Control Of Flexible Joint Robots Research Articles

Adaptive Neural Network Force Tracking Control of Flexible Joint Robot With an Uncertain Environment

Adaptive Neural Network Force Tracking Control of Flexible Joint Robot With an Uncertain Environment

Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Nonlinear PI“D”-Type Control of Flexible Joint Robots by Using Motor Position Measurements Is Globally Asymptotically Stable

An important subject that arises in the position control of robot manipulators is the local or global asymptotic stability of the closed–loop system equilibrium point, especially when an integral action is added to the control loop. For rigid joint robot manipulators, global nonlinear PID controllers have been introduced few years ago. However, for the case of flexible join robot manipulators, the design and analysis of such nonlinear PID laws is much more challenging. Only local asymptotic stability results have been reported. Thus, a novel global regulator for flexible joint robots is presented in this document. The proposed controller considers a nonlinear integral action and it requires only motor position measurements because an estimator subsystem is used to replace the motor velocity measurements. In other words, a nonlinear proportional-integral-“derivative” (PI“D”)-type controller is introduced. According to the closed–loop system analysis, conditions on the controller gains are established. Thus, the global asymptotic stability is concluded. Finally, experimental results on a two degrees-of-freedom serial flexible joint robot are shown and discussed.

Closure to “Discussion: Selective-Compliance-Based Lagrange Model and Multilevel Noncollocated Feedback Control of a Humanoid Robot” (Spyrakos-Papastavridis, E., Dai, J. S., Childs, P. R. N., and Tsagarakis, N. G., 2018, ASME J. Mech. Rob., 10(3), p. 031009)

Abstract This closure exposes the technical inconsistencies, erroneous comments, and misconceptions contained in the Discussion Article (Medrano-Cerda, 2020, “Discussion: ‘Selective Compliance-Based Lagrange Model and Multilevel Noncollocated Feedback Control of a Humanoid Robot’,” ASME J. Mech. Rob., 10.1115/1.4049005 [1]). The majority of incorrect comments relating to stability are not based on mathematical rigor and equations, but on the reproduction of more generic statements made in some textbooks published in the early 1990s. The comments in the Discussion Article fail to demonstrate how any of these generic statements could be applied to the specific cases presented in the Paper (Spyrakos-Papastavridis et al., 2018, “Selective-Compliance-Based Lagrange Model and Multilevel Noncollocated Feedback Control of a Humanoid Robot,” ASME J. Mech. Rob., 10(3). 10.1115/1.4039394 [2]) criticized by the Discussion Article. Further, the only equation that appears in the Discussion Article is incorrect, as will be demonstrated in this closure. Apart from being expressed incorrectly, the text accompanying this equation reveals the fundamentally incorrect mathematical assumption that a diagonal matrix is not equal to its transpose. Moreover, a significant proportion of the comments in the Discussion Article relies upon misconceptions about flexible-joint robot control and humanoid robotics, in terms of their theory and practice, standing in contrast to results reported in these research fields over the last decades.

Passive Impedance Control of Robots With Viscoelastic Joints Via Inner-Loop Torque Control

This article presents passive impedance control of flexible joint robots (FJRs) via inner-loop torque control of elastic joints. However, according to our theoretical analysis, the torque control methods of series elastic actuators (SEAs) are often limited by the fact that the acceleration signals are amplified by the control gains. Since the acceleration signals are often affected by differentiation noise, the analysis may become invalid in practice. To alleviate this limitation, we propose the use of the so-called series viscoelastic actuator (SvEA), which significantly reduces the acceleration amplification. Consequently, in contrast to the SEA case, the theoretical analysis of an SvEA-based FJR is valid in real implementations. We would like to highlight the fact that the theoretical analysis (more specifically, passivity analysis) is performed for nonlinear robot dynamics without linearization. As a result, the passive impedance controller can be realized more robustly with enhanced inner-loop torque control.

An Experimental Study on MPC based Joint Torque Control for Flexible Joint Robots

This paper presents an experimental evaluation of joint torque control in a flexible joint robot using a model predictive control approach. The control of elastic robots is challenging due to the underactuated nature of the connected link and motor dynamics. A typical solution is to formulate the controller in a cascaded way with an outer loop controller for the nonlinear link side dynamics in combination with an inner loop controller for the joint torque. In this paper the inner loop torque controller utilizes a model predictive control design in order to take actuator constraints into account and, in case of a special choice of the torque reference and control gains, to avoid the computation of higher time derivatives in the desired joint torque. We compare two different controller implementations, which differ in the choice of the reference model. In particular, we investigate on the use of the natural open loop dynamics for the reference model. The controllers are evaluated in detail using a simulation study as well as in experiments on a single joint testbed.

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

By relying on the input–output feedback linearization approach, a novel adaptive controller for flexible joint robots is proposed in this work. First, a model-based controller is developed to get a structure that is useful in the development of the adaptive controller. The adaptive version is developed by using two techniques. To stabilize the output function, an adaptive neural network controller is used, which approximates the non-linear function that contains the uncertainties. The desired rotor position required by the input–output feedback linearization controller is defined with the structure of a link dynamics adaptive regressor-based controller. The main reason to adopt the mentioned structure in the definition of the desired rotor link position is to guarantee its differentiability. Real-time experiment comparisons among the model-based controller, a model-based controller with desired compensation, an adaptive controller based on joint torque feedback, and an adaptive neural network-based controller are carried out. Experimental results support the theory reported in this document and the accuracy of the proposed approach.

Vibration Control of Flexible Joint Robots Using a Discrete-Time Two-Stage Controller Based on Time-Varying Input Shaping and Delay Compensation

Abstract Most industrial serial robots use decentralized joint controllers assuming rigid body dynamics. To prevent exciting the flexible mode, gains are kept low, resulting in poor control bandwidth and disturbance rejection. In this paper, a two-stage flexible joint discrete controller is presented, in which the decentralized approach is extended with a stiffness to take into account the dominant coupling mode. In the first-stage, an input shaping feedforward shapes the rigid closed-loop dynamics into desired dynamics that does not produce link vibrations. Robotic dynamic computation based on a recursive Newton–Euler algorithm is conducted to update the feedforward link inertia parameter during robot motion. A second-stage is added to increase disturbance rejection. A generalized Smith predictor (GSP) is developed to compensate for delay and feedback sensor filtering. An effective methodology is presented to optimize the control loop gains. Numerical simulations and experiments on a six-joint robot manipulator confirm that the proposed controller improves control performances in terms of bandwidth, vibration attenuation, and disturbance rejection.

Tracking control for flexible joint robots based on adaptive fuzzy compensation with uncertain parameters

SummaryThis article presents a control scheme for flexible joint robots which has uncertain parameters based on adaptive fuzzy compensation. Considering the unknown parameters, the proposed state feedback control approach utilizes measured variables to establish a cascade structure that is based on simplified dynamics. After reducing the number of fuzzy rules, the adaptive fuzzy logic system is added as compensation to decrease the approximated errors, and the robust terms are also used to enhance the robustness of closed‐loop system. Then, the global asymptotic stability could be confirmed through Lyapunov stability principle and Barbalat's lemma. Compared with the other two controllers, the proposed control method has not only higher position accuracy and better dynamic performance but also robustness to the approximation of motor inertia, friction torque and link torque. Some simulation experiments are conducted to show the validity of the proposed scheme.

Data-based PID control of flexible joint robot using adaptive safe experimentation dynamics algorithm

This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.

Cascaded Control of Flexible-Joint Robots Based on Sliding-Mode Estimator Approach

The inherent highly nonlinear coupling and system uncertainties make the controller design for a flexible-joint robot extremely difficult. The goal of the control of any robotic system is to achieve high bandwidth, high accuracy of trajectory tracking, and high robustness, whereby the high bandwidth for flexible-joint robot is the most challenging issue. This paper is dedicated to design such a link position controller with high bandwidth based on sliding-mode technique. Then, two control approaches ((1) extended-regular-form approach and (2) the cascaded control structure based on the sliding-mode estimator approach) are presented for the link position tracking control of flexible-joint robot, considering the dynamics of AC-motors in robot joints, and compared with the singular perturbation approach. These two-link position controllers are tested and verified by the simulation studies with different reference trajectories and under different joint stiffness.

Identification and Control of Flexible Joint Robot Using Multi-Time-Scale Neural Network

In this paper, a new identification and control scheme for the flexible joint robotic manipulator is proposed. Firstly, by defining some new state variables, the commonly used dynamic equations of the flexible joint robotic manipulators are transformed into the standard form of a singularly perturbed model. Subsequently, an optimal bounded ellipsoid algorithm based identification scheme using multi-time-scale neural network is proposed to identify the unknown system dynamic equations. Lastly, by using the singular perturbation theory, an indirect adaptive controller based on the identified model is proposed to control the system such that the joint angles can track the given reference signals. The closed-loop stability of the whole system is proved, and the effectiveness of the proposed schemes is verified by simulations.

Control of Flexible Joint Robot Based on Motor State Feedback and Dynamic Surface Approach

A controller based on dynamic surface control and observer is proposed by using motor state feedback for trajectory tracking of flexible joint robot with uncertain link dynamic model. Considering the link state information cannot be obtained, an observer is designed to estimate the link state information, and a dynamic surface controller is proposed based on link state observer. The controller based on the observer compared to backstepping controller avoids the repeated differentiation problem. At the same time, the dynamic surface method avoids the measurement of high order signal. The simulation results show that the designed controller has a good trajectory tracking effect, which effectively suppresses the residual vibration of the flexible joint robot. Moreover, the proposed controller and observer are robust to the uncertainty and external disturbance of the link dynamic model. The proposed controller can be directly applied in the flexible joint robot without installing additional sensors, which is very important for industrial applications.

Joint torque control of flexible joint robots based on sliding mode technique

For robots with flexible joints, the joint torque dynamics makes it difficult to control. An effective solution is to carry out a joint torque controller with fast enough dynamic response. This article is dedicated to design such a torque controller based on sliding mode technique. Three joint torque control approaches are proposed: (1) The proportional-derivative (PD)-type controller has some degree of robustness by properly selecting the control gains. (2) The direct sliding mode control approach which fully utilizes the physical properties of electric motors. (3) The sliding mode estimator approach was proposed to compensate the parameter uncertainties and the external disturbances of the joint torque system. These three joint torque controllers are tested and verified by the simulation studies with different reference torque trajectories and under different joint stiffness.

Reconfigurable control of flexible joint robot with actuator fault and uncertainty

Abstract This paper presents the fault tolerant control (FTC) of a flexible joint robot using singular perturbation method in order to compensate for the lost performance due to the occurrence of actuator fault and the uncertainty. This FTC is based on Lyapunov redesign principle. The singular perturbation method is used to reduce the dynamic model of the flexible joint robot in a fast and slow subsystem. The time scale reduction of the flexible joint model is carried out when their joint stiffness is large enough and the singular perturbation parameter is set to zero. The fault-tolerant control structure in this paper is based on two parts. The first term described the composite control for the system without defect and without uncertainty which represents the sum between slow and fast controllers. While the second term of the fault tolerant command describes additive control designed to compensate for the fault effect of the actuator on the uncertain system. The additive approach is based on the Lyapunov theorem, which guarantees asymptotic stability despite the presence of actuator defects and the parametric uncertainty. The theoretical results are applied on a robot manipulator with a single flexible joint.

Two-Time Scale Control of Flexible Joint Robots With an Improved Slow Model

This paper presents a novel multivariable two-time scale control method for flexible joint robots. The fundamental limitations of the conventional two-time scale control are clearly explained based on a singular perturbation analysis with a linearized dynamic model. Unlike the conventional methods, the bandwidth of the slow control can be made high and the derivatives of the rigid control are not required. The closed-form solution of a slow model that is more accurate than the rigid model is presented. It is shown that the modeling error of the proposed two-time scale control can be made arbitrarily small by the control gain only. A real-world experiment with a six degrees-of-freedom commercial industrial robot was performed to validate the presented approach.

Neuro-adaptive observer based control of flexible joint robot

Abstract Due to high nonlinearity, strong coupling and time-varying characteristics of flexible joint robot manipulators, their control design is generally a challenging problem. There are inevitable uncertainties associated with their kinematics and dynamics, so that accurate models would not be available for control design. Furthermore, practically we may face the problem that state variables required by the controller are not measurable. In this paper, we focus on the study of control system design using a neural network observer to solve the aforementioned unmeasurable problem. First, we propose an observer based on Radial Basis Function (RBF) neural network to estimate state variables of the normal system. We then design the controller based on dynamic surface control method for a single link flexible joint manipulator whose model is unknown. The unknown model of the manipulator is constructed by RBF neural network. The stability of the observer and controller is shown by Lyapunov method. Finally, simulation studies are performed to test and verify the effectiveness of the proposed controller.

バックステッピング制御法を用いたフレキシブルジョイントロボットの制御について

バックステッピング制御法を用いたフレキシブルジョイントロボットの制御について

Control of Flexible Joint Robot Using Integral Sliding Mode and Backstepping

The control of flexible joint robot is getting more attentions because its applications are more frequently used for robot systems in these days. This paper proposes a robust impedance controller for flexible joint robots by using integral sliding mode control and backstepping control. The sliding mode control decouple disturbances completely but requires matching condition of disturbances. The dynamic model of FJR is divided into motor side and link side and the disturbance of the link side does not satisfy matching condition and cannot be decoupled directly by the actual input in the motor side. To overcome this difficulty, backstepping control technique is used with sliding mode control. The mismatched disturbance in the link side is changed into matched one in the respect to virtual control input which is the state controlled by actual input in the motor side. Integral sliding mode control is used to preserve the impedance control performance and the improved robustness at the same time.