An adaptive output feedback tracking control of robot manipulator via fuzzy logic based compensation

A new adaptive controller for robot manipulators is proposed. The new approach uses only position measurements. The main conclusions derived from the closed-loop system analysis are in two main results. In the first one, the global convergence of the position and velocity tracking errors is stated by using a condition that relates the viscous friction damping and the desired joint speed. In the second one, such a condition is dropped out but the local exponential stability of the closed-loop system is shown. To confirm the theoretical conclusions, a detailed experimental study in a two degrees-of-freedom direct-drive manipulator is provided, where the performance of the new controller is compared with respect to a known output feedback adaptive controller.

The trajectory tracking problem of a class of robot manipulators is investigated by a simpler design adaptive neural network(NN) in this paper. The Radial Basis Function(RBF) NN is utilized to handle the uncertainties of the dynamics. Compared with the traditional schemes, the dimension of the input vectors of RBFNN is decrease from $4n$ to $3n$ but it have equal tracking and approximation performances. The output feedback control is considered when the velocity information cannot be obtained. Moreover, the weights of RBFNN converge to its optimal value by using the auxiliary filter to estimate weights error. The robot manipulator system is semi-globally and uniformly bounded which is proved by Lyapunov's theory. Simulation results demonstrate that the simpler controller has the same capability compared with the non-simplified method.

Adaptive control of robot manipulators based on the use of a sliding variable and the passivity property of the dynamic equations was originally designed and successfully applied to task tracking in joint space few decades ago. Surprisingly, no extension is available to date for the multi-task case such that the controller tends towards enforcing strict task priorities as the parameters tend to their real values. Given the importance of both multi-task control and adaptive algorithms, an approach that deals with this situation has an important impact in the robotic field. This letter provides a solution to this problem leveraging on our recent formulation of hierarchical multi-task impedance control for trajectory tracking and on geometric methods for model identification. Experiments are used to validate the stability analysis.

Adaptive fuzzy tracking control for a class of uncertain nonaffine nonlinear systems with dead-zone inputs

A switched adaptive controller is designed for robot manipulators with friction and changing loads. The nonlinear friction is depicted by a nonlinear friction model, and a switched nonlinear system is used to model the parameter jump caused by load change. Hyperstability theory is used in the designing procedure, which provides more options for adaptive laws than Lyapunov theory. In the presence of friction and changing loads, asymptotic tracking is achieved under arbitrary switching, which is not able to accomplish by a non-switched adaptive controller. The proposed method is validated by a simulation of a 2 degree of freedom manipulator.

Adaptive path tracking control of robotic manipulators with unknown payload dynamics

A neural network (NN)-based adaptive control law is proposed for the tracking control of an n-link robot manipulator with unknown dynamic nonlinearities. Basis-function-like networks are employed to approximate the plant nonlinearities, and the bound on the NN reconstruction error is assumed to be unknown. The proposed NN-based adaptive control approach integrates the NN approach and an adaptive implementation of the discrete variable structure control, with a simple estimation mechanism for the upper bound on the NN reconstruction errors and an additional control input as a function of the estimate. Lyapunov stability theory is used to prove the uniform ultimate boundedness of the tracking error, and simulation results demonstrate the applicability of the proposed method to achieve desired performance.

In this paper, a controller for robot manipulators is proposed to guarantee the tracking error of the systems bounded by predefined decreasing boundary. In this control scheme, a multi-layer neural network is used to approximate the unknown non-linear items, and the robustifying control term is used to compensate the approximation errors. The adaptive laws of weights of the neural network and robustifying control term are derived based on the Lyapunov stability analysis, so that, under appropriate assumptions, the transient and steady-state error bounds can be guaranteed. Compared with the existing work, the adaptable parameters in the proposed method do not need an off-line training procedure for better approximation. Simulations performed on a two-link robot manipulator illustrate the developed controller and demonstrate its performance.

A novel approach is presented for adaptive trajectory tracking of robot manipulators using a three-stage hierarchical neural network model involving Support Vector Machines (SVM) and an adaptive unsupervised Neural Network. It involves a novel adaptive Self Organizing feature map (SOFM) in the first stage which aims at clustering the input variable space into smaller sub-spaces representative of the input space probability distribution and preserving its original topology, while rapidly increasing, on the other hand, cluster distances. Moreover, its codebook vector adaptation rule involves m-winning neurons dynamics and not the winner takes all approach. During convergence phase of the map a group of Support Vector Machines, associated with its codebook vectors, is simultaneously trained in an online fashion so that each SVM learns to positively respond when the input data belong to the topological sub-space represented by its corresponding codebook vector, taking into account similarity with that codebook vector. Moreover, it learns to negatively respond to input data not belonging to such a previously mentioned corresponding topological sub-space. The proposed methodology is applied, with promising results, to the design of a neural-adaptive trajectory tracking controller, by involving the computer-torque approach, which combines the proposed three-stage neural network model with a classical servo PD feedback controller. The results achieved by the suggested hierarchical SVM approach are favorably compared to the ones obtained by traditional (PD) and non-hierarchical neural network based controllers.

The control objective in many robot manipulator applications is to command the end-effector motion to achieve a desired response. To achieve this objective a mapping is required to relate the joint/link control inputs to the desired Cartesian position and orientation. If there are uncertainties or singularities in the mapping, then degraded performance or unpredictable responses by the manipulator are possible. To address these issues, an adaptive tracking controller is developed in this paper for robot manipulators with uncertainty in the kinematic and dynamic models. The controller is developed based on the unit quaternion representation so that singularities associated with three parameter representations are avoided.

An adaptive fuzzy logic controller is proposed for trajectory tracking control of robot manipulators. The approach uses quantitative control schemes to ensure global stability and qualitative control schemes to approximate any nonlinear functions caused by disturbances, system uncertainties and interconnections. With the PD control as a preliminary component in maintaining the local stability, we add nonlinear feedback to ensure global stability of the entire system. An adaptive fuzzy logic controller is incorporated into the robot arm control system as a function approximator to compensate the interconnections effect, unmodeled dynamics, friction, gravity force and uncertainties. The stability criteria of the proposed controller is developed using the Lyapunov synthesis approach. Computer simulations show that the proposed adaptive fuzzy controller outperforms traditional control approaches.

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.

This paper mainly focuses on output feedback practical tracking controller design for stochastic nonlinear systems with polynomial function growth conditions. Mostly, there are some studies on output feedback tracking control problem for general nonlinear systems with parametric certainty in existing achievements. Moreover, we extend it to stochastic nonlinear systems with parametric uncertainty and system nonlinear terms are assumed to satisfy polynomial function growth conditions which are more relaxed than linear growth conditions or power growth conditions. Due to the presence of unknown parametric uncertainty, an output feedback practical tracking controller with dynamically updated gains is constructed explicitly so that all the states of the closed-loop systems are globally bounded and the tracking error belongs to arbitrarily small interval after some positive finite time. An example illustrates the efficiency of the theoretical results.

This paper studies the observer-based secure adaptive output feedback orbit tracking control issue for spacecraft systems with false data injection (FDI) attacks. By means of the inertial-line-of-sight coordinate system using relative distance, line-of-sight inclination and deviation angles, the model of spacecraft orbit system is constructed. An observer is designed to estimate unknown system states, thus improving the accuracy of the control system. The mechanism of parameter adaptive law is utilised to estimate the upper bound of false data injection attacks. Based on the back-stepping technique, the recursive scheme is developed to establish virtual and actual control laws step by step. The proposed secure adaptive output feedback tracking control scheme can not only guarantee that the estimation error and the tracking error converge to a small neighbourhood of the origin, but also ensure that the spacecraft orbit closed-loop system is uniformly ultimately bounded via the Lyapunov function. Finally, the effectiveness of the proposed scheme is demonstrated by a numerical simulation example.

This paper introduces an optimal tracking controller for robot manipulators with asymmetrically saturated torques and partially - unknown dynamics based on a reinforcement learning method using a neural network. Firstly, the feedforward control inputs are designed based on the backstepping technique to convert the tracking control problem into the optimal tracking control problem. Secondly, a cost function of the system with asymmetrically saturated input is defined, and the constrained Hamilton-Jacobi-Bellman equation is built, which is solved by the online reinforcement learning algorithm using only a single neural network. Then, the asymmetric saturation optimal control rule is determined. Additionally, the concurrent learning technique is used to relax the demand for the persistence of excitation conditions. The built algorithm ensures that the closed-loop system is asymptotically stable, the approximation error is uniformly ultimately bounded (UUB), and the cost function converges to the near-optimal value. Finally, the effectiveness of the proposed algorithm is shown through comparative simulations.