Max-plus-linear model-based predictive control for constrained hybrid systems: linear programming solution

In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the (max, +) algebra, a typical subclass of hybrid systems called max-plus-linear (MPL) systems is obtained. And then, model predictive control (MPC) framework is extended to MPL systems. In general, the nonlinear optimization approach or extended linear complementarity problem (ELCP) were applied to solve the MPL-MPC optimization problem. A new optimization method based on canonical forms for max-min-plus-scaling (MMPS) functions (using the operations maximization, minimization, addition and scalar multiplication) with linear constraints on the inputs is presented. The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization. The validity of the algorithm is illustrated by an example.     

Max-plus-linear model-based predictive control for constrained hybrid systems：linear programming solution

In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the （max, ＋） algebra, a typical subclass of hybrid systems called max-plus-linear （MPL） systems is obtained. And then, model predictive control （MPC） framework is extended to MPL systems. In general, the nonlinear optimization approach or extended linear complementarity problem （ELCP） were applied to solve the MPL-MPC optimization problem. A new optimization method based on canonical forms for max-min-plus-scaling （MMPS） functions （using the operations maximization, minimization, addition and scalar multiplication） with linear constraints on the inputs is presented. The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization. The validity of the algorithm is illustrated by an example.     

Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering

Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequence approach with an enlarged pheromone-particle table, and (4) global best exchange. These hybrid systems were applied to data clustering. The experimental results utilizing public UCI datasets show that the performances of the proposed hybrid systems are superior compared to those of the K-mean, standalone PSO, and standalone ACOR. Among the four strategies of hybridization, the sequence approach with the enlarged pheromone table is superior to the other approaches because the enlarged pheromone table diversifies the generation of new solutions of ACOR and PSO, which prevents traps into the local optimum.     

A discrete-time approach to impulse-based adaptive input shaping for motion control without residual vibration

Modifying a command or actuation signal by convolving it with a sequence of impulses is a useful technique for eliminating structural vibration in rest-to-rest motion of mechanical systems. This paper describes an adaptive discrete-time version of this approach where amplitude and timing of impulses are tuned during operation to match the system under control. Solutions giving zero residual vibration are formulated in terms of a quadratic cost function and constructed by iterative operations on measured sets of input–output data. The versatility of the approach is demonstrated by simulated test cases involving (1) amplitude optimization of impulses with fixed timings, (2) timing optimization of impulses with fixed amplitudes and (3) combined timing and amplitude optimization. The approach is model-free and directly applicable to multi-mode systems. Moreover, fast adaptation within a single rest-to-rest maneuver can be achieved.     

Performance assessment of decentralized control systems: An iterative approach

In this work, an efficient approach for performance assessment of decentralized control systems based on a general quadratic performance index involving both system states and inputs is proposed. The performance assessment problem is formulated as an optimization problem subject to constraints in the form of linear/bilinear matrix inequalities which explicitly take the block-diagonal structural constraint on decentralized control systems into account. In order to solve the optimization problem efficiently, an iterative approach based on the original optimization problem and an equivalent transformation of the original one is proposed. Specifically, the proposed approach under the assumption that the full state feedback is available is first presented; and then the approach is extended to the case that only output feedback is available. The proposed approach solves for both the best achievable performance and the corresponding controller (and observer) gains. The application of the proposed approach to two examples including a reactor–separator chemical process example illustrates the applicability and effectiveness of the proposed approach.     

基于NSGA-II的扩展置信规则库激活规则多目标优化方法

针对扩展置信规则库（extended belief rule base,EBRB）系统在不一致的激活规则过多时推理准确性不高的问题,引入带精英策略的快速非支配排序遗传算法（NSGA-Ⅱ）,提出一种基于NSGA-Ⅱ的激活规则多目标优化方法。该方法首先将激活权重大于零的规则（即激活规则）进行二进制编码,把最终参与合成推理的激活规则集合的不一致性以及激活权重和作为多目标优化问题的目标函数,通过带精英策略的快速非支配排序遗传算法求解不一致性更小的激活规则集合,从而降低不一致激活规则对于EBRB系统推理准确性的影响。为了验证本文方法的有效性和可行性,引入非线性函数和输油管道检漏实例进行测试。实验结果表明,基于NSGA-Ⅱ的扩展置信规则库激活规则多目标优化方法能够有效提高EBRB系统的推理能力。     

Discussion on the optimization problem formulation of flexible components in multibody systems

This paper is dedicated to the structural optimization of flexible components in mechanical systems modeled as multibody systems. While most of the structural optimization developments have been conducted under (quasi-)static loadings or vibration design criteria, the proposed approach aims at considering as precisely as possible the effects of dynamic loading under service conditions. Solving this problem is quite challenging and naive implementations may lead to inaccurate and unstable results. To elaborate a robust and reliable approach, the optimization problem formulation is investigated because it turns out that it is a critical point. Different optimization algorithms are also tested. To explain the efficiency of the various solution approaches, the complex nature of the design space is analyzed. Numerical applications considering the optimization of a two-arm robot subject to a trajectory tracking constraint and the optimization of a slider-crank mechanism with a cyclic dynamic loading are presented to illustrate the different concepts.     

The potential structure of sample paths and performance sensitivities of Markov systems

We study the structure of sample paths of Markov systems by using performance potentials as the fundamental units. With a sample path-based approach, we show that performance sensitivity formulas (performance gradients and performance differences) of Markov systems can be constructed intuitively, by first principles, with performance potentials (or equivalently, perturbation realization factors) as building blocks. In particular, we derive sensitivity formulas for two Markov chains with possibly different state spaces. The proposed approach can be used to obtain flexibly the sensitivity formulas for a wide range of problems, including those with partial information. These formulas are the basis for performance optimization of discrete event dynamic systems, including perturbation analysis, Markov decision processes, and reinforcement learning. The approach thus provides insight on on-line learning and performance optimization and opens up new research directions. Sample path based algorithms can be developed.     

Dynamic output feedback robust MPC for LPV systems subject to input saturation and bounded disturbance

For linear parameter varying (LPV) systems with unknown scheduling parameters and bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) with input saturation is investigated. By pre-specifying partial controller parameters, a main optimization problem is solved by convex optimization to reduce the on-line computational burden. The main optimization problem guarantees that the estimated state and estimation error converge within the corresponding invariant sets such that recursive feasibility and robust stability are guaranteed. The consideration of input saturation in the main optimization problem improves the control performance. Two numerical examples are given to illustrate the effectiveness of the approach.     

基于可视化计算的图形建模及参数寻优环境的设计与实现

研究了复杂非线性系统参数优化环境的可视化建模技术及软件实现问题．应用先进的仿真技术,采用面向对象和结构化设计方法,通过通用软件实现了复杂系统参数自动寻优环境的高度可视化的一体化;与基于常规高级和谐设计语言的参数寻优方法相比,该方法不仅不用传统程序代码对算法编程,而且可方便地对系统进行多参数自动迭代寻优试验及进行智能化分析．     

Optimization of sequences in CDMA systems: A statistical–mechanics approach

A statistical–mechanics approach is useful not only in analyzing the macroscopic system performance of wireless communication systems, but also in discussing design problems of wireless communication systems. We discuss a design problem of spreading sequences in code-division multiple-access (CDMA) systems, as an example demonstrating the usefulness of the statistical–mechanics approach. We analyze, via the replica method, the average mutual information between inputs and outputs of a randomly spread CDMA channel, and discuss the optimization problem with the average mutual information as the measure of optimization. It has been shown that the average mutual information is maximized by orthogonally-invariant random Welch bound equality (WBE) spreading sequences. This finding is an extension of the optimality of WBE spreading sequences with Gaussian inputs for non-Gaussian inputs.     

Design Optimization of Multibody Systems by Sequential Approximation

Design optimization of multibody systems is usually established by a direct coupling of multibody system analysis and mathematical programming algorithms. However, a direct coupling is hindered by the transient and computationally complex behavior of many multibody systems. In structural optimization often approximation concepts are used instead to interface numerical analysis and optimization. This paper shows that such an approach is valuable for the optimization of multibody systems as well. A design optimization tool has been developed for multibody systems that generates a sequence of approximate optimization problems. The approach is illustrated by three examples: an impact absorber, a slider-crank mechanism, and a stress-constrained four-bar mechanism. Furthermore, the consequences for an accurate and efficient accompanying design sensitivity analysis are discussed.     

基于时间性能的对象知识网结构优化研究

为了优化企业信息系统的体系结构, 基于时间性能研究了企业信息系统的形式化表达——对象知识网结构的优化方法。通过对象知识网的知识点间关系的表达, 对不同类型知识点结构的时间性能进行了讨论, 提出了基于二叉树的知识点表达式的表示方法, 解决了优化算法的编码问题, 采用基于改进的免疫遗传算法, 对对象知识网的结构进行优化, 并通过算例对提出的优化方法进行说明和验证, 获得了优化的对象知识网体系结构, 同时为基于时间性能的企业信息系统结构优化奠定了理论基础。     

基于不确定理论的复杂体系优化方法研究

为研究复杂体系的优化方法,首先针对复杂体系的三个发展阶段,提出了三类优化问题;其次通过研究体系结构、体系能力和体系效能之间的内在关系,给出了体系优化的思路,建立了以体系效能为优化目标的复杂体系优化模型;接着利用模糊集理论与证据理论讨论了体系能力评估中定性信息的定量化问题,探索了新的体系能力评估方法,并基于体系效能仿真,采用粗糙集理论建立了求解体系能力权值因子的算法;最后给出了求解优化模型的方法.所提出的优化方法为复杂体系优化问题提出了一种新思路.     

Revisiting state space exploration of timed coloured petri net models to optimize manufacturing system’s performance

Due to constant fluctuations in market demands, nowadays scheduling of flexible manufacturing systems is taking great importance to improve competitiveness. Coloured Petri Nets (CPN) is a high level modelling formalism which have been widely used to model and verify systems, allowing representing not only the system’s dynamic behaviour but also the information flow. One approach that focuses in performance optimization of industrial systems is the one that uses the CPN formalism extended with time features (Timed Coloured Petri Nets) and explores all the possible states of the model (state space) looking for states of particular interest under industrial scope. Unfortunately, using the time extension, the state space becomes awkward for most industrial problems, reason why there is a recognized need of approaches that could tackle optimization problems such as the scheduling of manufacturing activities without simplifying any important aspect of the real system. In this paper a timed state space approach for properties verification and systems optimization is presented together with new algorithms in order to get better results when time is used as a cost function for optimizing the makespan of manufacturing systems. A benchmarking example of a job-shop is modelled in CPN formalism to illustrate the improvements that can be achieved with the proposed implementations.     

Semantic query optimization for query plans of heterogeneousmultidatabase systems

New applications of information systems need to integrate a large number of heterogeneous databases over computer networks. Answering a query in these applications usually involves selecting relevant information sources and generating a query plan to combine the data automatically. As significant progress has been made in source selection and plan generation, the critical issue has been shifting to query optimization. This paper presents a semantic query optimization (SQO) approach to optimizing query plans of heterogeneous multidatabase systems. This approach provides global optimization for query plans as well as local optimization for subqueries that retrieve data from individual database sources. An important feature of our local optimization algorithm is that we prove necessary and sufficient conditions to eliminate an unnecessary join in a conjunctive query of arbitrary join topology. This feature allows our optimizer to utilize more expressive relational rules to provide a wider range of possible optimizations than previous work in SQO. The local optimization algorithm also features a new data structure called AND-OR implication graphs to facilitate the search for optimal queries. These features allow the global optimization to effectively use semantic knowledge to reduce the data transmission cost. We have implemented this approach in the PESTO (Plan Enhancement by SemanTic Optimization) query plan optimizer as a part of the SIMS information mediator. Experimental results demonstrate that PESTO can provide significant savings in query execution cost over query plan execution without optimization     

Manufacturing systems: A knowledge- and optimization-based approach

Optimization approaches have traditionally been viewed as tools for solving manufacturing problems. The optimization approach is not suitable for many problems arising in modern manufacturing systems due to their complexity and involvement of qualitative factors. Expert systems appear to remedy the latter weakness of optimization approaches. The biggest disadvantage of expert systems in manufacturing environment is the slow response time. In this paper an integrand approach involving knowledge-based and optimization approaches is explored. The knowledge-and optimization-based approach is applied to solve two manufacturing problems: group technology (static problem) and scheduling (dynamic problem). The approach presented is illustrated with numerical example and computational results.The original version of this paper was presented at the 2nd International Symposium on Robotics and Manufacturing (ISRAM), Albuquerque, New Mexico 16–18 November 1988. The published proceedings of this meeting may be ordered from: CAD Laboratory for Systems/Robotics, EECE Dept, UNM, Albuquerque, NM 87131, U.S.A.     

基于多媒体业务的无线网络跨层优化与仿真

由于无线信道的时变衰落特性以及不同用户对服务质量(QoS)的动态要求,传统的无线网络协议栈分层设计思想难以满足未来移动通信系统的需求.提出一种基于多用户公平性准则的跨层优化设计方法以解决该问题.基于多用户无线传输环境下的多媒体业务特性,联合物理层,数据链路层和应用层进行参数提取和优化设计,从应用层角度出发,提出了该跨层设计准则.在保证用户间的公平性同时最大化各用户观察到的视频流质量,充分有效地利用了无线资源.仿真结果表明跨层优化准则能同时很好地改善用户间的公平性和系统整体性能.     

Synchronization of decentralized multiple-model systems by market-based optimization.

Market-based optimization is a new optimization method for large decentralized systems where the distributed resource allocation of an economic system is adopted. Market-based algorithms can be interpreted as multi-agent scenarios where producer and consumer agents both compete and cooperate on a market of specified commodities. The market-based approach is applied to the synchronization of a set of local multiple-model systems. The method is extended to the case where each of the subsystems is represented by a Takagi-Sugeno (TS) fuzzy system. Although all local systems are provided with the same control input, the behaviors of the local systems are, in general, different because of different parameters in the subsystems. The task of the market-based optimization is to find an appropriate composition of subsystems so that all local systems exhibit a similar dynamical behavior. Examples show that even systems with potentially unstable local systems can be synchronized if there exists a stable combination of weighted subsystems.     

一种新的FMS混合调度算法

在综合一种新的Petri网模型以及另一种新的混合优化策略的基础上,提出了一种具有全局优化特征的PMS调度算法,最后,仿真结果验证了该算法的有效性以及良好的研究与应用前景。