Conventional Computed Torque Controller Research Articles

Intelligent and robust control of space manipulator for sustainable removal of space debris

This study focuses on enhancing the control precision and efficiency of a two-degree-of-freedom (2-DOF) space manipulator used for active space debris removal. The unpredictable space environment introduces large uncertainties, which introduces unique challenges beyond the capabilities of a standalone computed torque controller and degrades control performance. To address this problem, a robust controller is developed, integrating traditional techniques such as sliding mode and computed torque control with a Neural Network framework. This synergy leverages both methods' strengths—conventional controls' accuracy and Neural Network's adaptability. The integration of Neural Network-based sliding mode control complements the robustness of computed torque control by actively mitigating uncertainties and disturbances inherent in the space environment. The 2-DOF manipulator's state variables model the system dynamics, necessitating accurate relative motion estimation between the manipulator and debris. The global asymptotic stability of the developed algorithm is demonstrated through the Lyapunov theorem, guaranteeing error convergence to zero. The convergence, stability, precision, tracking errors, and responsiveness of the controller have been analysed and validated by the MATLAB Simulink simulations. The novel approach's performance effectiveness is substantiated by numerical simulations and a comparative analysis with conventional computed torque control. Outcomes highlight the superior precision and efficiency in manipulator tracking the trajectory, validating the integrated controller's potential for successful active space debris removal.

A Tangent Release Manipulation Controlled by a Dual-Arm Space Robot

As people further develop space with advanced technology, space robots have played a significant role in on-orbit servicing missions. Space robots can carry out more risky and complicated missions with less cost than astronauts. Dual-arm space robots can perform complex on-orbit space missions more effectively than single-arm space robots. Since the coupled dynamics between the free-floating base and the arms exist in space robots, accurate coordinate control of the base and the arms is essential. Spacecraft release missions have been proposed to berth/deberth a spacecraft to a space station. Based on the existing release missions, a tangent release strategy is introduced in this paper, which can release a space object in the tangent direction of the final link of a space manipulator. This strategy can control a dual-arm space robot to deploy cargo/spacecraft in variable directions in 3D space without thrusters and the associated fuel consumption. For instance, this tangent release operation can transport cargo or modules of large-scale spacecraft needing on-orbit assembly. Considering model uncertainties, robust controllers again model uncertainties that are used to control the dual-arm space robot with high accuracy. Hence, a robust sliding mode controller (SMC) is utilized to accurately control the space robot to carry out the proposed tangent release strategy. For comparison, we select a conventional computed torque control (CTC) implemented by a PD-type controller. In the simulations, the SMC performs better in tracking accuracy and robustness against the model uncertainties than the PD controller. Numerical simulations indicate the feasibility and effectiveness of the tangent release manipulation of a space object by a dual-arm space robot.

Hybrid force/position control in workspace of robotic manipulator in uncertain environments based on adaptive fuzzy control

In this study, a novel hybrid force/position controller in workspace of robotic manipulator based on adaptive fuzzy control is developed to improve the controlling performance of force and position in contact with uncertain environments. The dynamic model of the robotic manipulator in joint space is converted to a dynamic model in workspace, which is consisted of the model of position-controlled subsystem and model of force-controlled subsystem. Furthermore, in the position-controlled subsystem, an adaptive fuzzy computed torque control is proposed by considering adaptive fuzzy control and conventional computed torque control (AFCTC), for compensating deviations caused by the presence of structured uncertainty and unstructured uncertainty. In the force-controlled subsystem, a fuzzy proportional integral control method (FPI) is proposed by fuzzy logic and conventional proportional integral method to improve the control performance. The asymptotic stability of the developed controller is proved by Lyapunov theorem. The simulation results show the preferable performance of the proposed controller (AFCTC-FPI) by comparison with adaptive fuzzy sliding mode control (AFSMC), adaptive network-based fuzzy inference system with proportion differential and integral (ANFIS-PD+I), computed torque control-proportion integral (CTC-PI), fuzzy proportional integral derivative (FPID), and proportional integral derivative (PID) in uncertain environments. Furthermore, in the experimental study, average position error with AFCTC-FPI is decreased by 87.14 % than that with PID, meanwhile, range of interaction force with AFCTC-FPI is reduced by 70.31% than that with PID. In summary, the experimental results also show the superior control accuracy of the proposed controller in real environment.

Fault tolerant control for robotic manipulator using fractional-order backstepping fast terminal sliding mode control

In this paper, the problems of tracking control and finite-time stabilization of a high nonlinear system such as a robotic manipulator in the presence of actuator faults, uncertainties, and external disturbances are explored. In order to improve the performance of the system in the presence of actuator faults, uncertainties and external disturbances a novel fault tolerant control system based on fractional-order backstepping fast terminal sliding mode control is developed in this paper. The control system is developed by employing the results obtained from studies in the fields of fractional-order calculus, backstepping, sliding mode control, Mittag–Leffler stability, and finite-time Lyapunov stability. The performance of the suggested controller is then tested for a PUMA560 robot in which the first three joints are used. The simulation results validate the usefulness of the developed control approach in terms of accuracy of tracking, and convergence speed in the presence of disturbances, uncertainties and actuator faults. The trajectory tracking performance of the developed method is compared with other state of the art approaches such as conventional computed torque control, proportional integral derivative control and nonsingular fast terminal sliding mode control. The simulation results show that the proposed control approach performed better as compared to other control approaches in the presence of actuator faults, uncertainties, and disturbances.

A Non-Linear Continuous-Time Generalized Predictive Control for a Planar Cable-Driven Parallel Robot

This paper addresses a novel nonlinear algorithm for the trajectory tracking of a planar cable-driven parallel robot. In particular, we outline a nonlinear continuous-time generalized predictive control (NCGPC). The proposed controller design is based on the finite horizon continuous-time minimization of a quadratic predicted cost function. The tracking error in the receding horizon is approximated using a Taylor-series expansion. The main advantage of the proposed NCGPC is based on using an analytic solution, which can be truncated to a desired degree of order of the Taylor-series. This allows us to achieve a prediction horizon of NCGPC tracking performance. The description of the proposed NCGPC method is followed by a comparison between NCGPC and a conventional computed torque control (CTC) method. Robustness tests are performed by considering payload and parameter uncertainties for both controllers. Simulation results of NCGPC compared to the commonly used CTC prove the effectiveness and advantages of the proposed approach.

유전알고리즘을 이용한 로봇 매니퓰레이터의 동적제어기 설계

Robot manipulators are highly nonlinear system with multi-inputs multi-outputs, and various control methods for the robot manipulators have been developed to acquire good trajectory tracking performance and improve the system stability lately. The computed torque controller has nonlinear feedforward control elements and so it is very effective to control robot manipulators. If the control gains of the computed torque controller is adjusted according the payload, then more precise control performance is attained. This paper extends the conventional computed torque controller in the joint space to the Cartesian space, and optimize the control gains for some specified payloads in both joint and Cartesian spaces using genetic algorithms. Also a neural network is employed to have proper control gains for arbitrary payloads using generalization properties of the neural network. Computer simulation results show that the proposed control system for robot manipulators has excellent performance in various conditions.

Robust coordinated control of a dual-arm space robot

Dual-arm space robots are more capable of implementing complex space tasks compared with single arm space robots. However, the dynamic coupling between the arms and the base will have a serious impact on the spacecraft attitude and the hand motion of each arm. Instead of considering one arm as the mission arm and the other as the balance arm, in this work two arms of the space robot perform as mission arms aimed at accomplishing secure capture of a floating target. The paper investigates coordinated control of the base's attitude and the arms' motion in the task space in the presence of system uncertainties. Two types of controllers, i.e. a Sliding Mode Controller (SMC) and a nonlinear Model Predictive Controller (MPC) are verified and compared with a conventional Computed-Torque Controller (CTC) through numerical simulations in terms of control accuracy and system robustness. Both controllers eliminate the need to linearly parameterize the dynamic equations. The MPC has been shown to achieve performance with higher accuracy than CTC and SMC in the absence of system uncertainties under the condition that they consume comparable energy. When the system uncertainties are included, SMC and CTC present advantageous robustness than MPC. Specifically, in a case where system inertia increases, SMC delivers higher accuracy than CTC and costs the least amount of energy.

Design Sensitive Model Reference Controller with Application to Medical Technology

The past two decades have seen the incorporation of robotics into medical applications. From a manufacturing perspective, robots have been used in pharmaceuticals, preparing medications. But on more novel levels, robots have been used in service roles, surgery, and prosthetics. The capability of high-precision operation in manufacturing settings gave the medical industry high hopes that robots could be used to assist in surgery. Not only are robots capable of much higher precision than a human, they are not susceptible to human factors, such as trembling and sneezing, that are undesirable in the surgery room. Another advantage to robots in medicine is the ability to perform surgery with very small incisions, which results in minimal scar tissue, and dramatically reduced recovery times. The popularity of these minimally invasive surgical (MIS) procedures has enabled the incorporation of robots in surgeries. In this paper, model reference computed torque controller is recommended for four degrees of freedom serial links robot manipulator, which modeled in SIMSCAPE. To design stable controller conventional computed torque controller is recommended. It is a nonlinear, stable, and reliable controller. The proposed approach effectively combines of design methods from nonlinear controller, and linear Proportional-Derivative (PD) control to improve the performance and stability. This paper has two important objectives: a) study on modeling of 4 degrees of freedom (DOF) based on Simscape software and b) design PD model reference computed torque controller to improve the sensitivity of surgical robot manipulator.

Computed Torque Control of a Caterpillar Robot Manipulator Using Neural Network

This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

Output feedback tracking control of uncertain robot manipulators via higher-order sliding-mode observer and fuzzy compensator

This paper develops a new method to control uncertain robot manipulators by using only position measurements. The controller is designed based on a combination of a computed torque controller (CTC) with a higher-order sliding-mode observer and a fuzzy compensator. First, three higher-order sliding-mode (SM) observers (second-order SM, third-order SM and third-order SM linear (TOSML) observers) are designed and compared to verify whether the TOSML observer is the best for observing velocity and identifying uncertainty. A combined CTC-TOSML controller was then designed. Although this controller scheme can overcome the drawbacks of conventional CTCs, its tracking performance can still be improved. To enhance capability of the tracking performance, a CTC-TOSML controller plus fuzzy compensator called a CTC-TOSML-Fuzzy controller is proposed. The proposed controller increases the potential of the CTC for real robot applications. Finally, computer simulation results on a PUMA560 robot are discussed to verify the effectiveness of the proposed strategy.

Fuzzy Adaptive Compensation Control for Robot Manipulator with Uncertain Dynamics

This paper proposes two types of adaptive control schemes combined by the conventional computed-torque control and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties. Fuzzy compensators based on feed-forward and feed-back are developed to compensate these modeling uncertainties. The validity of the two types of adaptive control schemes is shown by numerical simulations of a three link rotary robot manipulator.

An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator

Parallel manipulators have advantages like high accuracy, high stiffness, high payload capability, low moving inertia, and so on. In this paper, a detailed study to apply an online self gain tuning method using neural networks for nonlinear PD computed torque controller to a 2-dof parallel manipulator is presented. A novel nonlinear PD computed torque controller is achieved by combining conventional computed torque controller and auto tuning method using neural networks which has advantages such as flexibility, adaptation and learning ability. The proposed controller has a simple structure and little computation time while securing good performance in tracking trajectories of parallel manipulators. To verify the control performance, various simulations of a 2-dof parallel manipulator are conducted. Simulation results show the effectiveness of the proposed method in comparison with the conventional computed torque controller.

Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics

To overcome the disadvantages of conventional computed-torque control and fuzzy control, and to exploit their attractive features, this paper proposes two types of adaptive control scheme combining conventional computed-torque control and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties. Fuzzy compensators based on feed-forward and feed-back are developed to compensate these uncertain dynamics. On the basis of Lyapunov stability theory, a tracking error limit is derived for the closed-loop control system and the convergence and stability of the control schemes are proved. Comparisons of their performances with conventional computed-torque controllers under the condition of these uncertainties are carried out. The validity of the two types of adaptive control scheme is shown by numerical simulations of a three-link rotary robot manipulator.

Episodic reinforcement learning control approach for biped walking

This paper presents a hybrid dynamic control approach to the realization of humanoid biped robotic walk, focusing on the policy gradient episodic reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: a conventional computed torque controller and an episodic reinforcement learning controller. The reinforcement learning part includes fuzzy information about Zero-Moment- Point errors. Simulation tests using a medium-size 36-DOF humanoid robot MEXONE were performed to demonstrate the effectiveness of our method.

Nonlinear computed torque control for a high-speed planar parallel manipulator

A new computed torque (CT)-type controller termed nonlinear CT (NCT) controller is developed and applied to a high-speed planar parallel manipulator. The NCT controller is designed by replacing the linear PD in the conventional CT controller with the nonlinear PD (NPD) algorithm. The stability of the parallel manipulator system with the NCT controller is proven using the Lyapunov theorem, and the proposed controller is further proven to guarantee asymptotic convergence to zero of both tracking error and error rate. The superiority of the proposed NCT controller is verified through the trajectory tracking experiments of an actual high-speed planar parallel manipulator, and the experiment results are compared with the CT controller.

Policy Gradient Fuzzy Reinforcement Learning Control of Humanoid Walking

This paper presents a novel dynamic control approach to acquire biped walking of humanoid robots focussed on policy gradient reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: conventional computed torque controller including impact-force controller and reinforcement learning computed torque controller. Reinforcement learning part includes fuzzy information about Zero-Moment Point errors. To demonstrate the effectiveness of our method, we apply it in simulation to the learning of a biped walking.

Dynamic Control Algorithm for Biped Walking Based on Policy Gradient Fuzzy Reinforcement Learning

This paper presents a novel dynamic control approach to acquire biped walking of humanoid robots focussed on policy gradient reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: conventional computed torque controller including impact-force controller and reinforcement learning computed torque controller. Reinforcement learning part includes fuzzy information about Zero-Moment Point errors. To demonstrate the effectiveness of our method, we apply it in simulation to the learning of a biped walking.

Adaptive Hybrid Control for Linear Piezoelectric Ceramic Motor Drive Using Diagonal Recurrent CMAC Network

This paper presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.

Velocity and Torque Feedback Control of a Nonholonomic Mobile Robot Using Fuzzy-Neural Networks

This article is concerned with the simulation of mobile robots by using the fuzzy-neural network controller (FNNC). The article describes one solution for the problem of simulation study of dynamic mobile robots to calculate the required configuration variables to accomplish the tasks of motion control, while satisfying the nonholonomic constraint.The FNNC proposed in this article can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the mobile base. Results from the simulations are presented and analysed here. It is concluded by the obtained results that a FNNC together will a well known conventional computed torque controller may improve the performance of the mobile robots drastically. There is not need of a priori information of the dynamic parameters of the mobile robots, because the FNNC learns them very fast.

Feedback Linearized Joint Torque Control of a Geared, DC Motor Driven Industrial Robot

Implementation of advanced model based robot control algorithms necessitates that joint actuators be capable of generating com manded joint torques. This capability permits compensation of gravity torques, for example. Relatively recent work reported in the robotics literature has focused on development of load torque sensing and control of robots actuated with permanent magnet DC motors and harmonic drive gear reducers. This development in troduces the possibility of the retrofit of a large class of small- to midsized industrial robots with advanced model based controllers, which require that the commanded torque signal be reproduced at each robot joint. In this paper, the authors establish theoretically that the direct application of computed torque control to a geared, DC permanent magnet actuated robot with local joint load torque controllers does not lead to decoupled dynamics, as is the case with direct drive robots. The resultant dynamics are not decoupled due to the interaction of the computed torque control with the inner joint torque control loops. To achieve decoupled system behavior, a modified computed torque control law is developed that leads to decoupled system dynamics. Experimental results are presented that compare the modified computed torque control with the stan dard computed torque control. The experiments are conducted on a commercial 6-DOF robot that has been retrofitted with joint torque control on the first three joints. Tests have been conducted at a con trol update rate of 1000 Hz. The experimental results illustrate that the modified computed torque controller performance is somewhat better than the conventional computed torque control approach.