Ali Azarbahram

Bio: Ali Azarbahram is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 3, co-authored 6 publications receiving 27 citations.

Sampled-Data Dynamic Event-Triggering Control for Networked Systems Subject to DoS Attacks

Abstract: The paper proposes a co-design framework for event-triggered stabilization control of a class of networked control systems (NCS) under unknown DoS attacks. To reduce the number of control inputs, a sampled-data dynamic event-triggering (S-DET) scheme is developed. Both the state measurements and monitoring of the S-DET are conducted periodically. The parameter design is based on the solution of linear matrix inequalities (LMI) obtained from a delay-dependent Lyapunov-Krasovskii functional (LKF) using the improved free weighting matrix (IFWM) technique. In the presence of DoS with unknown patterns, the proposed co-design framework is beneficial in two ways. (i): The desired level of resilience to DoS is included as a design input. (ii): The upper-bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) as compared to those obtained from other analytical solutions. The proposed co-design approach demonstrates the trade-off between the DoS resilience level and system performance indices. Numerical experiments quantify the effectiveness of the proposed approach both in terms of reducing the number of control updates and providing higher resilience to unknown DoS attacks.

Resilient fixed-order distributed dynamic output feedback load frequency control design for interconnected multi-area power systems

Abstract: The paper proposes a novel H-∞ load frequency control ( LFC ) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback ( DOF ) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multi-area power systems which also include uncertainties and time-varying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality ( LMI ) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed ( and decentralized ) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.

H_inf Consensus of nonlinear complex multi-agent systems using dynamic output feedback controller: An LMI approach

Abstract: This paper investigates a new method for consensus in a group of nonlinear complex multi-agent systems using fixed-order non-fragile dynamic output feedback controller, via an LMI approach. The proposed scheme is decentralized in the sense that each agent relies on the relative output information among the adjacent agents. The consensus based controllers are designed to minimize the effects of nonlinear terms of the agents as well as external disturbances. Converting consensus problem to stabilization of an equivalent augmented system using proper transformations, Lyapunov stability theorem is applied to obtain unknown controller parameters in order to guarantee consensus and simultaneously acquire considered control objectives. Finally, to demonstrate the effectiveness of the proposed algorithm and compare with similar earlier researches, a numerical example on a multi-agent system consisting of single link flexible manipulators is carried out.

Resilient Event-triggered Average Consensus Under Denial of Service Attack and Uncertain Network

Abstract: The paper investigates resilient conditions for the event-triggered average consensus problem under denial of service (DoS) attack and uncertainty in the network. To reach average consensus in the multiagent system, each node communicates with its neighbouring nodes only if an event-triggering condition is satisfied. In the presence of the DoS attack, no information can be communicated within the network. In addition to DoS, the information being transmitted through the communication channels is perturbed due to uncertainty in the nominally designed edge weights of the network. Using the Lyapunov theorem, we analytically determine the maximum allowable duration and frequency for the DoS attack and maximum network uncertainty for which the exponential event-triggered consensus convergence stays preserved. The practicability of the proposed event-triggering scheme is studied by proving the Zeno-behaviour exclusion. The performance of the implementation is quantified through simulations in different scenarios.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

Delay-dependent robust load frequency control for time delay power systems

Abstract: Summary form only given. The usage of communication channels introduces time delays into load frequency control (LFC) schemes. Those delays may degrade dynamic performance, and even cause instability, of a closed-loop LFC scheme. In this paper, a delay-dependent robust method is proposed for analysis/synthesis of a PID-type LFC scheme considering time delays. The effect of the disturbance on the controlled output is defined as a robust performance index (RPI) of the closed-loop system. At first, for a preset delay upper bound, controller gains are determined by minimizing the RPI. Secondly, calculation of the RPIs of the closed-loop system under different delays provides a new way to assess robustness against delays and estimate delay margins. Case studies are based on three-area LFC schemes under traditional and deregulated environments, respectively. The results show that the PID-type controller obtained can guarantee the tolerance for delays less than the preset upper bound and provide a bigger delay margin than the existing controllers do. Moreover, its robustness against load variations and parameter uncertainties is verified via simulation studies.

Event-Triggered Guaranteed Cost Leader-Following Consensus Control of Second-Order Nonlinear Multiagent Systems

Abstract: This article deals with the event-triggered leader-following guaranteed cost consensus control problem for second-order nonlinear multiagent systems, in which the guaranteed cost function is proposed to facilitate to enhance the consensus tracking regulation performance. To reduce the frequency of information transmission, a distributed event-triggered mechanism, which broadcasts the triggered states to its neighbours for each agent, is designed, and the triggering condition is then constructed for leader-following second-order nonlinear multiagent systems. By employing Lyapunov–Krasovskii method and Barbalat’s lemma, some sufficient conditions are derived to ensure the leader-following consensus and guaranteed cost performance for second-order nonlinear multiagent systems. It is also exhibited that the constructed triggering condition can efficaciously exclude the Zeno behavior. To testify the efficacy of the proposed theoretical methodology, a simulation example is offered.

Neural network controller design for fractional-order systems with input nonlinearities and asymmetric time-varying Pseudo-state constraints

Abstract: This article considers the neural adaptive control issues of a category of non-integer-order non-square plants with actuator Nonlinearities and Asymmetric Time-Varying pseudo-State Constraints. First, the original non-square non-affine system with input nonlinearities is transformed into an equivalent affine-in-control square model by defining a set of auxiliary variables and by employing the mean-value theorem. Second, Neural networks and Nussbaum functions are exploited to obviate the requirement of a complete knowledge of the system dynamics and the control directions, respectively. Third, a novel adaptive dynamic surface control method based on Caputo fractional derivative definitions and fractional order filters is developed to overcome the “explosion of complexity” problem in the traditional backstepping design process and to determine the parameter update laws and control signals, concurrently. Then, Asymmetric Barrier Lyapunov Functions with error variables are adopted to ensure the uniform stability of the closed-loop system and to prevent the violation of the full pseudo-State constraints. The novelties and contributions of this article are: (1) through the introduction of new technical Lemmas and corollaries, existing control design and stability theories linked to integer-order square systems are developed and extended to non-square non-integer-order ones. (2) all signals, including variables and errors in the closed-loop system are semi-global practical finite-time stability whereas the the tracking errors are asymptotically driven to zero without transgression of the constraints. Finally, the effectiveness and potential of the proposed control approach are substantiated by two example simulations.

Antagonistic Interaction-Based Bipartite Consensus Control for Heterogeneous Networked Systems

Abstract: This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.