Stability analysis for multi-agent systems using the incidence matrix: Quantized communication and formation control

The spectral properties of the incidence matrix of the communication graph are exploited to provide solutions to two multi-agent control problems. In particular, we consider the problem of state agreement with quantized communication and the problem of distance-based formation control. In both cases, stabilizing control laws are provided when the communication graph is a tree. It is shown how the relation between tree graphs and the null space of the corresponding incidence matrix encode fundamental properties for these two multi-agent control problems.     

Multi-level hierarchical interface-based supervisory control

Hierarchical Interface-Based Supervisory Control employs interfaces that allow properties of a monolithic system to be verified through local analysis. By avoiding the need to verify properties globally, significant computational savings can be achieved. In this paper we provide local requirements for a multi-level architecture employing command-pair type interfaces. This multi-level architecture allows for a greater reduction in complexity and improved reconfigurability over the two-level case that has been previously studied since it allows the global system to be partitioned into smaller modules. This paper also provides results for synthesizing supervisors in the multi-level architecture that are locally maximally permissive with respect to a given specification and set of interfaces.     

矿井主通风机自动监控系统的设计

为了保障主通风机安全、可靠、经济地运行,提出了一种基于S7-300 PLC的矿井主通风机自动监控系统的设计方案;给出了该系统的结构,详细介绍了该系统中PLC的硬件及软件结构、模拟量的检测实现及采用智能型PID调节器XMA5000对风机负压和流量进行自动调节的方法。实际应用表明,该系统能够实现通风机的就地、集中、远方3种控制方式,提高了主通风机的自动化程度和运行可靠性。     

Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p.     

Suppressing intersample behavior in iterative learning control

Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.     

A systematic approach to improve multiple Lyapunov function stability and stabilization conditions for fuzzy systems

This paper presents a systematic approach for decreasing conservativeness in stability analysis and control design for Takagi-Sugeno (TS) systems. This approach is based on the idea of multiple Lyapunov functions together with simple techniques for introducing slack matrices. Unlike some previous approaches based on multiple Lyapunov functions, both the stability and the stabilization conditions are written as linear matrix inequality (LMI) problems. The proposed approach reduces the number of inequalities and guarantees extra degrees of freedom to the LMI problems. Numeric examples illustrate the effectiveness of this method.     

Distributed control of production systems

This editorial introduces the special issue of the Elsevier journal, Engineering Application of Artificial Intelligence, on Distributed control of production systems. The current technology in communication and embedded systems allows products and production resources to play a more active role in the production process. This new active capacity will generate major changes in organizations and information systems (e.g., Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES)). New approaches are now required for modelling, testing and assessing the features made possible by the decisional and informational capabilities of these new active entities. One among the many possibilities is to use agents and holons, since agent and holon-based approaches assume interaction between intelligent entities to facilitate the emergence of a global behavior. This special issue thus focuses on the possible applications of distributed approaches for the design, evaluation and implementation of new control architectures for production systems. Both fundamental and applied research papers are presented.     

A general formulation of monotonically convergent algorithms in the control of quantum dynamics beyond the linear dipole interaction

This paper presents a general method to formulate monotonically convergent algorithms for identifying optimal control fields to manipulate quantum dynamics phenomena beyond the linear dipole interaction. The method, facilitated by a field-dependent dipole moment operator, is based on an integral equation of the first kind arising from the Heisenberg equation of motion for tracking a time-dependent, dynamical invariant observable associated with a reference control field.     

Finite-time weighted average consensus with respect to a monotonic function and its application

The consensus state is an important and fundamental quantity for consensus problems of multi-agent systems, which indicates where all the dynamical agents reach. In this paper, weighted average consensus with respect to a monotonic function, which means that the trajectories of the monotonic function along the state of each agent reach the weighted average of their initial values, is studied for a group of kinematic agents with time-varying topology. By constructing a continuous nonlinear distributed protocol, such a consensus problem can be solved in finite time even though the time-varying topology involves unconnected graphs. Then the distributed protocol is employed to compute the maximum-likelihood estimation of unknown parameters over sensor networks. Compared with the existing results, the estimate scheme proposed here may reduce the costs of data communication, storage memory, book-keeping and computational overheads.     

Existence of optimal controls for systems driven by FBSDEs

We prove the existence of optimal relaxed controls as well as strict optimal controls for systems governed by non linear forward-backward stochastic differential equations (FBSDEs). Our approach is based on weak convergence techniques for the associated FBSDEs in the Jakubowski S-topology and a suitable Skorokhod representation theorem.