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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 aim of this research paper is to design a disturbance observer (DO)-based robust adaptive tracking control of uncertain nonlinear system subject to unknown nonlinear disturbance.

To achieve desired control objectives, i.e. nonlinear trajectory tracking and disturbance attenuation, firstly, a control scheme is designed based on the adaptive criteria integrated in sliding mode control (SMC). In the second step, the disturbance estimation criterion is designed followed by patching with the controller obtained in the first step. Following the control development, using the Lyapunov candidate function, the stability criterion is ensured by designing appropriate adaptive gains.

In this paper, a robust adaptive nonlinear tracking method is presented. The findings includes the design of adaptive gains for the control parameters involved in the robust SMC technique, i.e. adaptive criterion is designed for the switching gain as well as for the gain used in sliding mode surface. Furthermore, a disturbance estimation criterion is developed to attenuate nonlinear disturbances with variable frequency and magnitude. Finally, the disturbance estimation scheme is combined with the control technique to obtain DO-based control (DOBC) algorithm.

Sliding mode control is a powerful robust control method. And, combining it with the DO achieves the control objectives of plants subject to disturbances and uncertainties. However, usually the uncertainties and disturbances are unknown and time varying. Thus, during practical implementation, designing the standard SMC is a challenging task due to the constant gains involved in the control design. Hence, it is important to have a criterion which adapts to the varying dynamics of plants due to the uncertainties and disturbances for achieving practical implementation of the control system.

Sliding mode control has been widely used for achieving the desired control objectives and robustness in the close-loop nonlinear systems. Besides, the SMC technique has been combined with the DOs as well. However, mostly the ideal conditions were considered during these developments, which required the control gains to be designed simply by manual tuning appropriately. However, by considering the real-time dynamics, uncertainties and disturbances, the constant control gain criteria can fail. Furthermore, due to external and internal disturbances, the model plant can vary with time. Thus, it is important to design the adaptive criteria for the control gains in DOBC schemes.

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 present the radial basis function (RBF) networks‐based adaptive robust control for an omni‐directional wheeled mobile manipulator in the presence of uncertainties and disturbances.

First, a dynamic model is obtained based on the practical omni‐directional wheeled mobile manipulator system. Second, the RBF neural network is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using the learning ability of neural networks, RBFNARC can co‐ordinately control the omni‐directional mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the sliding mode control.

Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

In this paper, an adaptive robust control scheme using neural networks combined with sliding mode control is proposed for crawler‐type mobile manipulators in the presence of uncertainties and disturbances. RBF neural networks approximate the system dynamics directly and overcome the structured uncertainty by learning. Based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimate boundedness of the tracking error are all strictly guaranteed.

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 aim of this paper is the implementation and validation of control and guidance algorithms for unmanned aerial vehicle (UAV) autopilots.

The path-following control of the UAV can be separated into different layers: inner loop for pitch and roll attitude control, outer loop on heading, altitude and airspeed control for the waypoints tracking and waypoint navigation. Two control laws are defined: one based on proportional integrative derivative (PID) controllers both for inner and outer loops and one based on the combination of PIDs and an adaptive controller.

Good results can be obtained in terms of trajectory tracking (based on waypoints) and of parameter variations. The adaptive control law guarantees smoothing responses and less oscillations and glitches on the control deflections.

The proposed controllers are easily implementable on-board and are computationally efficient.

The algorithm validation via hardware in the loop simulations can be used to reduce the platform set-up time and the risk of losing the prototype during the flight tests.

The uncertainty and nonlinearity are the challenging problems for the control of a nonholonomic wheeled mobile robot. To overcome these problems, many valuable methods have been proposed by using two control loops namely the kinematic control and the torque control so far. In majority of the proposed approaches the dynamics of actuators is omitted for simplicity in the control design. This drawback degrades the control performance in high-velocity tracking control. On the other hand, to guarantee stability and overcome uncertainties, the control methods become computationally extensive and may be impractical due to using all states. The purpose of this paper is to design a simple controller with guaranteed stability for overcoming the nonlinearity, uncertainty and actuator dynamics.

The control design includes two control loops, the kinematic control loop and the novel dynamic control loop. The dynamic control loop uses the voltage control strategy instead of the torque control strategy. Feedbacks of the robot orientation, robot position, robot linear and angular velocity, and motor currents are given to the control system.

To improve the precision, the dynamics of motors are taken into account. The most important advantages of the proposed control law is that it is free from the robot dynamics, thereby the controller is simple, fast response and robust with ignorable tracking error. The control approach is verified by stability analysis. Simulation results show the effectiveness of the proposed control applied on an uncertain nonholonomic wheeled mobile robot driven by permanent magnet dc motors. A comparison with an adaptive sliding-mode dynamic control approach confirms the superiority of the proposed approach in terms of precision, simplicity of design and computations.

The originality of the paper is to present a new control design for an uncertain nonholonomic wheeled mobile robot by using voltage control strategy in replace of the torque control strategy. In addition, a novel state-space model of electrically driven nonholonomic wheeled mobile robot in the workspace is presented.

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 purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle‐Torsion Spring (PM‐TS) for finger therapy. PM has complex nonlinear dynamics, which makes PM modelling difficult. To realize high‐accurate tracking for the robotic hand, an Echo State Network (ESN)‐based PID adaptive controller is proposed, even though the plant model is unknown.

To drive a single joint of rehabilitation robotic hand, the paper proposes a new PM‐TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Based on the novel actuator, a wearable robotic hand is designed. By employing the model‐free approximation capability of ESN, the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand. An ESN together with Recursive Least Square (RLS) is called a RLSESN, where the ESN output weight matrix is updated by the online RLS learning algorithm.

Practical experiments demonstrate the validity of the PM‐TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller. In addition, they also verify the effectiveness of the proposed rehabilitation robotic hand.

A new PM‐TS actuator configuration that uses a PM and a torsion spring for bi‐directional movement of joint is presented. By utilizing the new PM‐TS actuator, a novel wearable rehabilitation robotic hand for finger therapy is designed. Based on the unknown plant model, the RLSESN_PID controller is proposed to attain satisfactory performance.

The purpose of this paper is to solve the tracking problem for free-floating space manipulators (FFSMs) in task space with parameter uncertainties and external disturbance.

In this paper, the novel cerebellar model articulation controller (CMAC) is designed with the feedback controller. More precisely, the parameter uncertainties in the FFSM are considered for achieving the robustness.

By using the dynamically equivalent model, the CMAC can be designed and trained with the desired performance, such that the prescribed trajectory can be followed accordingly. The simulation results are presented for illustrating the validity of the derived results.

Based on the designed CMAC, the tracking error would be approaching zero by choosing appropriate quantization level in CMAC and the corresponding learning rules can be tuned online.