In conjunction with the well known arguments on linear matrix differential and Lyapunov equations, we elicit the fact that the quadratic performance index is always computable with this configuration. Based on this property, we suggest a nonlinear optimization programming method to get suboptimal or near optimal time-invariant dynamic state feedback controls. One can also evaluate the efficacy of pre-designed dynamic state feedback controllers utilizing this property. Two illustrative examples are given to show the effectiveness of the proposed approach.     

Simulation and control of hybrid electric vehicles

This research develops a typical model for a parallel hybrid electric vehicle. Model predictive controllers and simulations for this model have been built to verify the ability of the system to control the speeds and to handle the transitional period for the clutch engagement from the pure electrical driving to the hybrid driving. If the output constraints are the measured speeds and the unmeasured torques which are not strictly imposed and can be violated somewhat during the clutch engagements, a modified model predictive controller with soften output constraints has been developed. Simulations show that the new model predictive controller can control the speeds very well for rapid clutch engagements, which enhance the driving comfort and reduce the jerk on the parallel hybrid electric vehicles.     

Logic-based solution methods for optimal control of hybrid systems

Combinatorial optimization over continuous and integer variables is a useful tool for solving complex optimal control problems of hybrid dynamical systems formulated in discrete-time. Current approaches are based on mixed-integer linear (or quadratic) programming (MIP), which provides the solution after solving a sequence of relaxed linear (or quadratic) programs. MIP formulations require the translation of the discrete/logic part of the hybrid problem into mixed-integer inequalities. Although this operation can be done automatically, most of the original symbolic structure of the problem (e.g., transition functions of finite state machines, logic constraints, symbolic variables, etc.) is lost during the conversion, with a consequent loss of computational performance. In this paper, we attempt to overcome such a difficulty by combining numerical techniques for solving convex programming problems with symbolic techniques for solving constraint satisfaction problems (CSP). The resulting "hybrid" solver proposed here takes advantage of CSP solvers for dealing with satisfiability of logic constraints very efficiently. We propose a suitable model of the hybrid dynamics and a class of optimal control problems that embrace both symbolic and continuous variables/functions, and that are tailored to the use of the new hybrid solver. The superiority in terms of computational performance with respect to commercial MIP solvers is shown on a centralized supply chain management problem with uncertain forecast demand.     

Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

The paper describes a chain of tools aimed at the development and validation of energy management strategies (EMS) for hybrid powertrains. These tools comprise an offline optimizer based on Pontryagin minimum principle (PMP) and the online equivalent consumption minimization strategy (ECMS), both implemented in a dynamic simulation platform and as a real-time controller in a semi-physical testing equipment. The results presented are aimed at illustrating the continuity of the various approaches by comparing the offline-generated energy management laws with their online counterparts, both in terms of trajectories over time and in terms of global results (fuel consumption, state-of-charge deviations).     

Reasoning with advanced policy rules and its application to access control

This paper presents a formal framework to represent and manage advanced policy rules, which incorporate the notions of provision and obligation. Provisions are those conditions that need to be satisfied or actions that must be performed by a user or an agent before a decision is rendered, while obligations are those conditions or actions that must be fulfilled by either the user or agent or by the system itself within a certain period of time after the decision. This paper proposes a specific formalism to express provisions and obligations within a policy and investigates a reasoning mechanism within this framework. A policy decision may be supported by more than one rule-based derivation, each associated with a potentially different set of provisions and obligations (called a global PO set). The reasoning mechanism can derive all the global PO sets for each specific policy decision and facilitates the selection of the best one based on numerical weights assigned to provisions and obligations as well as on semantic relationships among them. The formal results presented in the paper hold for many applications requiring the specification of policies, but this paper illustrates the use of the proposed policy framework in the security domain only.     

A unified framework for hybrid control: model and optimal controltheory

We propose a very general framework that systematizes the notion of a hybrid system, combining differential equations and automata, governed by a hybrid controller that issues continuous-variable commands and makes logical decisions. We first identify the phenomena that arise in real-world hybrid systems. Then, we introduce a mathematical model of hybrid systems as interacting collections of dynamical systems, evolving on continuous-variable state spaces and subject to continuous controls and discrete transitions. The model captures the identified phenomena, subsumes previous models, yet retains enough structure to pose and solve meaningful control problems. We develop a theory for synthesizing hybrid controllers for hybrid plants in all optimal control framework. In particular, we demonstrate the existence of optimal (relaxed) and near-optimal (precise) controls and derive “generalized quasi-variational inequalities” that the associated value function satisfies. We summarize algorithms for solving these inequalities based on a generalized Bellman equation, impulse control, and linear programming     

A benchmark for optimal control problem solvers for hybrid nonlinear systems

In this paper, a benchmark problem is proposed in order to assess comparisons between different optimal control problem solvers for hybrid nonlinear systems. The model is nonlinear with 20 states, 4 continuous controls, 1 discrete binary control and 4 configurations. Transitions between configurations lead to state jumps. The system is inspired by the simulated moving bed, a counter-current separation process.     

Near optimal control for a class of stochastic hybrid systems

To address a computationally intractable optimal control problem for a class of stochastic hybrid systems, this paper proposes a near optimal state feedback control scheme, which is constructed by using a statistical prediction method based on approximate numerical solution that samples over the entire state space. A numerical example illustrates the potential of the approach.     

Explicit,optimal stability functionals and their application to cyclic discretization methods

The present paper contains a stability concept for discretization methods of a certain, very general classM, which is optimal (in the sense of yielding the best general, two-sided error bounds) without being more restrictive than any of the classical stability definitions. The optimal stability functional Ψ_h related to it depends on the linear part of the discretization operator, and has the important property that Ψ_h [δ] may be of orderq+1, i.e. Ψ_h [δ] = O(h ^q+1), even if the local error δ only has orderq, δ = O(h ^q). This result may be used for the construction of methods with maximum order. Its application to linear cyclic methods, for example, furnishes a new approach to the theory of linearM-cyclick-step methods of maximum order.     

Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles

Hybrid Electric Vehicles (HEVs) generate the power required to drive the vehicle via a combination of internal combustion engines and electric generators. To make HEVs as efficient as possible, proper management of the different energy elements is essential. This task is performed using the HEV control strategy. The HEV control strategy is the algorithm according to which energy is produced, used and saved. This paper describes a genetic-fuzzy control strategy for parallel HEVs. The genetic-fuzzy control strategy is a fuzzy logic controller that is tuned by a genetic algorithm. The objective is to minimize fuel consumption and emissions, while enhancing or maintaining the driving performance characteristics of the vehicle. The tuning process is performed over three different driving cycles including NEDC, FTP and TEH-CAR. Results from the computer simulation demonstrate the effectiveness of this approach in reducing fuel consumption and emissions without sacrificing vehicle performance.     

Dynamic optimal control policy in advertising price and quality

This study presents an algorithm for deriving the long-term polices of quality level, price and advertisement for a product. The diffusion models and cost functions are combined to formulate profit functions capable of determining future profit trends. The algorithm first implements the optimal control theory to derive the optimal conditions of the profit function. Then the genetic algorithm is employed to search for the approximate solutions of quality level, price and advertising expenditure at each period on the planning horizon (life cycle). Examples of different scenarios of the model parameters are presented to describe the results obtained herein. Sensitivity analysis for the major parameters is performed to specify their effects on profits. Results in this study allow us, firstly, to obtain explicit solutions simultaneously with respect to quality, price and advertising policies, secondly, to propose an appropriate algorithm for solving the different scenarios of the dynamic profit function, which consists of the diffusion function and cost function, and thirdly, to enhance the long-term profit performance via the polices proposed herein, that is the approximation of the best solution.