This article addresses the problem of fault-tolerant output tracking control for a class of Takagi–Sugeno (T–S) fuzzy systems with unmeasurable premise variables subject to additive and multiplicative actuator faults and external disturbances. In nominal conditions, utilizing a quadratic Lyapunov function and nonparallel distributed compensation technique, the suggested strategy delivers linear-matrix-inequality-based constraints. Simultaneously design of the proportional-integral (PI)-like state feedback controller and fuzzy antiwindup compensator is achieved with the aim of output tracking. In the faulty case, by considering the nominal system as a reference model, a direct adaptive projection-based approach is developed using the T–S fuzzy modeling and control techniques to supply the adaptive fault-tolerant controller components. An enhanced PI state/fault observer with unmeasurable premise variables is introduced only to provide the estimation of states to be used in the proposed controller. The overall closed-loop system ensures the uniformly ultimately bounded solutions for error dynamics. Two examples, subsuming an inverted pendulum and a chaotic power system, have been used to present the merits and efficiency of the suggested approach persuasively.

Active fault-tolerant control strategy aimed at tracking is studied for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems in the presence of input constraint and additive fault. In the fault-free case, the state space of the system is partitioned based on the presence or absence of inputs. Using the parallel distributed compensation technique, input-to-state stability is proved for the error dynamics. For input constraint satisfaction, a model predictive control based reference-management unit is proposed in the form of an online optimization module that solves a quadratic programming problem. In the faulty case, the fault-free system is used as a reference model that satisfies the input constraint. Then, the concept of virtual actuator is used to make the behavior of the faulty system similar to the behavior of the fault-free system by controlling the performance degradation. In this article, a full-order observer can be used for fault estimation. In order to keep the control input of the faulty system within the admissible range defined for the fault-free system, a constraint-change scenario is considered. After detecting the fault, the constraint on the input of the fault-free system is changed in a way to keep the control input of the faulty system in the specified range. All design criteria are formulated as a linear matrix inequality problem. In order to evaluate the proposed control approach, simulations are performed on two systems: a 3-DoF helicopter and a 2-D overhead crane.

Dynamic-state estimation plays a critical role in achieving real-time wide-area monitoring of power systems. On the other hand, false data injection (FDI) attacks are substantial threats, which can undesirably ruin the state estimation results. To tackle this problem, an effective secure hybrid dynamic-state estimation approach that involves a dynamic model of the attack vector is proposed in this article. In the proposed method, an initial estimation of the system states is first obtained using a designed unknown input observer (UIO). Subsequently, based on the system, UIO models, and the initial estimations of the states, a dynamic model for the attack vector is extracted. Ultimately, the attack model is augmented with the main system model for coestimation of the attack and the system states using a Kalman filter. The onset of the FDI attack is rapidly detected by the accurate estimation of the attack vector. The effectiveness of the proposed approach is demonstrated under different FDI attack scenarios by a thorough theoretical analysis as well as simulations on IEEE 14-bus and 57-bus test systems. In order to show that the proposed method can keep up with typical scan rates of commercial phasor measurement units, a series of software-in-the-loop experiments are also conducted and the real-time feasibility of the proposed approach is guaranteed.

This paper addresses a brand new way of thinking that distinguishes itself by integrating two entities, namely, dynamic spectrum management on the one hand and cognitive dynamic system (CDS) on the other. To be more precise, the CDS performs the role of a cognitive engine centered at the heart of wireless communications, wherein licensed bands rooted in network providers responsible for primary users and unlicensed bands rooted in other networks responsible for secondary users. Thus, these two worlds embody primary users and secondary users, which collectively function within a single complex wireless communication network empowered by the CDS for the first time ever.

There are two worlds of wireless communications: the legacy wireless world and the cognitive wireless world. Spectrum holes are the medium through which the two worlds interact. Releasing subbands by primary users allows the cognitive radio users to perform their normal tasks and, therefore, to survive. In other words, the old world affects the new world through appearance and disappearance of the spectrum holes and there is a master-slave relationship between them. Hence, the two worlds of wireless communications are going on side by side. This makes a cognitive radio network a multiple-time-scale dynamic system: a large-scale time in which the activities of primary users change and a small-scale time in which the activities of secondary users change accordingly. Such systems are called double-layer dynamic systems. A model is built that can be used as a testing tool for policy forecast and incorporates time evolution as the life span (control horizon) of a given policy. Two types of time dependency are studied: time-dependent equilibria and time-dependent behaviour away from the predicted curve of equilibria. Theories of evolutionary variational inequalities and projected dynamic systems on Hilbert spaces are used to study these two types of time dependency, respectively.

Built on the theory of supply chain networks, this paper presents a model for the spectrum market, which includes three different tiers of decision makers; legacy owners, spectrum brokers, and secondary users (cognitive radios). Behavior of various decision-makers, the governing equilibrium conditions, and the transient behavior of the network are studied. Prices are determined endogenously in the model.

This paper is inspired by how cognitive control manifests itself in the human brain and does so in a remarkable way. It addresses the many facets involved in the control of directed information flow in a dynamic system, culminating in the notion of information gap, defined as the difference between relevant information (useful part of what is extracted from the incoming measurements) and sufficient information representing the information needed for achieving minimal risk. The notion of information gap leads naturally to how cognitive control can itself be defined. Then, another important idea is described, namely the two-state model, in which one is the system's state and the other is the entropic state that provides an essential metric for quantifying the information gap. The entropic state is computed in the perceptual part (i.e., perceptor) of the dynamic system and sent to the controller directly as feedback information. This feedback information provides the cognitive controller the information needed about the environment and the system to bring reinforcement leaning into play; reinforcement learning (RL), incorporating planning as an integral part, is at the very heart of cognitive control. The stage is now set for a computational experiment, involving cognitive radar wherein the cognitive controller is enabled to control the receiver via the environment. The experiment demonstrates how RL provides the mechanism for improved utilization of computational resources, and yet is able to deliver good performance through the use of planning. The paper finishes with concluding remarks.

In this paper, we describe a cognitive radar (CR) that mimics the visual brain. Although the visual brain and radar are different in that the visual brain does not transmit a probing signal to the environment while the active radar greatly relies on the probing signal it transmits to the environment, both of them are observers of the surrounding environment. As such, there is much that we can learn from the visual brain in building a new generation of CRs that outperform traditional radars. In this paper, we confine the discussion, in both analytic and experimental terms, to CR aimed at target tracking. From a theoretical perspective, using the posterior Cramér-Rao lower bound (PCRLB), it is shown that a cognitive tracking radar has the potential to improve tracking performance significantly. In particular, computer experiments are presented, which demonstrate that CR can indeed go beyond the theoretical limits of traditional active radars (TARs) as well as fore-active radars (FARs); the latter are radars equipped with feedback from the receiver to the transmitter. Moreover, computer experiments are presented to demonstrate another practical benefit resulting from the combined use of memory and executive attention in CR for a target-tracking application. Specifically, it is shown that with the provision of these two cognitive processes, the transition in switching from one transmit waveform to another goes forward in a smooth manner. Such a capability is beyond that of TAR or FAR.

A cognitive radio network is a multiuser system, in which different users compete for limited resources in an opportunistic manner, interacting with each other for access to the available resources. The fact that both users and spectrum holes (i.e., unused spectrum subbands) can come and go makes a cognitive radio network a highly dynamic and challenging wireless environment. Therefore, finding robust resource-allocation algorithms, which are capable of achieving reasonably good solutions fast enough in order to guarantee an acceptable level of performance under worst case interference conditions, is crucial in such environment. The focus of this paper is the transmit-power control in cognitive radio networks, considering a noncooperative framework. Moreover, tools from control theory are used to study both the equilibrium and transient behaviors of the network under dynamically varying conditions.

While a conventional bilateral teleoperation system involves only one pair of master/slave robots, cooperative telerobotic systems can consist of multiple pairs of robotic manipulators. Due to dynamic interaction among slave manipulators as well as communication latency, the control of such systems can be particularly challenging. This paper presents a multimodel discrete-time controller for teleoperation in cooperative environments subject to a known constant communication delay. Discrete-time state-space models that explicitly incorporate signal delays are developed for free motion/soft contact and rigid contact phases of teleoperation. Mode-based linear quadratic Gaussian (LQG) controllers are proposed that can deliver a stable transparent response for each phase of operation. Switching among these controllers occur according to the identified phase of contact. The robustness of the controllers with respect to parametric uncertainty is examined via the Nyquist analysis. Simulation results demonstrate the effectiveness of the proposed approach

Cooperative telerobotic systems consist of multiple pairs of master/slave robotic manipulators operating in a shared environment. This paper presents a multilateral adaptive nonlinear control architecture for cooperative teleoperation. The proposed framework allows for transmission of position and force information between all master and slave robots rather than merely between corresponding units. An adaptive nonlinear controller establishes kinematic correspondence among masters and slaves. The operators are presented with a virtual intervening tool in order to collaboratively interact with the environment. Models of operators, master and slave robots, tool, and environment are incorporated in the design. The stability of the system in the presence of parametric uncertainty in the dynamics is proven via Lyapunov analysis. Simulation and experimental studies demonstrate that the proposed approach is highly effective in all phases of a teleoperation task, i.e. in free motion, in contact with a flexible environment, and in contact with a rigid environment.