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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the data-driven consensus tracking problem for heterogeneous multi-agent systems. All of the heterogeneous dynamics, disturbances and measurement noises are considered for the output tracking consensus of nonlinear multi-agent network under a data-driven scheme. A dynamic linearisation method is introduced to deal with the nonlinear nonaffine structures of the agents and a heterogeneous linear data model for the agent is obtained due to the heterogeneous dynamics of the agent itself. On this basis, a distributed model-free adaptive predictive control (DMFAPC) algorithm is constructed by the use of consensus errors and the robustness analysis is conducted in the presence of disturbances. Further, the results are modified by using measured outputs to replace the system outputs, and thus a measured output-based DMFAPC is presented to deal with the omnipresent measurement noises in practical applications. The two proposed DMFAPC methods are data driven since no mechanistic model information is required wherein. And thus, no unmodelled dynamics affects the consensus performance. Instead, they can improve the control performance by utilising additional predictive information. The two proposed DMFAPC methods are verified using numerical simulations. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the problem of blind adaptive identification, which focuses on how to obtain the consistent estimation of channel characteristics when only the output signal of each transmission channel is available. To solve this problem, traditional algorithms usually construct a single‐input–multiple‐output system resorting to the technique of antenna array or time oversampling. However, they simply suppose that the noise of each channel is known a priori or balanced, which cannot always be satisfied in practice. Therefore, considering the practical situation where the noise of each transmission channel is both unknown and unbalanced, a bias‐compensated recursive least‐squares algorithm is proposed, which can estimate the unbalanced noises in real time and obtain the consistent estimation of channel characteristics. Simulation results illustrate the good performance of the proposed algorithm under different signal‐to‐noise‐ratio conditions. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive generalised function projective synchronisation scheme of fractional-order chaotic systems with different dimensions and orders and fully unknown parameters is presented. On the basis of the Lyapunov method of fractional-order systems, a stability theorem of the fractional-order system with non-identical orders is proven. Using the fractional-order controller and adaptive control theory, sufficient conditions for synchronisation and unknown parameters update rules are obtained. Theoretical analysis and numerical simulations are provided to verify the validity of the proposed scheme. Moreover, synchronisation results are applied to secure communication via modified chaotic masking (MCM) method. The unpredictability of the scaling function matrix and the use of fractional-order systems with different orders can increase the security of the cryptosystem. The security analysis shows that the introduced algorithm has large key space, high sensitivity to encryption keys, higher security and the acceptable encryption speed. [ABSTRACT FROM AUTHOR]

Summary: Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined L1 adaptive control and iterative learning control (ILC) framework to achieve high‐precision trajectory tracking in the presence of unknown and changing disturbances. The L1 adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses L1 adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high‐level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined L1‐ILC framework compared with approaches using ILC with an underlying proportional‐derivative controller or proportional‐integral‐derivative controller. Results highlight that our L1‐ILC framework can achieve high‐precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller. [ABSTRACT FROM AUTHOR]

In this paper, an immune algorithm (IA)-based dead-time elimination PWM control strategy is proposed. For existing dead-time elimination applications, one of the major problems is the dead-time control around the zero-current-crossing points. To deal with this problem, this paper proposes a different PWM control method which first restricts the control sequence to a specified level around the zero-crossing zone. Also, the proposed method can improve the current waveform quality by using the IA approach and three-level control strategy. Compared with conventional dead-time elimination methods, this technique has the features of simple hardware requirement and adaptive control. Moreover, this control strategy effectively eliminates the effect of dead-time, while at the same time significantly reducing the total harmonic distortion of output current and improving the amplitude of output RMS value in different modulation indexes and loads conditions. To verify the analysis, an experimental platform based on DSP and field-programmable gate array is built. The simulation and experimental results are given to demonstrate the effectiveness and feasibility of this new method. [ABSTRACT FROM AUTHOR]

In this paper, a new robust adaptive controller is investigated to force an underactuated surface marine vessel to follow a predefined parameterised path at a desired speed, despite actuator saturation and the presence of model uncertainties as well as environmental disturbances induced by waves, wind and sea-currents. To ensure robustness of the path-following controller, time-varying constraint on the off-track error (i.e. the maximal distance from the ship to the reference path) is considered. To address the off-track error constraint the tan-barrier Lyapunov function is incorporated with the control scheme, where the idea of auxiliary design system introduced in Chen, Sam, and Ren (2011) is adopted and its states are used in combination with backstepping and Lyapunov synthesis to adaptive tracking control design with guaranteed stability. Furthermore, the command filters are adopted to implement physical constraints on the virtual control laws so that analytic differentiation of the virtual control laws is avoided. We show that the proposed robust adaptive control law is able to guarantee semi-global uniform ultimate bounded stability of the closed-loop system. Numerical simulations and experimental results are carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The present work has addressed the output control problem using backstepping controller based on adaptive observer for control and stabilization of underactuated system, represented by planar Acrobot system. The backstepping control strategy is a procedural approach based on the passivity concept used for upright stabilization of Acrobot, while the adaptive observer has been used for speed and mass estimation in presence of uncertainties. The planar Acrobot system is two Degree of Freedom underactuated system. It moves in the vertical plane and it works like a gymnast on a high bar, where the first and the second joints are analogous to hands and hips of the gymnast respectively. This paper is devoted to design and develop an observer-based backstepping control algorithm in order to stabilize and balance the links of Acrobot at their upright angular positions. Therefore, two design algorithms have been presented; one is developed for the backstepping control and the other one for adaptive observer. The stability of the observer-based backstepping controlled system has been analyzed based on Lyapunov direct method. The robustness characteristic of the adaptive observer has been evaluated under the variation of the mass of unactuated link an amount of 10% over the nominal value. Computer simulation based on MATLAB programming format has showed that the backstepping control algorithm could successfully stabilize and balance the Acrobot's links at the desired upright position. In addition, the adaptive observer could keep good robustness characteristics in estimation of unmeasured state in the presence of uncertainty in system parameter. [ABSTRACT FROM AUTHOR]

For the terminal phase guidance problem of the missile intercepting weaving maneuvering target, an adaptive internal model guidance laws in the three-dimensional (3-D) engagement space is proposed in this paper. The guidance law adopts the disturbance rejection theory by treating the target weaving maneuvering accelerations as external disturbance, which comprises of nominal part and adaptive part. The nominal part based on feedback linearization method ensures the whole guidance system stable and the adaptive part based on internal model principle is used to recover the disturbance signals on-line to reject the target maneuver asymptotically. The algorithm guarantees the whole guidance system with satisfying performance both in transient and steady state on the effect of target maneuver on guidance system. The stability analyses and theory proof are provided in this paper. At last, numerical simulations are carried out to illustrate the effectiveness of the proposed guidance law. [ABSTRACT FROM AUTHOR]

This article discusses the issue of robust adaptive neural network (NN) consensus tracking control for nonlinear strict-feedback multi-agent systems with quantised input. By combining the neural network approach with robust techniques, a novel switching function is introduced to guarantee the tracking performance of this system. To estimate the unmeasured state, an NN-based adaptive state observer is developed. Based on backstepping dynamic surface control algorithms, a robust output feedback controller is constructed to guarantee that all signals in the closed-loop system remain globally uniformly ultimately bounded. Finally, numerical simulations are carried out to demonstrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a distributed swarm system for small fixed-wing unmanned aerial vehicles (UAVs). In particular, to perform various missions with multiple UAVs that are densely gathered and collision free, a hybrid-flocking control algorithm is synthesized by using three types of control protocols: vector field guidance (for path following/loitering), augmented Cucker–Smale (ACS) model (for collective flocking behavior), and potential field (for collision avoidance). In particular, to address the issue of conflicts between different control protocols, the adaptive ACS model is proposed and the optimization problem is formulated to determine the suitable mixing weights of control protocols. We also design the transition of multiple operation modes and communication architecture for the swarm system. The system is evaluated using the proposed hybrid-flocking control algorithm by proof-of-concept real flight experiments using 18 small fixed-wing UAVs as well as extensive numerical simulations. Flight experiments are successfully performed for multiple consecutive tasks including the individual task, circular path loitering and elliptical path loitering while avoiding collisions among UAVs. • The distributed small fixed-wing UAV swarm system is designed. • The hybrid-flocking control algorithm is proposed by combining three control protocols. • The optimal mixing weight improves the mission performance. • Numerical simulations and flight experiments validate the hybrid-flocking algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the region-reaching control problem for a flexible-joint robotic manipulator which is formulated by Lagrangian dynamics. An adaptive control scheme is proposed for the manipulator system having two constrained regions which are constructed by selecting appropriate objective functions. The two joints of the flexible-joint manipulator can be, respectively, confined in different regions, and this gives more flexibility than the traditional fixed-point tracking control. By performing a straightforward Lyapunov stability analysis, a simple control algorithm is established to provide a solution for the region-reaching control problem. Finally, numerical simulations are given to validate the theoretical results. [ABSTRACT FROM AUTHOR]

SUMMARY Recently, the passivity results for linear time-invariant systems were successfully extended to nonlinear and nonstationary systems, thus guaranteeing stability of adaptive control of nonlinear square systems. Based on this theoretical development, this paper presents the development of a new class of direct adaptive controllers, which employ a new decentralized adaptation law mechanism that is developed from the simple adaptive control technique. The resulting direct adaptive control methodology is referred to as decentralized simple adaptive control. A simplification of this new control algorithm, referred to as decentralized modified simple adaptive control, is also presented. In addition, it is shown that both control methodologies can be modified to avoid divergence in practical situations, where the trajectory tracking errors cannot reach zero. Using Lyapunov direct method and Lasalle's invariance principle for nonautonomous systems, the formal proof of stability is established. As well, a numerical simulation study for a trajectory tracking problem by a rigid-joint manipulator is presented to illustrate the new adaptive control approaches. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the adaptive consensus problem of multi-agent nonlinear systems with odd impulsive sequence. To reduce the required information transmitted among the nodes, the distributed impulsive control protocol is designed. Considering the unknown system parameters in real system, the adaptive control technique is also used and some novel sufficient consensus conditions are derived to achieve the consensus of multi-agent nonlinear systems. The proposed odd impulsive control can obtain larger control region than the common impulsive consensus method with fixed impulsive instants, which is more flexible and reliable in practical application. Finally, two simulations are given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Highlights • Fractional-order calculus is proposed to yield more performing ACC/CACC gap control. • The control design relies in multi-objective optimization seeking a Pareto front. • String stability is further improved for lower time gaps maintaining robustness. • Comparison with state-of-the-art approaches is included, showing its benefits. • The comparison and validation section has been reorganized. Abstract Traffic flow optimization and driver comfort enhancement are the main contributions of an Adaptive Cruise Control (ACC) system. If communication links are added, more safety and shorter gaps can be reached performing a Cooperative-ACC (CACC). Although shortening the inter-vehicular distances directly improves traffic flow, it can cause string unstable behavior. This paper presents fractional-order-based control algorithms to enhance the car-following and string stability performance for both ACC and CACC vehicle strings, including communication temporal delay effects. The proposed controller is compared with state-of-the-art implementations, exhibiting better performance. Simulation and real experiments have been conducted for validating the approach. [ABSTRACT FROM AUTHOR]

This paper presents an aerodynamic model-based robust adaptive control algorithm for close formation flight. The leader aircraft is assumed to be at level and straight flight which characterizes the most common scenario for close formation flight. The formation aerodynamic effects are assumed to be unknown, but an online formation aerodynamic model is available to predict those effects. In light of the online formation aerodynamic model, a robust adaptive control algorithm is thereafter developed on the follower aircraft to counteract the unknown formation aerodynamic effects and obtain highly accurate formation tracking performance. The proposed control algorithm is composed of a baseline controller and an integrator-augmented robust adaptive controller, which can efficiently deal with both matched and mismatched formation aerodynamic effects and external disturbances. The major advantage of the proposed design is that it can achieve at least ultimate bounded tracking control with certain transient performance guaranty. The efficiency and robustness of the proposed control design are eventually validated via numerical simulations of close formation flight at two different scenarios. [ABSTRACT FROM AUTHOR]

This paper was aimed at using an adaptive control to develop a backstepping method for a special class of nonlinear systems, which requires no information on the upper bound of parametric uncertainty or disturbance. In this research, a novel control approach, including a developed backstepping method and an adaptive controlling method, is introduced as an adaptive generalized backstepping method (AGBM). Compared to the standard backstepping method, AGBM is far more efficient because the standard method is applicable in strictly feedback systems, but AGBM expands this class of systems. Another advantage of AGBM is that it can be applied to a special class of nonlinear systems with unmatched uncertainty as well as unknown upper bound disturbance. In this method, an adaptive controlling method is used to compensate uncertainty and parametric uncertainty besides stabilizing the controller against disturbances applied to the system. Finally, numerical simulation results demonstrate the advantages and feasibility of the presented algorithm, showing that the AGBM controller guarantees robustness and system stability in the presence of disturbance and uncertainty. [ABSTRACT FROM AUTHOR]

This paper investigates the design of an improvement scheme of direct power control (DPC) based on adaptive neuro-fuzzy inference system (ANFIS) for mitigation of power quality problems. The proposed control technique is performed using two ANFIS blocks. The first ANFIS block uses the grid voltage vector position, the errors of the instantaneous active and reactive powers. The second ANFIS block utilizes the dc link voltage error and its change. The purpose of the proposed control algorithm is to regulate perfectly dc link voltage, to control directly the instantaneous powers, near-sinusoidal source current waveforms and unity power factor (UPF) operation. The regulation method has been developed and simulated using MATLAB/Simulink environment. The numerical simulation results confirm the validity of the proposed control technique for three-phase PWM rectifiers and have proved excellent robustness and performance. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of the fully traffic adaptive signal control algorithm by Lämmer 2 in the agent-based transport simulation MATSim. The implementation is tested at an illustrative, single intersection scenario and compared to the results of Lammers MATLAB simulation. Plausibility of the self-controlled signals and overall results can be confirmed. Small deviations can be explained by differences in flow simulation and resolution of simulation time steps. In the simulation of the illustrative intersection, the adaptive control is proved to be stable and, overall, superior to a fixed-time control. Constant vehicle arrivals are simulated to show the performance of the control and its underlying sub-strategies. The expected behavior of the algorithm and its implementation are validated by analyzing queue lengths over time. The adaptive control significantly outperforms the fixed-time control for stochastic demand, where its ability to dynamically react to changes in flow becomes important. [ABSTRACT FROM AUTHOR]

The effectiveness of common algorithms for feedforward compensation of narrowband disturbance depends mainly on the model quality. To avoid this dependency several direct adaptive control algorithms without explicitly identified secondary path models have been developed over the last years. However an overview of their properties and a comparison of their performances in a standardized benchmark is still lacking. In this paper the three most promising algorithms are modified for narrowband feedforward vibration control for the use in rotating machinery. As in this application the reference signal is generated using the frequency measurement from a speed sensor it can be assumed that there is no coupling between reference measurement and the secondary path. First the algorithms are tested in simulation, then they are implemented on a test rig for active vibration control of unbalance induced rotor vibration. In simulation as well as for the test rig the performances of the algorithms are compared to each other. Advantages and drawbacks of the algorithms are discussed and practical instructions for implementation are given. The work is intended to serve as starting point and motivation for future research in this field of study. [ABSTRACT FROM AUTHOR]

In this paper, we address the design of a navigation control algorithm for the autonomous landing of a quadrotor on a moving mobile platform. The quadrotor's velocity is unknown, and we assume that it is equipped with an on-board downward-looking single camera for determining its position relative to the target. The proposed autonomous landing operation proceeds in three phases: the search phase, the homing phase, and the landing phase. To prepare for these phases, a safety sphere with a desired radius having the target position as its center is constructed along with a virtual target point that can move on its surface. During the docking procedure, the quadrotor is first commanded to reach the virtual target point; as it approaches this target point, a second stage is initiated to provide the quadrotor with more precise guidance to land safely on the mobile platform. The core design of the landing algorithm is proposed based on a concise adaptive tracking control scheme using backstepping and dynamic surface control. Simulations are carried out to validate the effectiveness of the proposed guidance landing control approach. [ABSTRACT FROM PUBLISHER]

In this paper, active control of impulsive noise with symmetric α -stable ( SαS ) distribution is studied. A general step-size normalized filtered- x Least Mean Square (F x LMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation. [ABSTRACT FROM AUTHOR]

In this study, we propose a positive adaptive control approach for spacecraft in proximity. The new control law aims to track a predefined path under various uncertainties, such as physical parameters of the vehicle and others persisting in the external environment. Besides, the proposed method also considers the divergent performance and the nature of monodirectionality of the thrusters to ensure each control command is strictly nonnegative. In other words, the proposed method is capable of not only compensating for various uncertainties but also automatically allocating active thrusters with strictly positive manners. The numerical simulation results demonstrate the potential of the proposed algorithm by comparing the magnitude of fluctuation of the states concerning the activation of the adaptive scheme. Considering the findings, we attempt to perform a hardware experiment by utilizing the recently developed air-bearing-based hardware-in-the-loop spacecraft test facility. The results of the experiment clearly suggest the practical effectiveness of the proposed controller by showing that individual thrust magnitudes are manipulated based on the estimation of scale factor errors and other external uncertainties under their boundaries. [ABSTRACT FROM AUTHOR]

We consider adaptive optimal tracking problems for a minimum-phase discrete-time plant with uncertainty in the output channel and bounded exogenous disturbance. The coefficients of the equation of the linear time-invariant nominal model, the uncertainty gain, and the upper bound of the exogenous disturbance are assumed to be unknown, and the worst-case asymptotic tracking error serves as the performance criterion. It is shown that the statement of the optimal problem depends on a priori information about the bounded control. The solution of the problems is based on the polyhedral estimation of all unknown parameters and the use of the performance factor of the tracking problem as an identification criterion for calculating the current optimal estimates. The performance and efficiency of the proposed control algorithms is illustrated by the results of numerical simulation. [ABSTRACT FROM AUTHOR]

The crux in designing the PID controller lies in determining its gain values, which play a major role in deciding its performance. The gains are fed as inputs to the controller and are to be decided before its run. On the other hand, the effectiveness of the biped walk purely depends on the performance of the PID controller. Initially, the upper and lower body gaits of the two-legged robot are determined using the concept of inverse kinematics. Further, the dynamics of the biped robot is derived by using Lagrange–Euler formulation. The main objective of the present research is to decide the gains of the torque-based PID controller with the help of a neural network trained by using nature-inspired optimization algorithms, namely MCIWO and PSO. The adaptiveness of the algorithm lies in modifying the gains of the controller based on the magnitude of the error in the angular displacement received at the input to the NN. Once the controller is developed, its effectiveness is tested in computer simulations. Finally, the optimum controlled gait angles obtained by the best approach are tested on a real biped robot. [ABSTRACT FROM AUTHOR]

Abstract: This work presents an accurate algorithm for solving the chaotic control (optimal and adaptive control) problems of hyperchaotic finance system. A modification of the spectral homotopy analysis method technique is proposed for solving the hyperchaotic finance system and the extreme conditions obtained from the Pontryagin minimum principle. The proposed method is based on implementing the spectral homotopy analysis method on a sequence of multiple intervals thereby increasing its radius of convergence to yield highly accurate method that is referred to as the piecewise spectral homotopy analysis method. Finally, piecewise spectral homotopy analysis method is used to solve the problem of Lyapunov stabilization of the hyperchaotic finance system. Numerical simulations are given to illustrate and verify effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of finite-time tracking control for underactuated surface vessels (USVs) through sliding-mode control algorithms with external disturbances. Considering the nonexistence of relative degree caused by the underactuated property, the initial tracking error system is firstly transformed to a high order system for the possibility of applying a sliding-mode control algorithm. Subsequently, a finite-time controller based on an integral sliding surface (ISMS) is designed to achieve trajectory tracking. With the aid of this controller, the tracking errors converge to a steady state in a finite time. In contrast to the backstepping-based approach, the proposed method makes it possible to integrate controller design of position tracking and attitude tracking in one step, thus ensuring simplicity for implementation. Finally, theoretical analysis and numerical simulations are conducted to confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]