Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.     

Event-triggered control design of linear networked systems with quantizations

This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.     

H∞ observer-based event-triggered sliding mode control for a class of discrete-time nonlinear networked systems with quantizations

This paper investigates the problem of H_∞ observer-based event-triggered sliding mode control (SMC) for a class of uncertain discrete-time Lipschitz nonlinear networked systems with quantizations occurring in both input and output channels. The event-triggered strategy is used to save the limited network bandwidth. Then, based on the zero-order-hold (ZOH) measurement, a state observer is designed to reconstruct the system state, which facilitates the design of the discrete-time sliding surface. Considering the effects of quantizations, networked-induced constraints and event-triggered scheme, the nonlinear state error dynamics and sliding mode dynamics are converted into a unified linear parameter varying (LPV) time-delay system with the aid of a reformulated Lipschitz property. By using the Lyapunov-Krasovskii functional and free weighting matrix, a new sufficient condition is derived to guarantee the robust asymptotic stability of the resulting closed-loop system with prescribed H_∞ performance. And then the observer gain, event-triggering parameter and sliding mode parameter are co-designed. Furthermore, a novel SMC law is synthesized to force the trajectories of the observer system onto a pre-specified sliding mode region in a finite time. Finally, a single-link flexible joint robot example is utilized to demonstrate the effectiveness of the proposed method.     

Predictive cloud control for multiagent systems with stochastic event-triggered schedule

This paper addresses a predictive cloud control problem for a linear multiagent system with random network delays and noises. To reduce communication cost, a stochastic event-triggered schedule is introduced to decide whether current measurements need to be transmitted. An optimal state estimation algorithm is designed to compensate random network delays in the feedback channel. Subsequently, a predictive cloud control scheme is proposed for the multiagent system to achieve both stability and consensus. Simultaneously, random network delays in the forward channel is compensated actively. Sufficient and necessary conditions of stability and consensus for the closed-loop multiagent system are derived. Finally, a numerical example is provided to verify correctness and effectiveness of the proposed methods.     

Event-triggered consensus control of disturbed multi-agent systems using output feedback

In this article, the event-triggered consensus control is investigated for general linear multi-agent systems with external disturbances, by using the accessible measurement outputs. In particular, a novel protocol is proposed using the local observed state variables at event-triggered instants, which are respectively derived by adopting an observer to each agent based on output signal. Then it is proved that under the designed event-triggered control protocol consensus can be achieved with the desired disturbance attenuation ability and no Zeno behavior occurs. A numerical simulation is given to demonstrate the effectiveness of the developed control strategy     

Observer-based reliable stabilization of uncertain linear systems subject to actuator faults,saturation, and bounded system disturbances

A matrix inequality approach is proposed to reliably stabilize a class of uncertain linear systems subject to actuator faults, saturation, and bounded system disturbances. The system states are assumed immeasurable, and a classical observer is incorporated for observation to enable state-based feedback control. Both the stability and stabilization of the closed-loop system are discussed and the closed-loop domain of attraction is estimated by an ellipsoidal invariant set. The resultant stabilization conditions in the form of matrix inequalities enable simultaneous optimization of both the observer gain and the feedback controller gain, which is realized by converting the non-convex optimization problem to an unconstrained nonlinear programming problem. The effectiveness of proposed design techniques is demonstrated through a linearized model of F-18 HARV around an operating point.     

Bidding-based multi-agent system for integrated process planning and scheduling: a data-mining and hybrid tabu-SA algorithm-oriented approach

This paper conceptualizes a bidding-based multi-agent system for solving integrated process-planning and scheduling problem. The proposed architecture consists of various autonomous agents capable of communicating (bidding) with each other and making decisions based on their knowledge. Moreover, in contrast to the traditional model of integrated process-planning and scheduling problem, a new paradigm has been conceptualized by considering tool cost as a dynamic quantity rather than a constant. Tool cost is assumed to comprise tool-using cost and its repairing cost. The repairing cost is considered to depend on the tool-breaking probability, which is predicted by the data-mining agent equipped with the virtues of C-fuzzy decision tree. When a job arrives at the shop floor, the component agent announces a bid for one feature at a time to all the machine agents. Among the machine agents capable of producing the first feature, one comes forward to become a “leader”, and groups other machine agents for the processing of remaining features of the job. Once all features are assigned to the appropriate machines, the leader then sends this allocation information to the optimization agent. The optimization agent finds optimal/near-optimal process plans and schedules via the hybrid tabu-SA algorithm.     

用于非线性加热炉的神经网络预测控制器

将预测控制的优化思想与神经网络精确描述非线性和不确定性动态过程的特性有机结合,提出了可用于非线性加热炉的直接优化的神经网络预测控制方法,该方法是采用离线训练的神经网络,通过在线反馈校正,分段优化控制量,可使最优预测输出逼近参考轨迹;基于李雅普诺夫方法,讨论了闭环系统的稳定性,得到闭环系统局部渐进稳定的一个充分条件;讨论了加权系数h和λ对系统的影响;仿真结果表明了该方法的有效性、准确性和鲁棒性。     

Distributed optimal coordination control for nonlinear multi-agent systems using event-triggered adaptive dynamic programming method

This paper is concerned with the design of distributed optimal coordination control for nonlinear multi-agent systems (NMASs) based on event-triggered adaptive dynamic programming (ETADP) method. The method is firstly introduced to design the distributed coordination controllers for NMASs, which not only avoids the transmission of redundant data compared with traditional time-triggered adaptive dynamic programming (TTADP) strategy and minimizes the performance function of each agent. The event-triggered conditions are proposed based on Lyapunov functional method, which is deduced by guaranteeing the stability of NMASs. Then a new adaptive policy iteration algorithm is presented to obtain the online solutions of the Hamiton–Jocabi–Bellman (HJB) equations. In order to implement the proposed ETADP method, the fuzzy hyperbolic model based critic neural networks (NN) are utilized to approximate the value functions and help calculate the control policies. In critic NNs, the NN weight estimations are updated at the event-triggered instants leading to aperiodic weight tuning laws so that computation cost is reduced. It is proved that the weight estimation errors and the local neighborhood coordination errors is uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to show the effectiveness of the proposed ETADP method.     

A new approach to event-triggered static output feedback control of networked control systems

This paper is concerned with the event-triggered static output feedback control of networked control systems. The event-triggered mechanism is represented by a time-delay model and some latest techniques are employed to deal with the induced time-delay. Furthermore, a novel strategy is developed to eliminate the coupling among control gain, input matrix and output matrix. With these techniques, a new sufficient condition for system stability is established in the framework of linear matrix inequalities. The effectiveness of the proposed method is shown by two numerical examples.     

Event-triggered reliable control for fuzzy Markovian jump systems with mismatched membership functions

The problem of event-triggered reliable control for fuzzy Markovian jump system (FMJS) with mismatched membership functions (MMFs) is addressed. Based on the mode-dependent reliable control and event-triggered communication scheme, the stability conditions and control design procedure are formulated. More precisely, a general actuator-failure is designed such that the FMJS is reliable in the sense of stochastically stable and reduce the utilization of network resources. Furthermore, the improved MMFs are introduced to reduce the conservativeness of obtained results. Finally, simulation results indicate the effectiveness of the proposed methodology.     

Formation-containment control of networked Euler–Lagrange systems: An event-triggered framework

To improve the concurrency of leaders’ formation and followers’ containment, a difficult problem of designing the formation controller and the containment controller simultaneously should be addressed for networked systems. Motivated by this, this paper presents an even-triggered control framework for networked Euler–Lagrange systems to achieve formation-containment control even in the presence of uncertain parameters. An event-triggered formation controller is firstly designed for leaders to achieve the desired configuration. An event-triggered containment control law is then developed to guarantee that all the followers can converge to the convex hull formed by leaders. The key feature of the containment control law is that it does not necessitate any relative velocity information with respect to neighbor followers. Each controller’s gains are adaptively tuned using only local information. The parametric uncertainties are accommodated by using the adaptive updating law. Zeno behaviors of the triggering time sequences are also excluded. As a result, the communication burden of formation-containment system can be reduced. Numerical simulation is finally presented to verify the effectiveness of the proposed event-triggered formation-containment control framework.     

Optimizing parameters of complexly loaded friction bearings

Crankshaft rod bearings of a combustion engine are used as an example to show the technique and results of optimization of the design parameters of friction bearing under a complex load. The field of hydrodynamic pressures in a lubricating layer, which separates the crank pin and the bearing lining, is determined by integrating the equation for filling of the gap. This approach ensures observance of the condition of lubricant continuity at the boundaries of discontinuity and restoration of the lubricating layer. The lubricant viscosity, which is assumed to be an effective function of the temperature of the lubricating layer, is adjusted at each step of the calculation of the crank pin’s motion trajectory.     

Group shop scheduling with uncertain data and a general cost objective

In this paper, a stochastic group shop scheduling problem with a due date-related objective is studied. The group shop scheduling problem provides a general formulation including two other shop scheduling problems, the job shop and the open shop. Both job release dates and processing times are assumed to be random variables with known distributions. Moreover, earliness and tardiness of jobs are penalized at different rates. The objective is to minimize the expected maximum completion cost among all jobs. A lower bound on the objective function is proposed, and then, a hybrid approach following a simulation optimization procedure is developed to deal with the problem. An ant colony optimization algorithm is employed to construct good feasible solutions, while a discrete-event simulation model is used to estimate the performance of each constructed solution that, taking into account its lower bound, may improve the best solution found so far. The proposed approach is then evaluated through computational experiments.     

Model predictive controller-based multi-model control system for longitudinal stability of distributed drive electric vehicle

Distributed drive electric vehicle(DDEV) has been widely researched recently, its longitudinal stability is a very important research topic. Conventional wheel slip ratio control strategies are usually designed for one special operating mode and the optimal performance cannot be obtained as DDEV works under various operating modes. In this paper, a novel model predictive controller-based multi-model control system (MPC-MMCS) is proposed to solve the longitudinal stability problem of DDEV. Firstly, the operation state of DDEV is summarized as three kinds of typical operating modes. A submodel set is established to accurately represent the state value of the corresponding operating mode. Secondly, the matching degree between the state of actual DDEV and each submodel is analyzed. The matching degree is expressed as the weight coefficient and calculated by a modified recursive Bayes theorem. Thirdly, a nonlinear MPC is designed to achieve the optimal wheel slip ratio for each submodel. The optimal design of MPC is realized by parallel chaos optimization algorithm(PCOA)with computational accuracy and efficiency. Finally, the control output of MPC-MMCS is computed by the weighted output of each MPC to achieve smooth switching between operating modes. The proposed MPC-MMCS is evaluated on eight degrees of freedom(8DOF)DDEV model simulation platform and simulation results of different condition show the benefits of the proposed control system.     

绗缝加工企业信息化构建

随着绗缝企业规模的扩大,企业的信息化建设势在必行。针对绗缝企业生产流程控制和优化问题进行了分析和思考,从数据驱动控制理论的角度出发,考虑采用预测控制,并结合子空间方法以及现场总线技术等方法,提出了一种绗缝生产企业信息化质量管理系统解决方案。以基于数据驱动控制方法为核心,以预测控制为技术手段来建立整体控制系统。底层生产设备的控制上采用预测控制器,上层的企业整体控制系统考虑采用子空间预测控制构建信息化管理系统。     

Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring

This paper presents a new approach for cutting force denoising in micro-milling condition monitoring. In micro-milling, the comparatively small cutting force signal is contaminated by heavy noise, and as a result, it is necessary to denoise the force signal before further processing it. The traditional denoising methods, based on Gaussian noise assumption, are not effective in this situation because the noise is found to contain high non-Gaussian component. Based on the force and noise's sparse structures in the time–frequency domain, this approach employs a sparse decomposition approach and solves denoising as a convex optimization problem. It is shown that the proposed approach can separate the heavy non-Gaussian noise and recover useful information for condition monitoring.     

Optimization of a structure with contact conditions using equivalent loads

Engineering structures consist of various components, and the components interact with each other through contact. Engineers tend to consider the interaction in analysis and design. Interactions of the components have nonlinearity because of the friction force and boundary conditions. Nonlinear analysis has been developed to accommodate the contact condition. However, structural optimization using nonlinear analysis is fairly expensive, and sensitivity information is difficult to calculate. Therefore, an efficient optimization method using nonlinear analysis is needed to consider the contact condition in design. Nonlinear Response Optimization using Equivalent Loads (NROEL) has been proposed for nonlinear response structural optimization. The method was originally developed for optimization problems considering geometric/material nonlinearities. The method is modified to consider the contact nonlinearity in this research. Equivalent loads are defined as the loads for linear analysis, which generate the same response field as that of nonlinear analysis. A nonlinear response optimization problem is converted to linear response optimization with equivalent loads. The modified NROEL is verified through three examples with contact conditions. Three structural examples using the finite element method are demonstrated. They are shape optimization with stress constraints, size optimization with stress/displacement constraints and topology optimization. Reasonable results are obtained in the optimization process.     

Bi-objective redundancy allocation problem for a system with mixed repairable and non-repairable components

Traditionally, in the redundancy allocation problem (RAP), two general classes of optimization problems are considered; reliability optimization and availability optimization. Contrary to reliability optimization, fewer researchers have studied availability optimization to find out the optimal combination of components type and redundancy levels for each subsystem in a system for maximizing (or minimizing) the objectives. In each problem it is assumed that either the entire components are repairable or they are non-repairable. However, in real world situations, systems usually consist of both repairable and non-repairable components. In this paper a new Mixed Integer Nonlinear Programming (MINLP) model is presented to analyze the availability optimization of a system with a given structure, using both repairable and non-repairable components, simultaneously. To find the solution of the introduced MINLP, an efficient Genetic Algorithm (GA) is also developed. Furthermore, to show the efficiency of the proposed GA, a numerical example is presented. Experimental results demonstrate that the proposed GA has a better performance compared to one of the most recommended algorithm in the literature.     

Design of a robust model predictive controller with reduced computational complexity

The practicality of robust model predictive control of systems with model uncertainties depends on the time consumed for solving a defined optimization problem. This paper presents a method for the computational complexity reduction in a robust model predictive control. First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities. The simulation results show that the number of iterations to solve the problem at each sampling interval is reduced while the control performance does not alter noticeably.