Prescribed Performance Tracking of a Variable Stiffness Actuated Robot

Psomopoulou, Efi; Theodorakopoulos, Achilles; Doulgeri, Zoe; Rovithakis, George A.

doi:10.1109/tcst.2015.2394748

Cited by 83 publications

(51 citation statements)

References 28 publications

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“…Moreover, up to now, the FC has been used in some specific fields only. [51][52][53][54][55] In this paper, a novel UIO-based funnel nonsingular terminal SMC (FNTSMC) method is proposed for servomechanisms with unknown dynamics (eg, nonlinear friction, uncertainties, and external disturbances). An auxiliary filtered variable is defined and used to construct a UIO to estimate the lumped unknown dynamics.…”

Section: Introductionmentioning

confidence: 99%

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Wang

Ren

et al. 2018

Intl J Robust & Nonlinear

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Summary In this paper, an unknown input observer is proposed for servomechanisms with unknown dynamics (eg, nonlinear friction, parameter uncertainties, and external disturbance). An unknown input observer is developed by introducing auxiliary filtered variables in terms of first‐order low‐pass filters, where only one constant (filter parameter) needs to be tuned. A modified funnel variable is developed by using the tracking error to replace the scaling factor to improve the transient and steady‐state performance of the control systems. Then, a sliding mode surface with the new transformed error is defined and used to design a funnel nonsingular terminal sliding mode controller to achieve finite‐time convergence. In addition, the proposed funnel nonsingular terminal sliding mode controller method avoids the singularity problem in the conventional sliding mode control via modifying the terminal sliding manifold. Finally, experimental results are given to illustrate the effectiveness of the suggested control scheme.

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Section: Introductionmentioning

confidence: 99%

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Wang

Ren

et al. 2018

Intl J Robust & Nonlinear

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“…The composite disturbance vector d w ∈ R 6 is shown in (10). The dw 2 is taken as an example to demonstrate the calculation process of the d w , as shown in (11) and (12).…”

Section: System Modelmentioning

confidence: 99%

“…Although the controllers with integral terms presented in [5,17,18] and the output-based feedback controller presented in [21] are proved to be efective, they may exhibit undesired control performances when the system models have large parametric uncertainties and unknown disturbances. The gain scheduling control strategies have been proposed with improved robustness in dealing with the parametric variations of the joint stiffness of the AwAS [7,8], but the fluctuation tracking error and the static tracking error of the output link of the joint are relatively obvious when the external disturbance is applied to the output link, and this problem has been pointed out in [10]. For the state feedback controller presented in [10], although the robustness of the controller to the external perturbations and the transmission losses of the motors are demonstrated, the effect of parametric uncertainties of the system model on tracking performance is not shown in the simulation, and further research is required on the feasibility between the actuator specifications and the prescribed performance of the closed-loop system.…”

Section: Introductionmentioning

confidence: 99%

“…The gain scheduling control strategies have been proposed with improved robustness in dealing with the parametric variations of the joint stiffness of the AwAS [7,8], but the fluctuation tracking error and the static tracking error of the output link of the joint are relatively obvious when the external disturbance is applied to the output link, and this problem has been pointed out in [10]. For the state feedback controller presented in [10], although the robustness of the controller to the external perturbations and the transmission losses of the motors are demonstrated, the effect of parametric uncertainties of the system model on tracking performance is not shown in the simulation, and further research is required on the feasibility between the actuator specifications and the prescribed performance of the closed-loop system. In the design of the adaptive neural network controller presented in [11], the stiffness characteristic of the joint is considered quasistatic, i.e., the manipulator with joints driven by VSAs can be equivalent to a manipulator based on series elastic actuators, and the control scheme in [11] is not suitable for simultaneous position and stiffness tracking control of the VSA.…”

Section: Introductionmentioning

confidence: 99%

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Robust Tracking Control Of The Variable Stiffness Actuator Based On The Lever Mechanism

Guo

Tian

2018

Asian Journal of Control

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This paper is concerned with the design of a robust adaptive tracking control scheme for a class of variable stiffness actuators (VSAs) based on the lever mechanisms. For these VSAs based on the lever mechanisms, the AwAS‐II developed at Italian Institute of Technology (IIT) is chosen as the study object, and it is an enhanced version of the original realization AwAS (actuator with adjustable stiffness). Firstly, for the dynamic model of the AwAS‐II system in the presence of parametric uncertainties, unknown bounded friction torques, unknown bounded external disturbance and input saturation constraints, by using the coordinate transformations and the static state feedback linearization, the state space model of the AwAS‐II system with composite disturbances and input saturation constraints is transformed into an uncertain multiple‐input multiple‐output (MIMO) linear system with lumped disturbances and input saturation constraints. Subsequently, a combination of the feedback linearization, disturbance observer, sliding mode control and adaptive input saturation compensation law is adopted for the design of the robust tracking controller that simultaneously regulates the position and stiffness of the AwAS‐II system. Under the proposed controller, the semi‐global uniformly ultimately bounded stability of the closed‐loop system has been proved via Lyapunov stability analysis. Simulation results illustrate the effectiveness and the robustness of the proposed robust adaptive tracking control scheme.

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“…Based on the model of FJR presented in [1], multifarious nonlinear control methods are presented such as backstepping method [2][3][4], sliding-mode control [5][6][7][8], switching control [9], fuzzy control [10], and neural network control [11,12]. In consideration of the problem caused by the inherent structure of FJR under practical circumstance, such as friction, time delay, and variable stiffness, some researchers proposed effective strategies to solve such problem [13][14][15]. Moreover, the teleoperation control method is also widely used in robot research [16,17].…”

Section: Introductionmentioning

confidence: 99%

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Wang

2017

Complexity

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This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

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Prescribed Performance Tracking of a Variable Stiffness Actuated Robot

Cited by 83 publications

References 28 publications

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Unknown input observer‐based robust adaptive funnel motion control for nonlinear servomechanisms

Robust Tracking Control Of The Variable Stiffness Actuator Based On The Lever Mechanism

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

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