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Highlights • The angular velocity guidance part improves the dynamic performance including overshoot and oscillation of MFA method. • The control-oriented adaptive Kalman filter is proposed which is independent of the mathematical model. • The proposed adaptive Kalman filter suppresses the heading sensor noise well with stochastic model parameter perturbation. • The improved MFA method has strong robustness and adaptability with uncertainties. Abstract Based on the model-free adaptive control (MFA) theory, the heading control problem of unmanned surface vehicle (USV) with uncertainties is studied. The compact form dynamic linearization based MFA (CFDL-MFA) control method and its difficulties in USV heading control applications are analyzed. Aiming at the time delay of rudder-heading control system and the overshoot characteristic of CFDL-MFA method, the angular velocity guidance algorithm is introduced. The outer loop PID guidance controller calculates the desired angular velocity, and the inner loop MFA controller is used for angular velocity control. The heading control is realized indirectly. Considering sensor noise in applications, a control-oriented adaptive Kalman filter (AKF) based on dynamic linearized model is proposed, which effectively suppresses the adverse effect of sensor noise on heading control of USV. Numerical simulations of filtering experiments and heading control experiments are completed which demonstrate the validity of the proposed heading MFA control method with angular velocity guidance and the proposed AKF method. [ABSTRACT FROM AUTHOR]

This paper shows that under an ISS-like condition on the zero-dynamics with time-delay, it is possible to achieve global adaptive regulation by dynamic partial state feedback for certain classes of time-delay cascade systems with nonlinear parameterization. The main contribution is the development of a delay-independent, partial state adaptive control scheme that employs a dynamic gain-based method, together with the adaptive feedback domination design and the technique of changing supply rate. By constructing suitable dynamic gain-based Lyapunov–Krasovskii functionals, we prove that all the states of the time-delay cascade system can be regulated to the origin while maintaining boundedness of the closed-loop system. [ABSTRACT FROM AUTHOR]

This paper examines an observer-based backstepping terminal sliding mode controller (BTSMC) for 3 degrees of freedom overhead transmission line de-icing robot manipulator (OTDIRM). The control law for tracking of the OTDIRM is formulated by the combination of BTSMC and neural network (NN) based approximation. For the precise trajectory tracking performance and enhanced disturbance rejection, NN-based adaptive observer backstepping terminal sliding mode control (NNAOBTSMC) is developed. To obviate local minima problem, the weights of both NN observer and NN approximator are adjusted off-line using particle swarm optimization. The radial basis function neural network-based observer is used to estimate tracking position and velocity vectors of the OTDIRM. The stability of the proposed control methods is verified with the Lyapunov stability theorem. Finally, the robustness of the proposed NNAOBTSMC is checked against input disturbances and uncertainties. [ABSTRACT FROM AUTHOR]

Adaptive control methods are developed for stability and tracking control of flight systems in the presence of parametric uncertainties. This paper offers a design technique of adaptive sliding mode control (ASMC) for finite-time stabilization of unmanned aerial vehicle (UAV) systems with parametric uncertainties. Applying the Lyapunov stability concept and finite-time convergence idea, the recommended control method guarantees that the states of the quad-rotor UAV are converged to the origin with a finite-time convergence rate. Furthermore, an adaptive-tuning scheme is advised to guesstimate the unknown parameters of the quad-rotor UAV at any moment. Finally, simulation results are presented to exhibit the helpfulness of the offered technique compared to the previous methods. [ABSTRACT FROM AUTHOR]

We firstly investigate the fixed-time stability analysis of uncertain permanent magnet synchronous motors with novel control. Compared with finite-time stability where the convergence rate relies on the initial permanent magnet synchronous motors state, the settling time of fixed-time stability can be adjusted to desired values regardless of initial conditions. Novel adaptive stability control strategy for the permanent magnet synchronous motors is proposed, with which we can stabilize permanent magnet synchronous motors within fixed time based on the Lyapunov stability theory. Finally, some simulation and comparison results are given to illustrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

Mobile wireless sensor networks (MWSNs) are wireless networks of small sensors moving around a certain coverage area relaying information among themselves and conveying their readings and data to base stations. Imposters are malicious nodes actively engaging other legitimate nodes in the network to read or inject wrong data. MWSN are susceptible to imposter attack and therefore, the protection of MWSN from imposter nodes placed by an adversary to contaminate the sensed data is essential for the reliability of its operation. Imposters detection algorithms need to be distributed in nature and therefore they are susceptible to variety of faults that can perturb the variables for algorithm and cause a major malfunction in the operation of the algorithm and subsequently the entire network if proper recovery mechanisms are not employed. The distributed nature of imposter detection schemes for WSN and the physical environment where the sensors are deployed require some approaches such as stabilization to deal with faults. A stabilizing distributed algorithm can withstand transient faults and start in an arbitrary initial configuration by eventually entering a legitimate system configuration regardless of the current system configuration. We view a fault as a transient fault if it affects the states of the system processes but not their program. In this paper, we propose an imposter detection scheme that can effectively deal with transient faults and arbitrary initialization. In addition, the proposed algorithm effectively adapts to the introduction and the removal of sensor nodes to/from the WSN which makes the proposed algorithm appropriate for practical sensor network applications. Other faults that can occur in the network and in the nodes are beyond the scope of this work. [ABSTRACT FROM AUTHOR]

A key problem when coordinating the behaviour of spatially situated networks, like those typically found in the Internet of Things (IoT), is adaptation to changes impacting network topology, density and heterogeneity. Computational goals for such systems, however, are often dependent on geometric properties of the continuous environment in which the devices are situated rather than the particulars of how devices happen to be distributed through it. In this article, we identify a new property of distributed algorithms, eventual consistency, which guarantees that computation converges to a final state that approximates a predictable limit, based on the continuous environment, as the density and speed of devices increases. We then identify a large class of programs that are eventually consistent, building on prior results on the field calculus computational model (Beal et al. 2015; Viroli et al. 2015a) that identify a class of self-stabilizing programs. Finally, we confirm through simulation of IoT application scenarios that eventually consistent programs from this class can provide resilient behavior where programs that are only converging fail badly. [ABSTRACT FROM AUTHOR]

In this paper, a new intelligent control scheme based on multiple models and neural networks is proposed to adaptively control a class of Hammerstein nonlinear systems with arbitrary deadzone input. This approach consists of a linear robust adaptive controller, multiple neural networks-based nonlinear adaptive controllers and a switching mechanism. Since the control input is derived from a modified certainty equivalent principle, the manner in which the closed-loop stability is established forms the main contribution. To show the usefulness of the developed results, three simulation examples, including a direct current motor subject to a nonlinear friction, are studied. [ABSTRACT FROM AUTHOR]

This paper presents the proofs of robust stability of a discrete-time robust model reference controller combined with variable structure in an adaptive framework. All the proofs of robust stability are derived for the discrete-time case and are similar to those already existing for the conventional non-combined case. The controller is applied to a SISO LTI plant with unmodeled dynamics of multiplicative and additive types. It is shown that the combined controller can arbitrarily improve the convergence of the error while maintaining the robustness if compared with the non-combined case. Simulation results illustrate the performance of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper investigates the leader-follower consensus problem of uncertain nonlinear systems in strict-feedback form. By parameterizations of unknown nonlinear dynamics of the agents, an adaptive dynamic surface control with the aid of predictors, tracking differentiators is proposed to realize output consensus of the multi-agent systems. Unlike the existing adaptive consensus methods, the predictor errors are used to learn the unknown parameters, which can achieve fast learning without high-frequency signals in control inputs. As a fast precise signal filter, the tracking differentiator is used in the control design instead of first-order filters, which can further improve the control performance. Based on graph theory and Lyapunov stability theory, it is shown that the outputs of all followers ultimately synchronize to that of the leader with bounded tracking errors. Simulation results are provided to validate the effectiveness and advantage of the proposed consensus algorithm. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The nonlinear nature of the system behavior has several disturbances due to which the stability of the system gets affected. Controlled operation of power systems is very critical and the most important factor is to achieve stable power system. For this reason, there is a need of different adaptive control techniques. Therefore, for obtaining lesser modeling error in the system, different intelligent control techniques are used, which improves the power system operation. PID controllers are most commonly used as it is simple in operation. Therefore, several tuning techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) are being used for the optimum setting of PID controller parameters so as to improve the system stability. In this paper, the performance of different PID controller techniques is compared for four error minimization methods and is being applied to the load frequency control model that helps to identify optimum controller for an individual. [ABSTRACT FROM AUTHOR]

This brief deals with the design and experimental application of a hybrid fractional adaptive cruise control (ACC) at low speeds. First, an improved fractional-order cruise control (CC) is presented for a commercial Citroën C3 prototype—which has automatic driving capabilities—at low speeds, which considers a hybrid model of the vehicle. The quadratic stability of the system is proved using a frequency domain method. Second, ACC maneuvers are implemented with two different distance policies using two cooperating vehicles—one manual, the leader, and the other, automatic—also at very low speeds. In these maneuvers, the objective is to maintain a desired interdistance between the leader and follower vehicles, i.e., to perform a distance control—with a proportional differential (PD) controller in this case—in which the previously designed fractional-order CC is used for the speed control. Simulation and experimental results, obtained in a real circuit, are given to demonstrate the effectiveness of the proposed control strategies. [ABSTRACT FROM PUBLISHER]

A vehicle stability control approach for four-wheel independently actuated (FWIA) electric vehicles is presented. The proposed control method consists of a higher-level controller and a lower-level controller. An adaptive control-based higher-level controller is designed to yield the vehicle virtual control efforts to track the desired vehicle motions due to the possiblemodeling inaccuracies and parametric uncertainties. The lower-level controller considering tire force saturation is given to allocate the required control efforts to the four in-wheel motors for providing the desired tire forces. An analytic method is given to distribute the high-level control efforts, without using the numerical-optimization-based control allocation algorithms. Simulations based on a high-fidelity, CarSim, and full-vehicle model show the effectiveness of the control approach. [ABSTRACT FROM AUTHOR]

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An incorrect inequality was applied in the proof of Theorem 2, in the above-mentioned paper. In this note, we correct the proof. The revised theorem requires bounded regressors. [ABSTRACT FROM AUTHOR]

This contribution considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems online. This method requires only the system outputs, which are assumed as measurable. The structure of the framework consists of three parts. The first part is based on a dynamic recurrent neural network (DRNN) to be used for local identification, analysis and multi-step-ahead prediction of the system. In the second part, a set of given input values will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, the most suitable control input value is chosen for the next predefined time interval according to a suitable cost function. The proposed controller is able to gain useful local knowledge and define autonomously suitable local control input according to the stability criterion. Numerical examples using inverted pendulum system and Lorenz system are shown to demonstrate the successful application and performance of the method. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]