Dajun Du

Multiple event-triggered H2/H∞ filtering for hybrid wired-wireless networked systems with random network-induced delays

Unlike the widely existing H2/H∞ filtering methods under single (i.e., wired or wireless) network with single channel environment, there exist multiple event-generators and different network-induced delays in hybrid wired-wireless networks, which make H2/H∞ filtering analysis and design more complex. The main objective of this paper is to investigate multiple event-triggered H2/H∞ filtering for hybrid wired-wireless networked systems with random network-induced delays. For multi-sensor network communications, a multiple event-triggered mechanism is employed firstly, which can reduce communication burden of each channel. The different communication characters of hybrid wired-wireless networks are then described by two Markov chains, and a general filtering error dynamic system model with the event-triggered parameters and multi-channel network-induced delays of hybrid wired-wireless networks is presented. Furthermore, the designed filter enables the filtering error dynamic system to be stochastic stability and to achieve a prescribed performance, and the relationships between the stability criteria and the maximum network-induced delays of hybrid wired-wireless networks, the event-triggered parameters and the system performance parameter are established. Finally, simulation results confirm the effectiveness of the proposed method.

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness.

Quantized control of distributed event-triggered networked control systems with hybrid wiredwireless networks communication constraints

Traditional analysis and design for networked control systems (NCSs) with state quantization have been investigated under single wired/wireless network environment. This paper studies the quantized control of distributed event-triggered NCSs under hybrid wiredwireless networks environment. Unlike the most existing NCSs, the controller communicates with distributed sensors through multiple wiredwireless channels and the measured signals from sensors might suffer from communication constraints of hybrid wiredwireless networks, which makes the quantized control of distributed event-triggered NCSs more complex. To reduce the communication burden of each channel, a distributed event-triggered mechanism and multiple quantization scheme are firstly proposed. The communication characters of hybrid wiredwireless networks are then analyzed, which are described by different Markov chains. Furthermore, a novel system model is presented, and a sufficient condition of the stochastic stability is derived. The relationship between the system stability criteria and the maximum hybrid wiredwireless network-induced delays, the event-generators parameters and multiple quantization parameters is established. Finally, simulation results confirm the effectiveness and feasibility of the proposed method.

Adaptive event-triggered communication scheme for networked control systems with randomly occurring nonlinearities and uncertainties

This paper proposes a novel adaptive event-triggered communication scheme for networked control systems (NCSs) with randomly occurring nonlinearities and uncertainties. Firstly, an adaptive event-triggered communication scheme for NCSs is proposed, which can adaptively adjust the trigger parameter with respect to the dynamic error to save the limited network resources while ensuring the desired control performance. Secondly, an integrated model of the studied system is built under consideration of the network-induced delay, adaptive event-triggered communication scheme and randomly occurring nonlinearities and uncertainties in a unified framework. Then, sufficient stability criterion to judge the mean-square sense asymptotically stable and stabilization criterion to co-design the parameters of the communication scheme and controller are obtained for the system under consideration. Finally, two examples are given to illustrate the effectiveness of the developed method.

Decentralized event-triggering communication scheme for large-scale systems under network environments

This paper investigates a decentralized event-triggering communication scheme for large-scale systems under network environments. First, a decentralized event-triggering communication (DETC) scheme for large-scale systems is proposed, which does not require that all transmitted nodes are synchronous. Second, a heuristic algorithm is presented to optimize the parameters of DETC to reduce the occupancy of the network resources induced by DETC. Third, by constructing a novel LyapunovKrasovskii functional, a stability criterion and a stabilization criterion are derived for ensuring that the solution of studied system is uniformly ultimately bounded (UUB). Compared with some existing emulation-based methods, the controller gains in this paper are no longer required to be known a priori. Finally, an example is given to show the effectiveness of the proposed approach.

Probabilistic optimal power flow for power systems considering wind uncertainty and load correlation

Abstract Considering wind uncertainty and load correlation, this paper is concerned with the probabilistic optimal power flow (POPF) calculation. The POPF model for wind-integration power system is firstly proposed. Two kinds of schemes in point estimate method, 2m and 2m+1 scheme, are then employed to solve the POPF. Moreover, the correlation samples of nodal injections are generated by the Cholesky decomposition method, and the path following interior point method is employed to solve the deterministic optimal power flow calculation. Finally, the proposed method is tested on the modified 5-bus and IEEE 30-bus test system. Simulation results show that 2m+1 scheme is feasible and effective to solve the POPF for wind-integration power system. Also, it is seen that correlated loads affect the POPF results, and the POPF with load correlation would reflect the system operation more accurately.

Modelling and stability analysis of MIMO networked control systems withmulti-channel random packet losses

Traditional networked control systems (NCSs) analysis and design have been based on the single closed-loop configuration. This paper studies the modelling and stability of multi-input multi-output (MIMO) networked control systems (NCSs) with multiple channels. Unlike the NCSs based on the single close-loop configuration, there exist data packet dropout, data packet out-of-order and network-induced delay in every channel, which make multi-channel MIMO NCSs more complex. In order to solve these network-related non-deterministic issues, a general switched system model with unknown switched sequence for multi-channel MIMO NCSs is first proposed, which can not only describe the MIMO NCSs where the controller communicates with sensors and actuators through distinct channels, but also can describe the NCSs based on the single closed-loop configuration. Based on Lyapunov stability theory combined with linear matrix inequalities (LMIs) techniques, a sufficient condition is then derived for multi-channel MIMO NCSs to be asymptotical stable in term of a set of bilinear matrix inequalities. Furthermore, the proposed results are easily extended to the uncertain MIMO NCSs. Finally, simulation results confirm the feasibility and effectiveness of the proposed method.

An improved event-triggered communication mechanism and L ∞ control co-design for network control systems

This paper studies an improved event-triggered mechanism (ETM) and L ∞ control co-design for network control systems with communication delay and external disturbances. Most existing ETMs only use the state or state-independent information. The drawback of such schemes is that the low transmission rate of sampled data cannot be obtained when the system is running close to or far away from the origin. To solve the issue, an improved ETM that can effectively improve the transmission efficiency during the whole operation time is firstly proposed. This is achieved by using both the state-dependent and the state-independent information. A general system model with communication delay and external disturbances is then presented, and sufficient conditions for both ultimately bounded stability and asymptotic stability are derived. The relationship between the stability criteria and parameters of the improved ETM, controller gain matrices, maximum communication delay, and upper bound of L ∞ -gain is established. Moreover, a co-design scheme is provided to design the desired ETM and controller that render the network load and control performance reach an expected level, which is more convenient than the two-step design method. Finally, numerical examples confirm the effectiveness of the proposed method.

A novel locally regularized automatic construction method for RBF neural models

This paper investigates automatic construction of radial basis function (RBF) neural models for nonlinear dynamic systems. The main objective is to automatically and effectively produce a parsimonious RBF neural model that generalizes well. This is achieved by proposing a locally regularized automatic construction (LRAC) method which combines a recently proposed fast recursive algorithm (FRA) with the leave-one-out (LOO) cross-validation criterion. The new method offers distinctive advantages over existing approaches. Firstly, it uses an error criterion where the original model parameters are regularized, in contrast to orthogonal least square (OLS) based approaches where transformed model parameters are regularized. This enables the determination of the significance of each original candidate center and produces a compact neural model. Further, it can automatically determine the network size by the iteratively minimizing a LOO mean-square-error (MSE) without the need to specify any additional termination criterion. Finally, by defining a proper regression context, the whole network construction process can be concisely formulated and easily implemented with significantly reduced computation. An analysis of computational complexity confirms the efficiency of the proposed method, and simulation results reveal its effectiveness in comparison with alternative approaches for producing sparse RBF neural models.

Probabilistic load flow calculation with Latin hypercube sampling applied to grid-connected induction wind power system

This paper investigates the probabilistic load flow (PLF) calculation with Latin hypercube sampling (LHS) technique for grid-connected induction wind power system. Considering the uncertainties of both loads and wind power outputs, firstly, probabilistic models of main components in wind power generation system are introduced. A combined iterative method for deterministic load flow is then extended to the PLF calculation for grid-connected induction wind power system, which facilitates simultaneous correction for the slip of induction generator and the nodal voltages during all iterations. Furthermore, to overcome the drawback of simple random sampling like excessive time consumption, LHS is combined with Monte Carlo simulation to execute the PLF. Finally, the proposed method is verified by an IEEE 14-bus system modified to include 20 wind turbines. Simulation results confirm the efficiency of the proposed method and reveal the impact of wind farm capacity on PLF results.