Simple Adaptive Feedback Research Articles

Synchronization of stochastic fractional-order model of muscular blood vessels

This study deals with the synchronization problem of the stochastic fractional-order biomathematical model of muscular blood vessels (SFOMBVs) with nonlinear inputs using a state-feedback adaptive control law, in which the effects of uncertainties and external disturbances are considered in the modeling. The linear state feedback and adaptive laws are combined via an adaptive update law, leading to a simple and robust adaptive feedback scheme. The synchronization and tracking performances of the SFOMBV master and slave systems are investigated, and the stability of the proposed control system is proved based on stochastic analysis techniques and Lyapunov theory. The simulation results show that the proposed control method can lead an abnormal muscular blood vessel to a normal trajectory with good robustness by reducing the tracking error.

Real-time emulation of photovoltaic energy using adaptive state feedback control

The current paper presents an experimental validation of photovoltaic (PV) source emulation using a real-time dSpace controller. The PV source emulator is a nonlinear power electronic device used to provide in real-time the electrical behavior of PV panels by tracking the PV current–voltage (I–V) characteristic. It aims to deliver nearly the same current and voltage as an actual PV module without depending on atmospheric conditions. This innovative tool is able to aid researchers in PV systems to perform their tests and measurements at the desired weather parameters without time, space and cost constraints. The proposed PV emulator is based on a simple and efficient adaptive state feedback control that ensures good accuracy and high dynamic response compared to the existing commercial PV emulators based on the programmable power supply, which improves significantly the static and dynamic performances of existing PV emulators.

Adaptive PID-tracking control of muscle-like actuated compliant robotic systems with input constraints

• Simple adaptive feedback strategies to dominate an uncertain system. • Bio-inspiration/biomechanics. • Controlling a human-upper-arm-like system: double pendulum with muscle forces. • Input constraints. In this paper, adaptive control strategies are analyzed to control a compliant robot arm in form of a double pendulum structure. Powered by antagonistic, muscle-like actuators, the controller shall enable the system to track prescribed trajectories for a desired movement of the arm, as quickly and precisely as possible. But, due to the natural muscle behavior, the control variables underly prescribed bounds: the limited control inputs are generated by muscle intensities. Therefore, feedback strategies are sought which have to be limited/saturated from the very beginning. For this, at first, simulation studies of adaptive λ -tracking controllers are performed to apply the best-rated ones to the compliant robotic system . After some model adjustments concerning the mechanics of a prototype (for experimental verification), an intensity control is determined, to divide the controller variables to the respective muscle pairs. Various numerical simulations of a system with DoF=2 show the effectiveness of the saturated controllers in set-point and tracking control. The efficiency of these controllers is verified and proven in experiments of a prototype.

Pinning Synchronization of Coupled Memristive Recurrent Neural Networks with Mixed Time-Varying Delays and Perturbations

In this paper, the pinning synchronization of coupled memristive recurrent neural networks (MNNs) with mixed time-varying delays and perturbations is investigated. Precisely, the considered coupled MNNs include the non-delay, discrete time-varying delays, distributed time delays, impulsive perturbations and stochastic perturbations. Comparing with the existing results, the new and simple feedback controller and adaptive feedback controller are designed to achieve exponential synchronization with pinning schemes. Based on the suitable Lyapunov functional and the definition of pinning control, with the aid of inequality techniques and differential inclusions theory, some effective and novel sufficient conditions are obtained to guarantee the synchronization of our proposed model. Finally, numerical examples are given to illustrate the effectiveness and reasonable of our theoretical results.

Beamforming with a Simple Adaptive Partial Feedback Method in Cooperative Cognitive Radio Networks

This paper focuses on beamforming problem in a cooperative cognitive radio communication system where primary users (PUs) coexist with a secondary user (SU). An adaptive partial feedback structure for computing the channel state information is proposed. PUs and SU receivers execute their associated normalized least mean square adaptive algorithms. The output of each algorithm contains an estimate of the related channel coefficients. The receivers also reply the quantized step-size values of their algorithms via feedback channel to the transmitter, which empower it to execute these algorithms simultaneously. The transmitter iteratively employs the feedback information to estimate the channel weights and uses them for its beamforming scheme, in such a way that the SU receiver’s channel gain is maximized while the interference on PUs is maintained below a predefined level. Analysis of the proposed method shows that, with a simple modification, the required bits in feedback channel are considerably reduced. The method is applicable for time-varying systems. Besides, when the cross correlation matrix is the only available channel information between primary and secondary networks, a simple and compatible beamforming scheme is proposed. Simulation results evaluate the reasonable performance of the proposed method.

Suppressing chaos for a class of fractional-order chaotic systems by adaptive integer-order and fractional-order feedback control

This paper is devoted to studying how to more effectively suppress chaos for a class of fractional-order nonlinear systems. By means of adaptive control theory for integer-order nonlinear system, we propose two simple and novel adaptive feedback methods to control chaos. Rigorous theoretical proof is provided based on some essential properties of fractional calculus and Barbalat's Lyapunov-like stability theorem. It is discovered that both fractional-order feedback controller and integer-order one can guide chaotic trajectories to the unstable equilibrium point. To display the feasibility and validity of presented methods, some typical fractional-order chaotic systems have been chosen as numerical illustration. Furthermore, by comparing two different control techniques, one can find the fractional-order feedback control algorithm is more stable and more flexible.

Simple adaptive partial feedback method for full multiple-input multiple-output channel estimation

Partial feedback in multiple-input multiple-output communication systems provides tremendous capacity gain and enables the transmitter to exploit channel condition and to eliminate channel interference. In the case of severely limited feedback, constructing a quantised partial feedback is an important issue. To reduce the computational complexity of the feedback system, this study introduces an adaptive partial method in which at the transmitter, a set of easy to implement least-square adaptive algorithms is engaged to compute the channel state information. In the proposed method, at the receiver, the time-varying step-sizes of the algorithms are computed and replied to the transmitter via a reliable feedback channel. The transmitter iteratively employs this feedback information to estimate the channel weights. This method is independent of the employed space–time coding schemes and gives all channel components. Complementary solution is given to reduce the computational complexity and simulation examples are given to evaluate the performance of the proposed method.

Bio-inspired adaptive feedback error learning architecture for motor control

This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

Adaptive Control of Singularly Perturbed Worm-Like Locomotion Systems

We consider a certain type of a worm-like locomotion system: a three mass point system. We assume that this worm is towing a payload, so the masses differ extremely, and we have to deal with a singularly perturbed system. The dynamics equations are coupled via a small parameter \({{\varepsilon}}\) , which is typical for singularly perturbed systems. In order to apply simple adaptive feedback mechanisms (to approach desired properties of the worm motion) we have to guarantee the minimum-phase property (among others) of such systems. We show that such properties are preserved under singular perturbation. This is done within a general system class via construction of a normal form to gain deeper insight and to analyze such systems. We present the general case and apply the basic transformations to the worm equations. We point out that we are able to classify all required assumptions and properties by system qualities as, e.g., zeros of the reduced model or boundary-layer system. This is in contrast to other authors.

Adaptive Feedback Control for Chaos Control and Synchronization for New Chaotic Dynamical System

This paper investigates the problem of chaos control and synchronization for new chaotic dynamical system and proposes a simple adaptive feedback control method for chaos control and synchronization under a reasonable assumption. In comparison with previous methods, the present control technique is simple both in the form of the controller and its application. Based on Lyapunov′s stability theory, adaptive control law is derived such that the trajectory of the new system with unknown parameters is globally stabilized to the origin. In addition, an adaptive control approach is proposed to make the states of two identical systems with unknown parameters asymptotically synchronized. Numerical simulations are shown to verify the analytical results.

A simple adaptive feedback control method for chaos and hyper-chaos control

This paper investigates the problem of chaos and hyper-chaos control, and proposes a simple adaptive feedback control method for chaos control under a reasonable assumption. In comparison with previous methods, the present control technique is simple both in the form of the controller and its application. Several illustrative examples with numerical simulations are studied by using the results obtained in this paper. Study of examples shows that our control method works very well in chaos control.

Synchronization of three dimensional chaotic systems via a single state feedback

This paper investigates the synchronization of three dimensional chaotic systems by extending our previous method for chaos stabilization, and proposes a novel simple adaptive feedback controller for chaos synchronization. In comparison with previous methods, the present controller contains single state feedback. To our knowledge, the above controller is the simplest control scheme for synchronizing the three dimensional chaotic systems. The results are validated using numerical simulations.

A simple universal adaptive feedback controller for chaos and hyperchaos control

A simple universal adaptive feedback controller is proposed for chaos control. In comparison with previous methods, the proposed scheme, which uses a single feedback gain and converges very fast, is suitable for application to a larger class of chaotic, hyperchaotic and nonhyperbolic chaotic systems. A sufficient condition for selecting the least feedback terms is given, and a numerical example using the Lorenz system verifies the correctness and effectiveness of the proposed approach.

Controlling chaotic and hyperchaotic systems via a simple adaptive feedback controller

This paper investigates the problem of chaos control, and proposes a simple adaptive feedback control method for it under a reasonable assumption. In comparison with previous methods, the present control technique is simple both in the form of the controller and in its application. A numerical example using the Genesio chaotic system verifies the effectiveness and correctness of the proposed approach.

Takagi–Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design

This paper proposes the design scheme of the alternative adaptive observer and controller based on the Takagi–Sugeno (T–S) fuzzy model. The T–S fuzzy modeling and the state feedback control technique are adopted for the simple structure. The proposed method maintains consistent performance in the presence of parameter uncertainties and incorporates linguistic fuzzy information from human operators. In addition, with the simple adaptive state feedback controller, it solves the singularity problem, which occurs in the inverse dynamics based on the feedback linearization method. Using Lyapunov theory and Lipschitz condition, the stability analysis is conducted, and the adaptive law is derived. The proposed method is applied to the stabilization problem of a flexible joint manipulator in order to guarantee its performance.

Identifying the Topology of a Coupled FitzHugh–Nagumo Neurobiological Network via a Pinning Mechanism

Topology identification of a network has received great interest for the reason that the study on many key properties of a network assumes a special known topology. Different from recent similar works in which the evolution of all the nodes in a complex network need to be received, this brief presents a novel criterion to identify the topology of a coupled FitzHugh-Nagumo (FHN) neurobiological network by receiving the membrane potentials of only a fraction of the neurons. Meanwhile, although incomplete information is received, the evolution of all the neurons including membrane potentials and recovery variables are traced. Based on Schur complement and Lyapunov stability theory, the exact weight configuration matrix can be estimated by a simple adaptive feedback control. The effectiveness of the proposed approach is successfully verified by neural networks with fixed and switching topologies.

Synchronization of chaotic dynamical network with unknown generally time-delayed couplings via a simple adaptive feedback control

In this paper, based on the principle of functional differential equations, a simple adaptive feedback controller is proposed to synchronize the network with unknown generally time-delayed coupling functions. Unlike the other control schemes, we design the controllers by the output of each node. The advantage of this scheme is that we need not to know the concrete structure of coupling functions or a solution of node system in advance. Moreover, an illustrative example is considered and numerical simulations are performed to demonstrate the effectiveness of this control scheme. The numerical simulation results show that our method is valid and robust against the weak noise.

Improving consumer quality-efficiency by using simple adaptive feedback in a choice setting

This paper proposes a feedback control based adaptive scheme for providing choices for users in a web setting. We hypothesise that using this scheme for updating the number of choices presented produces an efficient method of user interaction. We conducted an experiment with 75 subjects who had to choose a computer they would buy, given monetary constraints, from a large choice set. We used various algorithms for the number of choices to be presented to the users in a time-sequential manner. One algorithm was the proposed simple feedback algorithm, which was proven by the data collected to be the most quality-efficient.

Pinning synchronization of delayed neural networks

This paper investigates adaptive pinning synchronization of a general weighted neural network with coupling delay. Unlike recent works on pinning synchronization which proposed the possibility that synchronization can be reached by controlling only a small fraction of neurons, this paper aims to answer the following question: Which neurons should be controlled to synchronize a neural network? By using Schur complement and Lyapunov function methods, it is proved that under a mild topology-based condition, some simple adaptive feedback controllers are sufficient to globally synchronize a general delayed neural network. Moreover, for a concrete neurobiological network consisting of identical Hindmarsh-Rose neurons, a specific pinning control technique is introduced and some numerical examples are presented to verify our theoretical results.

Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays

A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed, which is composed of constant coupling, coupling discrete time-varying delay and coupling distributed time-varying delay. All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion, which reflects a more realistic dynamical behaviour of coupled systems in practice. Based on a simple adaptive feedback controller and stochastic stability theory, several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays. Finally, numerical simulations illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.