On the stability of ADRC for manipulators with modelling uncertainties

Active disturbance rejection control with fractional-order model-aided extended state observer

Increasing the observer bandwidth of the extended state observer (ESO) can significantly enhance the disturbance rejection performance of the active disturbance rejection control (ADRC). However, a large observer bandwidth can also amplify the noise and degrade the control quality. This study proposes a novel fractional-order ADRC (FOADRC) approach. A fractional-order model-aided extended state observer (FOMESO) is designed by leveraging available plant information. Under a given observer bandwidth, the disturbance estimation performance of FOMESO is improved, while noise sensitivity is not aggravated. Additionally, FOMESO transforms a typical second-order plant into a fractional-order double-integrator model, reducing the phase lag to below 180∘. Consequently, the derivative component, which is sensitive to noise, is unnecessary in the feedback controller. Simulation and comprehensive comparisons demonstrate that the proposed FOADRC scheme outperforms the traditional ADRC in tracking, noise sensitivity, disturbance rejection, and stability. Furthermore, the proposed FOADRC approach is robust to plant parameter variations. The proposed method is validated through experiments on a permanent magnet synchronous motor speed servo system, confirming its superiority and effectiveness.

Neural Adaptive Dynamic Surface Asymptotic Tracking Control of Hydraulic Manipulators With Guaranteed Transient Performance

In this article, a novel neural network (NN)-based adaptive dynamic surface asymptotic tracking controller with guaranteed transient performance is proposed for n -degrees of freedom (DOF) hydraulic manipulators. To fulfill the work, the entire manipulator system model, including hydraulic actuator dynamics, is first established. Then, the neural adaptive dynamic surface controller is designed, in which the NN is utilized to approximate the unknown joint coupling dynamics, while the approximation error and uncertainties of the actuator dynamics are addressed by the nonlinear robust control law with adaptive gains. In addition, a modified funnel function that ensures the joint tracking errors remains within a predefined funnel boundary and is skillfully incorporated into the adaptive dynamic surface control (ADSC) design to achieve a guaranteed transient tracking performance. The theoretical analysis reveals that both the guaranteed transient tracking performance and asymptotic stability can be achieved with the proposed controller. Contrastive simulations are performed on a 2-DOF hydraulic manipulator to demonstrate the superiority of the proposed controller.

Extended state observer based dynamic iterative learning for trajectory tracking control of a six-degrees-of-freedom manipulator

With the development of industrial automation comes an ever, broadening number of application scenarios for manipulators along with increasing demands for their precise control. However, manipulator trajectory tracking control schemes often exhibit problems such as those related to high levels of coupling, complex calculations, and in various difficulties in application for industrial environments. For the problems of low accuracy in control and poor robustness of multiple-jointed robotic trajectory tracking, iterative learning control (ILC) with model compensation (MC) based on extended state observer (ESO) has been proposed for the trajectory tracking control of six-degrees-of-freedom (six-DOF) manipulators. The scheme has excellent features to overcome uncertainties in repetitive tasks, including unknown bounded perturbations that are external to the model or dynamic perturbations that are internal to the model. The proposed control strategy combines ESO, iterative learning, and MC, for precise control of trajectory tracking. Here, ESO is used to estimate disturbances, iterative learning allows fast and accurate control in repeated tasks, and the model-compensated control algorithm alleviates the necessary for many inverse operations. The convergence of our proposed control scheme is proved through Lyapunov function and time-varying approximation theory. Simulation and experimental results verify the validity of the proposed scheme.

Novel Adaptive Extended State Observer for Dynamic Parameter Identification with Asymptotic Convergence

In this paper, a novel method of parameter identification of linear in parameter dynamic systems is presented. The proposed scheme employs an Extended State Observer to online estimate a state of the plant and momentary value of total disturbance present in the system. A notion is made that for properly redefined dynamics of the system, this estimate can be interpreted as a measure of modeling error caused by the parameter uncertainty. Under this notion, a disturbance estimate is used as a basis for classic gradient identification. A global convergence of both state and parameter estimates to their true values is proved using the Lyapunov approach under an assumption of a persistent excitation. Finally, results of simulation and experiments are presented to support the theoretical analysis. The experiments were conducted using a compliant manipulator joint and obtained results show the usefulness of the proposed method in drive control systems and robotics.

Periodic event-triggered sliding mode control for lower limb exoskeleton based on human-robot cooperation

This paper presents a periodic event-triggered sliding mode control (SMC) scheme based on human-robot cooperation for lower limb exoskeletons. Firstly, a Genetic Algorithm-Back propagation (GA-BP) neural network is proposed to estimate the motion intention of the wearer through electromyography (EMG) signals. Secondly, the periodic event-triggered SMC strategy based on tanh function is designed to ensure the asymptotic convergence of the exoskeleton system and save communication resources, where the detailed expressions of sampling period and control gain are designed. Finally, comparative simulation and experimental analysis is presented to verify the effectiveness of the proposed control method.

Cascade extended state observer for active disturbance rejection control applications under measurement noise

The extended state observer (ESO) plays an important role in the design of feedback control for nonlinear systems. However, its high-gain nature creates a challenge in engineering practice in cases where the output measurement is corrupted by non-negligible, high-frequency noise. The presence of such noise puts a constraint on how high the observer gains can be, which forces a trade-off between fast convergence of state estimates and quality of control task realization. In this work, a new observer design is proposed to improve the estimation performance in the presence of noise. In particular, a unique cascade combination of ESOs is developed, which is capable of fast and accurate signals reconstruction, while avoiding over-amplification of the measurement noise. The effectiveness of the introduced observer structure is verified here while working as a part of an active disturbance rejection control (ADRC) scheme. The conducted numerical validation and theoretical analysis of the new observer structure show improvement over standard solution in terms of noise attenuation.

Analysis of an Impact of Inertia Parameter in Active Disturbance Rejection Control Structures

This paper is focused on a distributed control of fully actuated manipulators under operating conditions when dynamic couplings between their joints are insignificant. The main research aspect was to examine the influence of the inertia parameter on the tracking quality for control systems based on the active rejection paradigm. The theoretical description and preliminary hypothesis were supported by extensive simulation and experimental results. In particular, it is demonstrated that choosing the inertia parameter according to the real dynamics properties does not guarantee the best performance of the considered control structures.