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This paper aims to present an adaptive robust sliding mode tracking controller for a 6 degree-of-freedom industrial assembly robot with parametric uncertainties and external disturbances. The controller is used to achieve both stringent trajectory tracking, accurate parameter estimations and robustness against external disturbances.

The controller is designed based on the combination of sliding mode control, adaptive and robust controls and hence has good adaptation and robustness abilities to parametric variations and uncertainties. The unknown parameter estimates are updated online based on a discontinuous projection adaptation law. The robotic dynamics is first formulated in both joint spaces and workspace of the robot’s end-effector. Then, the design procedure of the adaptive robust sliding mode tracking controller and the parameter update law is detailed.

Comparative tests are also conducted to verify the effectiveness of the proposed controller, which show that the proposed controller achieves significantly better dynamic trajectory tracking performances as compared with conventional proportional derivative controller and sliding mode controller under the same conditions.

This is a new innovation for industrial assembly robot to improve assembly automation.

The purpose of this paper is to propose an adaptive robust controller for coupled attitude and orbit control of rigid spacecraft based on dual quaternion in the presence of external disturbances and model uncertainties.

First, based on dual quaternion, a theoretical model of the relative motion for rigid spacecraft is introduced. Then, an adaptive robust controller which can realize coordinated control of attitude and orbit is designed in the existence of external disturbances and model uncertainties.

This paper takes advantage of the Lyapunov function which can guarantee the asymptotic stabilization of the whole system in the existence of parameters uncertainties. Simulation results show that the proposed controller is feasible and effective.

This paper proposes a coupled attitude and orbit adaptive robust controller based on dual quaternion. Simulation results demonstrate that the proposed controller can achieve higher control performance in the presence of parameters uncertainties.

The purpose of this paper is to present μ-synthesis-based robust attitude trajectory tracking control of three degree-of-freedom four rotor hover vehicle.

Comprehensive modelling of hover vehicle is presented, followed by development of uncertainty model. A μ-synthesis-based controller is designed using the DK iteration method that not only handles structured and unstructured uncertainties effectively but also guarantees robust performance. The performance of the proposed controller is evaluated through simulations, and the controller is also implemented on experimental platform. Simulation and experimental results validate that μ-synthesis-based robust controller is found effective in: solving robust attitude trajectory tracking problem of multirotor vehicle systems, handling parameter variations and dealing with external disturbances.

Performance analysis of the proposed controller guarantees robust stability and also ensures robust trajectory tracking performance for nominal system and for 15-20 per cent variations in the system parameters. In addition, the results also ensure robust handling of wind gusts disturbances.

This research addresses the robust performance of hover vehicle’s attitude control subjected to uncertainties and external disturbances using μ-synthesis-based controller. This is the only method so far that guarantees robust stability and performance simultaneously.

The requirement of robust cooperative control is essential to achieve consensus between unmanned aerial vehicles (UAVs) operating in swarm formation. Often the performance of these swarm formations is affected by wind gust disturbances. This study proposes an effective robust consensus protocol, which will ensure the UAVs in swam formation to collectively meet the desired objective in real-time scenario.

In this work, the swarm of UAVs are modeled as multiagent systems by using the concepts of algebraic graph theory. To address the challenges of a complex and dynamic environment, an adaptive sliding mode control (SMC)-based consensus protocol is proposed. The closed loop stability analysis is established through Lyapunov theory.

The efficacy of the discussed robust consensus controller is analyzed through numerical simulations. Further, the quantitative analysis using Monte-Carlo simulations validates performance of the proposed robust consensus protocol. The presented consensus protocol can be easily implementable as robust flight controller for swarm of UAVs. Also, as the consensus theory is based on the algebraic graph theory, the proposed design is scalable for a large number of UAVs in swarm formation.

The proposed adaptive SMC achieves robust consensus of longitudinal dynamics states between all the UAVs by mitigating the effects of wind gust disturbances. Also, the adaptive SMC offers chattering-free control efforts.

The purpose of this paper is to show the application of an L₁ adaptive controller to control an autonomous underwater vehicle (AUV), considering realistic perturbations.

In this paper, an L₁ adaptive controller is proposed to control the pitch channel of an AUV, for the first time. Based on a six degree of freedom (6-DOF) nonlinear equations, an appropriate linear model considering real perturbations is derived for the pitch channel of AUV. Then L₁ adaptive controller is applied on the model in the presence of the bounded disturbances and uncertainties. For this purpose, verified parameters of the REMUS AUV have been considered. Several simulations are performed in different operating conditions.

The results confirm the quality of the proposed method in various situations. Furthermore, it is shown that the L₁ adaptive controller is potential to contribute in practical applications of AUVs such as other autonomous systems.

This is the first time that this method has been applied to an AUV.

The aim of this paper is to design a nonlinear disturbance observer-based control (DOBC) method obtained by patching a control method developed using a robust adaptive technique and a DO.

For designing a DOBC, initially a class of nonlinear system is considered with an external disturbance. First, a DO is designed to estimate the external disturbances. This estimate is combined with the controller to reject the disturbances and obtain the desired control objective. For designing a controller, the robust sliding mode control theory is used. Furthermore, instead of using a constant switching gain, an adaptive gain tuning criterion is designed using Lyapunov candidate function. To investigate the stability and effectiveness of the developed DOBC, stability analysis and simulation study are presented.

The major findings of this paper include the criteria of designing the robust adaptive control parameters and investigating the disturbance rejection when robust adaptive control based DOBC is developed.

In practice, the flight of quadrotor is affected by different kind of external disturbances, thus leading to the change in dynamics. Hence, it is necessary to design DOBCs based on robust adaptive controllers such that the quadrotor model adapts to the change in dynamics, as well as nullify the effect of disturbances.

Designing DOBCs based on robust control method is a common practice; however, the robust adaptive control method is rarely developed. This paper contributes in the domain of DOBC based on robust adaptive control methods such that the behavior of controller varies with the change in dynamics occurring due to external disturbances.

The purpose of this paper is to address the trajectory tracking control in task space of a non‐holonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm is robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. The system stability and the convergence of tracking errors to zero are rigorously proved using a Lyapunov theory.

The proposed algorithm is derived based on the advantage of the robot regressor dynamics that express the highly non‐linear robot dynamics in a linear form in terms of the known and unknown robot parameters. The update law for the unknown dynamic parameters is obtained using Lyapunov theory.

Simulation experiments show the effectiveness of the proposed robust adaptive based controller in comparison with a classical passivity based controller.

The proposed adaptive approach is interesting for the control of the mobile manipulators in the task space coordinate even in the presence of dynamic uncertainties and external disturbances.

The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties.

The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem.

The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed.

The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.

To develop a simplified robust control scheme for a class of nonlinear time‐varying uncertain chaotic systems.

By means of input‐to‐state stability theory, a new robust adaptive control scheme is designed, which is simpler than the one proposed by Li et al. and applicable to a larger class of nonlinear systems. Only one parameter is adjusted in the controller and the scheme assures that all the signals remain bounded. The behavior of the proposed control scheme is also analyzed through simulations on the Rössler system.

By adjusting only one parameter in the controller and imposing only one mild assumption on the time‐varying parameters, the proposed control algorithm assures that all the signal remain bounded and that the state of the original system will follow a desired trajectory defined either by the trajectory and its first time derivative, or given by a reference model.

The results are limited to a particular class of nonlinear systems where the dimension of the input vector is equal to the order of the system (dimension of the state vector).

The main advantage of the proposed method is that the modification introduced leads to a substantially simpler adaptive robust controller whose practical implementation will be easier.

The contribution of the proposed method is in the simplification of the control algorithm applied to a class of nonlinear time‐varying uncertain chaotic systems. This will be useful for control engineers to control complex industrial plants.

The proportional‐integral‐derivative (PID) controller has been a practical application in industry due to its simple architecture, being easily designed and its parameter tuning without complicated computation. However, the traditional PID controller usually needs some manual retuning before being used for practical application in industry. The purpose of this paper is to propose an auto‐tuning PID controller (ATPIDC) which can automatically tune the controller parameters based on the gradient descent method and the Lyapunov stability theorem. Finally, a field‐programmable gate array (FPGA) chip is adopted to implement the proposed ATPIDC scheme for possible low‐cost and high‐performance industrial applications, and it is applied to a DC servomotor to show its effectiveness.

To ensure the stability of the intelligent control system, a compensator usually should be designed. The most frequently used compensator is designed as a sliding‐mode control, which results in substantial chattering in the control effort. To tackle this problem, the proposed ATPIDC system is composed of a PID controller and a fuzzy compensator. The PID controller can automatically tune the gain factors of the controller gains based on the gradient descent method, and the fuzzy compensator is utilized to eliminate approximation error based on the Lyapunov stability theorem. The proposed fuzzy compensator not only can remove the chattering phenomena of conventional sliding‐mode control completely, but also can guarantee the stability of the closed‐loop system.

The proposed ATPIDC system is applied to a DC servomotor on a FPGA chip. The hardware implementation of the ATPIDC scheme is developed in a real‐time mode. Using the FPGA to implement, the ATPIDC system can achieve the characteristics of small size, fast execution speed and less memory. A comparison among the fuzzy sliding‐mode control, adaptive robust PID control and the proposed ATPIDC is made. Experimental results verify a better position tracking response can be achieved by the proposed ATPIDC method after control parameters training.

The proposed ATPIDC approach is interesting for the design of an intelligent control scheme. An on‐line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable tracking performance and no chattering phenomena can be achieved by using the proposed ATPIDC system. Also, the proposed ATPIDC methodology can be easily extended to other motors.