Chao-Yang Chen

Optimal Tracking Performance Limitation of Networked Control Systems With Limited Bandwidth and Additive Colored White Gaussian Noise

This paper studies optimal tracking performance issues for multi-input-multi-output linear time-invariant systems under networked control with limited bandwidth and additive colored white Gaussian noise channel. The tracking performance is measured by control input energy and the energy of the error signal between the output of the system and the reference signal with respect to a Brownian motion random process. This paper focuses on two kinds of network parameters, the basic network parameter-bandwidth and the additive colored white Gaussian noise, and studies the tracking performance limitation problem. The best attainable tracking performance is obtained, and the impact of limited bandwidth and additive colored white Gaussian noise of the communication channel on the attainable tracking performance is revealed. It is shown that the optimal tracking performance depends on nonminimum phase zeros, gain at all frequencies and their directions unitary vector of the given plant, as well as the limited bandwidth and additive colored white Gaussian noise of the communication channel. The simulation results are finally given to illustrate the theoretical results.

Fundamental performance limitations of networked control systems with novel trade-off factors and constraint channels

Abstract This paper focuses on the optimal tracking performance issues for linear time invariant system with bandwidth limited and additive colored white Gaussian noise (ACGN) simultaneously. The nonminimal phase and unstable plant are considered, and multi-repeated zeros and poles is investigated. The objective function of tracking response is minimized jointly with the control effort. In order to more fully reflect the performance of the network control systems (NCSs), the performance index is measured by the tracking error energy, input channel energy and plant input energy using novel trade-off factors. The novel trade-off factors can be measured each frequency band for each signal, which are stable and minimal phase transfer function. To obtain the optimal performance, the two-parameter controller is adopted. The tracking performance is given by explicit expression, which is critically dependent on the intrinsic characteristics of the given plant (unstable poles and nonminimal phase zeros), communication parameters (bandwidth and statistical characteristics of network noise) and statistical characteristics of reference signal. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

Adaptive neural control for a class of stochastic nonlinear systems with unknown parameters, unknown nonlinear functions and stochastic disturbances

In this paper, adaptive neural control (ANC) is investigated for a class of strict-feedback nonlinear stochastic systems with unknown parameters, unknown nonlinear functions and stochastic disturbances. The new controller of adaptive neural network with state feedback is presented by using a universal approximation of radial basis function neural network and backstepping. An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function. Adaptive bounding design technique is used to deal with the unknown nonlinear functions and unknown parameters. It is shown that the global asymptotically stable in probability can be achieved for the closed-loop system. The simulation results are presented to demonstrate the effectiveness of the proposed control strategy in the presence of unknown parameters, unknown nonlinear functions and stochastic disturbances.

Task-space coordinated tracking of multiple heterogeneous manipulators via controller-estimator approaches

Abstract This paper studies the task-space coordinated tracking of a time-varying leader for multiple heterogeneous manipulators (MHMs), containing redundant manipulators and nonredundant ones. Different from the traditional coordinated control, distributed controller-estimator algorithms (DCEA), which consist of local algorithms and networked algorithms, are developed for MHMs with parametric uncertainties and input disturbances. By invoking differential inclusions, nonsmooth analysis, and input-to-state stability, some conditions (including sufficient conditions, necessary and sufficient conditions) on the asymptotic stability of the task-space tracking errors and the subtask errors are developed. Simulation results are given to show the effectiveness of the presented DCEA.

Optimal tracking performance of control systems with two-channel constraints

This paper focuses on the tracking performance limitation for a class of networked control systems (NCSs) with two-channel constraints. In communication channels, we consider bandwidth, energy constraints and additive colored Gaussian noise (ACGN) simultaneously. In plant, non-minimal zeros and unstable poles are considered; multi-repeated zeros and poles are also applicable. To obtain the optimal performance, the two-parameter controller is adopted. The theoretical results show that the optimal tracking performance is influenced by the non-minimum phase zeros, unstable poles, gain at all frequencies of the given plant, and the reference input signal for NCSs. Moreover, the performance limitation is also affected by the limited bandwidth, additive colored Gaussian noise, and the corresponding multiples for the non-minimum phase zeros and unstable poles. Additionally, the channel minimal input power constraints are given under the condition ensuring the stability of the system and acquiring system performance limitation. Finally, simulation examples are given to illustrate the theoretical results.

Event-driven multi-consensus of multi-agent networks with repulsive links

Due to diversification in environment, a network of agents may have several emergent behaviors. This paper introduces an event-driven paradigm for realizing multi-consensus, which as a generic version of group/cluster consensus, depends both on the initial underlying topology and initial states.For the network feature, we resort to a distributed event-driven paradigm, since event-driven control has advantages on communication reduction and control energy saving. Moreover, to relax assumption on network topology, repulsive links are exploited at event instants to facilitate multiple coordination.To realize multi-consensus, we design a distributed event-driven controller based on coupled intra-subgroup and extra-subgroup information. In this context, with different control strengths, the finial value of MAN would be varying. This phenomenon matches the principle of multi-consensus in the current study.Based on LaSalles invariance principle, we prove that under the proposed event-driven configuration, the joint cooperation and competition contributes to multi-consensus provided that the sampling period is no larger than a positive threshold. It is shown there is no further requirement on network topologies in pursuit of multiple coordination. This paper studies multiple coordination of multi-agent networks under an event-driven paradigm. For an undirected connected graph, a node clustering scheme is first adopted to ensure a relatively strong degree of connectivity within each potential subgroup, and some repulsive effect is used to deal with the extra-subgroup links. To reduce unnecessary communication, a distributed event-driven controller is designed via coupled intra-subgroup and extra-subgroup information. Based on the LaSalles invariance principle, it is shown that under the proposed event-driven control configuration, multi-agent networks can realize multi-consensus without any balanced requirement on the underlying topologies. Simulation work is presented to validate the theoretical results.

Optimal Tracking Performance of MIMO Discrete-Time Systems with Network Parameters

The optimal regulation properties of multi-input and multioutput (MIMO) discrete-time networked control systems (NCSs), over additive white Gaussian noise (AWGN) fading channels, based on state space representation, are investigated. The average performance index is introduced. Moreover, the regulation performance is measured by the control energy and the error energy of the system, and fundamental limitations are obtained. Two kinds of network parameters, fading and the additive white Gaussian noise, are considered. The best attainable regulation performance limitations can be obtained by the limiting steady state solution of the corresponding algebraic Riccati equation (ARE). The simulation results are given to demonstrate the main results of the theoretical development.

Robust Adaptive Neural Control for a Class of Stochastic Nonlinear Systems

In this paper, adaptive neural control is investigated for a class of nonlinear stochastic systems with stochastic disturbances and unknown parameters. Under the condition of all system states being available for feedback, by employing the back stepping method, a suitable stochastic control Lyapunov function is then proposed to construct an adaptive neural network state-feedback controller, and unknown parameters are reasonably disposed. It is shown that, the the closed-loop system can be proved to be global asymptotically stable in probability. The simulation results demonstrate the effectiveness of the proposed control scheme.

Trade-off performance analysis of LTI system with channel energy constraint.

In this paper, the trade-off performance between tracking error, control input energy and channel input power is studied. By modelling the communication channel as the additive coloured Gaussian noise channel (ACGN) with limited bandwidth, a new performance index is proposed and minimized over all stabilizing two-degree-of-freedom controllers. The results show that the trade-off performance is correlated to the intrinsic characteristics of the plant, including the locations and directions of the unstable pole, non-minimum phase zero. However it is unrelated to the non-minimum phase zeros of filter because of the two-degree-of-freedom controller. We also demonstrated that ACGN may degenerate the tracking performance. Finally, a typical example is given to validate the theoretical results.

Optimal tracking performance of MIMO networked control systems with communication constraints

In this paper, we study the optimal tracking performance problem of continuous-time linear multi-input multi-output (MIMO) networked control systems. The unstable and non-minimum phase systems is considered. The output feedback path is subject to quantization noise, additive white Gaussian noise and bandwidth constraints, and encoding and decoding are considered. The reference input is a random signal. The performance is measured through the tracking error energy, and two-parameter controller is considered. The explicit expression of the tracking performance has been obtained. The results show that the unstable poles and non-minimum phase zeros of the plant, the channel noise, bandwidth, quantization noise and coder will aggravate the optimal tracking performance.