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Autonomous underwater vehicles (AUVs) are highly nonlinear underactuated systems with uncertain dynamics and a challenging control problem. The main focus of this paper is to present a control law that shows desirable performance in the presence of modeling uncertainties. In this study, uncertainties are considered to be bounded and the AUV mathematical model is obtained in the presence of such uncertainties. Forces and torques applied to the AUV are also designed using a nonlinear dynamic controller. Appropriate adaptive rules are also presented to overcome system uncertainties and external disturbances. The adaptive nonlinear dynamic controller is designed based on upper bounds of system uncertainties and its stability is proven using the Lyapunov theory. In this article, the performance of the proposed control algorithm for tracking reference trajectories in an obstacle-rich environment is investigated. Therefore, the control algorithm is combined with potential fields for obstacle avoidance. Obtained results show the efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Rotor speed control of wind turbines is a key factor in achieving the maximum power of wind. It is known that a high-performance controller can significantly increase the amount of energy that can be captured from this source. The main problem regarding this issue is the lack of information about the correct dynamic model of the system. This uncertainty of the model is generally associated with unknown parameters (structured uncertainty) and/or external disturbances (unstructured uncertainty). Some adaptive and robust control approaches are developed in the literature in order to deal respectively with structured and unstructured uncertainties. In this paper, to compensate for both types of uncertainty, a robust controller, which includes an adaptive feedforward term, is proposed to track the optimal speed. In addition to considering the uncertainties, another advantage of the presented approach is that, using a smooth control effort, it provides global asymptotic tracking. A complete stability proof of the system is presented, and simulation results illustrate the effectiveness of the controller. [ABSTRACT FROM AUTHOR]

This paper studies relative pose control for a rigid spacecraft with parametric uncertainties approaching to an unknown tumbling target in disturbed space environment. State feedback controllers for relative translation and relative rotation are designed in an adaptive nonlinear robust control framework. The element-wise and norm-wise adaptive laws are utilized to compensate the parametric uncertainties of chaser and target spacecraft, respectively. External disturbances acting on two spacecraft are treated as a lumped and bounded perturbation input for system. To achieve the prescribed disturbance attenuation performance index, feedback gains of controllers are designed by solving linear matrix inequality problems so that lumped disturbance attenuation with respect to the controlled output is ensured in the L 2 -gain sense. Moreover, in the absence of lumped disturbance input, asymptotical convergence of relative pose are proved by using the Lyapunov method. Numerical simulations are performed to show that position tracking and attitude synchronization are accomplished in spite of the presence of couplings and uncertainties. [ABSTRACT FROM AUTHOR]

A realistic control paradigm should concurrently account for different sources of uncertainty such as those in modeling parameters, external disturbances and noise, as well as operational cost. Yet, this is a daunting task for which many current control approaches lack in one aspect or the other. In particular, the consumed control energy is an important aspect of controller design that is often ignored. In this paper, we propose a stable robust adaptive interval type-2 fuzzy H 2 /H ∞ controller (RAIT2FH 2 H ∞ C) for a class of uncertain nonlinear systems that aims to address the above concerns through its hybrid robust/adaptive structure. In particular, the H 2 energy and tracking cost function is minimized with respect to a H ∞ disturbance attenuation constraint, while the adaptive interval fuzzy logic system (IT2FLS) handles the uncertainties in approximating the unknown nonlinear dynamics of the system. In principle, the interval fuzzy logic approach aims to manage portions of uncertainty that could not be precisiated, leading to improved error performance. Several simulation studies, with or without disturbance and noisy measurements, as well as actual experimental implementation on a 3-PSP (prismatic-spherical-prismatic) parallel robot confirm this assessment. More specifically, in comparison with a competing methodology as well as its type-1 counterpart, the proposed interval type-2 strategy expends better or comparative control effort while reaching considerably better tracking performance. [ABSTRACT FROM AUTHOR]

This paper provides a solution for the position tracking and attitude synchronization problem of the close proximity phase in spacecraft rendezvous and docking. The chaser spacecraft must be driven to a certain fixed position along the docking port direction of the target spacecraft, while the attitude of the two spacecraft must be synchronized for subsequent docking operations. The kinematics and dynamics for relative position and relative attitude are modeled considering dynamic coupling, parametric uncertainties and external disturbances. The relative motion model has a new form with a novel definition of the unknown parameters. An original robust adaptive control method is developed for the concerned problem, and a proof of the asymptotic stability is given for the six degrees of freedom closed-loop system. A numerical example is displayed in simulation to verify the theoretical results. [ABSTRACT FROM AUTHOR]

Choosing an appropriate control scheme to alleviate nonlinearities and uncertainties is not a trivial task, especially when models are not easily available and practical evaluation provides the only means for actual performance assessment. Various factors can contribute to these nonlinearities and uncertainties, such as friction and stiction. Thus, this article investigates four different control schemes, namely PID, adaptive, conventional sliding mode control (SMC) and integral sliding mode control (ISMC) which are implemented in the Bristol Elumotion Robot Hand (BERUL) to analyse and overcome the aforementioned problems. The hand has five fingers with 16 joints and all fingers are underactuated. The implementation of the proposed control schemes are challenging since the BERUL fingers have significant friction, stiction and unknown parameters. The fingers are light in weight and fragile. Comparative performance characteristics have shown that the ISMC is the most suitable candidate to provide good experimental trajectory following and positioning control for underactuated BERUL fingers. [ABSTRACT FROM AUTHOR]

This brief describes the application of direct and indirect model reference adaptive control to a lightweight low-cost quadrotor unmanned aerial vehicle platform. A baseline trajectory tracking controller is augmented by an adaptive controller. The approach is validated using simulations and flight tested in an indoor test facility. The adaptive controller is found to offer increased robustness to parametric uncertainties. In particular, it is found to be effective in mitigating the effects of a loss-of-thrust anomaly, which may occur due to component failure or physical damage. The design of the adaptive controller is presented, followed by a comparison of flight test results using the existing linear and augmented adaptive controllers. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we study the cooperative global adaptive output regulation problem for a class of nonlinear uncertain multi-agent systems subject to an uncertain leader system. In comparison with the case where the leader system is known exactly, we need to combine adaptive control and robust control techniques to deal with the unknown parameters in the leader system. By employing a distributed internal model for the multi-agent system, we first convert the problem into a global adaptive robust stabilization problem of a distributed augmented system. Then we further solve the global adaptive stabilization problem of the augmented system by a distributed adaptive control law. Finally, we show that the unknown parameters in the leader system can be exactly estimated provided that the distributed internal model is of minimal order. [Copyright &y& Elsevier]

In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs). Stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented. The updated model is used to control the real system the next time feedback information is unavailable. Additionally, new estimation models are proposed that offer better convergence properties than typical state-space parameter estimation methods; common assumptions such as availability of persistently exciting inputs and estimation of only a canonical form of the system are relaxed. The implementation of upgraded models in MB-NCSs results in better usage of the network by allowing longer intervals without the need for measurement updates. [ABSTRACT FROM AUTHOR]

In this paper, robust adaptive boundary control is developed for a class of flexible string-type systems under unknown time-varying disturbance. The dynamics of the string system is represented by a nonhomogeneous hyperbolic partial differential equation (PDE) and two ordinary differential equations. Boundary control is proposed at the right boundary of the string based on the original distributed parameter system model (PDE) to suppress the vibration excited by the external unknown disturbance. Adaptive control is designed to compensate the system parametric uncertainty. With the proposed robust adaptive boundary control, all the signals in the closed-loop system are guaranteed to be uniformly ultimately bounded. The state of the string system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Simulations are provided to illustrate the effectiveness of the proposed control. [ABSTRACT FROM PUBLISHER]

This paper presents a global task coordinate frame (TCF) (GTCF)-based integrated direct/indirect adaptive robust contouring controller for linear-motor-driven biaxial systems that achieves both stringent contouring performance and accurate parameter estimations. In contrast to the past research works those use various locally defined TCFs “attached” to the desired contour to approximately calculate the contouring error for feedback controller designs, this paper first formulates the contouring control problem using a recently developed GTCF, in which calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical-model-based indirect-type parameter-estimation algorithm is then synthesized to obtain accurate online estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust controller with dynamic-compensation-type fast adaptation is also constructed to preserve the excellent transient and steady-state performance of the direct adaptive robust control (ARC) designs. Comparative experimental results show that the proposed GTCF-based integrated direct/indirect ARC algorithm not only achieves the best contouring performance but also possesses rather accurate estimations of physical parameters. [ABSTRACT FROM PUBLISHER]

This article presents a robust adaptive control scheme for a discrete-time plant that is subjected to both coprime factor perturbations and unknown exogenous disturbances. With the proposed control scheme, all the variables in the closed-loop system are bounded in the presence of the perturbations and disturbances, and an a priori computable upper bound on the size of the nonparametric dynamical uncertainty, for which stability is ensured, is provided. Moreover, one can guarantee a priori bound on the asymptotic performance of the overall adaptive system which is arbitrarily close to that of the corresponding nonadaptive control system. In addition, it is shown that the ℓ1 optimal robust controller design is continuous as a map from the plant to the optimal closed-loop solution. Furthermore, if the set of plants is compact, then the ℓ1 optimal robust controller design is uniformly continuous on the set of plants. These properties are necessary for analysing the interplay between identification and control in the overall adaptive system. [ABSTRACT FROM AUTHOR]

This paper proposes a robust self-organizing neural-fuzzy-control (RSONFC) scheme for a class of uncertain nonlinear multiple-input–multiple-output (MIMO) systems. We first develop a self-organizing neural-fuzzy network (SONFN) with concurrent structure and parameter learning. The fuzzy rules of SONFN are generated or pruned systematically. The proposed RSONFC scheme comprises an SONFN identifier, an uncertainty observer, and a supervisory controller. The SONFN identifier functions as the principal controller, and the uncertainty observer is designed to oversee uncertainties within the compound system. The supervisory controller combines sliding-mode control (SMC) and an adaptive bound-estimation scheme with various weights to achieve H_\infty tracking performance with a desired level of attenuation. Projection-type adaptation laws of network parameters developed using the Lyapunov's synthesis approach guarantee the stability of the overall control system. Simulation studies on a single-link flexible-joint manipulator and a two-link robot demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a robust intelligent tracking control (RITC) system employs an adaptive output recurrent cerebellar model articulation controller (ORCMAC) is developed for uncertain nonlinear system to achieve H ∞ tracking performance. The proposed dynamic structure of ORCMAC has superior capability in efficient learning mechanism and dynamic response. Temporal relations are embedded in conventional cerebellar model articulation controller (CMAC) by adding feedback connections between the output space and input space, so that the ORCMAC captures the system dynamic. In the RITC design, the Taylor linearization technique is employed to increase the learning ability of ORCMAC and the on-line adaptive laws are derived based on the Lyapunov stability analysis, the sliding mode control methodology and the H ∞ control technique so that the stability of the closed-loop system and H ∞ tracking performance can be guaranteed. Finally, the proposed control system is applied to control an inverted pendulum system, a Van der Pol oscillator and a Genesio chaotic system. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performances for the uncertain nonlinear systems with unknown dynamic functions and under the occurrence of external disturbance. [Copyright &y& Elsevier]

Abstract: We consider an artificial swarm system consisting of multi-agents. The agents may interact with each other based on their relative positions. Each agent exhibits a repulsion/attraction behavior toward another agent, which mimics some biological swarm systems. The performance of each individual agent is the accumulation of these respective considerations toward other agents. The overall performance of the artificial swarm system mimics the aggregation and formation in biological systems. We propose an adaptive robust control for each agent toward achieving the performance. The control can withstand uncertainty, which is time-varying, nonlinear, and without known bound. The controlled system converges to the desirable swarm system performance regardless of the uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we establish the robustness of adaptive controllers designed using the standard backstepping technique with respect to unmodeled dynamics involving unknown input time delay. While noting that some results on robust stabilization of non-minimum phase systems using the backstepping technique are available, we realize that the standard adaptive backstepping technique has only been shown applicable to unknown minimum phase systems. Another significance of our result is to enable the class of systems stablizable by adaptive backstepping controllers to cross the boundary of minimum phase systems, since systems with input time delay belong to non-minimum phase systems. Moreover, the and norms of the system output are also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters. [Copyright &y& Elsevier]

We develop a new adaptive gain-scheduling control scheme for continuous-time linear systems with polytopic uncertainties. The gain-scheduled control law is proposed as a convex sum of a fixed set of controller gains, exploiting the polytopic representation of the system uncertainty, which is not possible with classical robust control results in the literature. To realize this scheme, an adaptation law is proposed to adaptively provide the tuning parameter for the gain-scheduling implementation. The admissible domain of the stabilizing control feedback gains, defined by the fixed set of controller gains, can be determined offline by solving a set of linear matrix inequality constraints over a scalar line search. Using Lyapunov-based arguments, the proposed design conditions and the adaptation law ensure that all closed-loop signals are bounded. In particular, if the uncertain parameters are not time-varying, then the system states asymptotically converge to the origin. Theoretical arguments and appropriate numerical illustrations are provided to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

• The exponential decay function is proposed as the reference model to guarantee speed error decay rapidly to zero. • By introducing the estimation of unknown disturbance boundary in the control law and the robust term in the updating law of parameter, the robustness is improved. • The convergence parameters are adjusted to improve the adaptive speed of parameters. • The steering load disturbances are divided into predictable time-varying term and bounded unknown time-varying disturbances to reduce the scope of uncertainty. Ensuring passenger comfort in steering process of autonomous vehicles is a key issue in corresponding product development. However, the existing steering control system has an inability to plan steering speed, which will increase the discomfort of passengers. In this paper, a novel reference model based adaptive control tactic is proposed to regulate steering speed of automatic steering system (ASS). In order to improve the response speed and control accuracy of ASS, firstly, exponential decay function is creatively proposed as a reference model to control the decay rate of the speed error of steering motor. Subsequently, to reduce the system uncertainty range, the steering load disturbance is treated as a known time-varying term and a bounded unknown time-varying disturbance. In order to compensate the uncertainties of nonlinear system, the additional estimation of disturbance boundary and the robust term in the parameters updating law are introduced. Besides, the stability and robustness of the whole system are verified by Lyapunov theory. Finally, simulation and HIL test results demonstrate the proposed control tactic ensures the tracking performance of steering speed control and the robust stability in the bounded unknown time-varying disturbance, which provides benefit reference for ASS development in practice. [ABSTRACT FROM AUTHOR]