Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.

Modeling and analysis of hysteresis using the Maxwell-slip model for variable stiffness actuators

Hysteresis non-linearity in variable stiffness actuators(VSAs)causes significant torque errors and reduces the stability of the actuators,leading to poor human–computer interaction performance.At present,fewer hysteresis compensation models have been developed for compliant drives,so it is necessary to establish a suitable hysteresis model for compliant actuators.In this work,a new model with a combination of the Maxwell-slip model and virtual deformation is proposed and applied to an elbow compliant actuator.The method divides the periodic variation of the actuator into three parts:an ascending phase,a descending phase,and a transition phase.Based on the concept of virtual deformation,the nonlinear hysteresis curve is transformed into a polyline,and the output torque is estimated using the revised Maxwell-slip model.The simulation results are compared with the experimental data.Its torque error is controlled within 0.2Nm,which validates the model.An inverse model is finally established to calculate the deformation deflection angle for hysteresis compensation.The results show that the inverse model has high accuracy,and the deformation deflection is less than 0.15 rad.

New nonlinear stiffness actuator with predefined torque‒deflection profile

A nonlinear stiffness actuator(NSA)could achieve high torque/force resolution in low stiffness range and high bandwidth in high stiffness range,both of which are beneficial for physical interaction between a robot and the environment.Currently,most of NSAs are complex and hardly used for engineering.In this paper,oriented to engineering applications,a new simple NSA was proposed,mainly including leaf springs and especially designed cams,which could perform a predefined relationship between torque and deflection.The new NSA has a compact structure,and it is lightweight,both of which are also beneficial for its practical application.An analytical methodology that maps the predefined relationship between torque and deflection to the profile of the cam was developed.The optimal parameters of the structure were given by analyzing the weight of the NSA and the mechanic characteristic of the leaf spring.Though sliding friction force is inevitable because no rollers were used in the cam-based mechanism,the sliding displacement between the cam and the leaf spring is very small,and consumption of sliding friction force is very low.Simulations of different torque‒deflection profiles were carried out to verify the accuracy and applicability of performing predefined torque‒deflection profiles.Three kinds of prototype experiments,including verification experiment of the predefined torque‒deflection profile,torque tracking experiment,and position tracking experiment under different loads,were conducted.The results prove the accuracy of performing the predefined torque‒deflection profile,the tracking performance,and the interactive performance of the new NSA.

Robust adaptive control for uncertain nonlinear systems with unknown control directions and actuator nonlinearity

A robust adaptive control scheme is proposed for a class of uncertain nonlinear systems in strict feedback form with both unknown control directions and non-symmetric dead-zone nonlinearity based on backstepping design. The conditions that the dead-zone slopes and the boundaries are equal and symmetric are removed by simplifying nonlinear dead-zone input model, the assumption that the priori knowledge of the control directions to be known is eliminated by utilizing Nussbaum-type gain technique and neural networks （NN） approximation capability. The possible controller singularity problem and the effect of dead-zone input nonlinearity are avoided perfectly by combining integral Lyapunov design with sliding mode control strategy. All the signals in the closed-loop system are guaranteed to be semi-globally uniformly ultimately bounded and the tracking error of the system is proven to be converged to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the proposed control scheme.

Robust Adaptive Actuator Failure Compensation for a Class of Uncertain Nonlinear Systems

This paper presents a robust adaptive state feedback control scheme for a class of parametric-strict-feedback nonlinear systems in the presence of time varying actuator failures. The designed adaptive controller compensates a general class of actuator failures without any need for explicit fault detection. The parameters, times, and patterns of the considered failures are completely unknown. The proposed controller is constructed based on a backstepping design method. The global boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. The proposed approach is employed for a two-axis positioning stage system as well as an aircraft wing system. The simulation results show the correctness and effectiveness of the proposed robust adaptive actuator failure compensation approach.

Design of high performance linear feedback laws for operation that extends into the nonlinear region of AMB systems

Existing active magnetic bearings （AMBs） operate in the linear region of the magnetic material flux density, which limits the utilization of the bearing capacity. In order to increase the utilization of the bearing capacity and enhance the performance of the AMB system, this paper develops a method for designing high performance linear feedback laws. The resulting feedback laws allow the AMB to operate in its nonlinear region and hence improve the closed-loop performance. We first establish an approximate nonlinear AMB current force response model, and place this nonlinear curve inside a sector formed by two piecewise linear lines. Based on the linear line segments in these two piecewise linear lines, we determine the maximum disturbance that can be tolerated by solving an optimization problem with linear matrix inequality （LMI） constraints. For a given level of disturbance under the maximum tolerable disturbance, we formulate and solve the problem of designing the linear feedback that achieves the highest level of disturbance rejection as another LMI problem. Both g2 disturbances and g disturbances are considered. Finally, we illustrate our design by both simulation and experimental results.