Control Of Flexible Joint Manipulators Research Articles

Vibration Suppression and Trajectory Tracking Control of Flexible Joint Manipulator Based on PSO Algorithm and Fixed-Time Control

Vibration Suppression and Trajectory Tracking Control of Flexible Joint Manipulator Based on PSO Algorithm and Fixed-Time Control

Research on Constraint Following Control of Flexible Joint Manipulators Based on Singular Perturbation

For the control of 2-link flexible joint manipulators, a control method based on the singular perturbation theory and the Udwadia-Kalaba (U-K) method was proposed. The control design was implemented by 2 steps. First, the system order was reduced based on the singular perturbation method and the system was divided into fast and slow sub-systems, to simplify the solution process and overcome the system flexibility. Second, the state feedback constraint following control law for the fast and slow sub-systems was designed with the U-K method, to make the constraint following errors of the fast and slow sub-systems converge to zero, even if the system can't initially satisfy the constraints. The proposed method can deal with holonomic and nonholonomic servo constraints at the same time without the auxiliary variables of the Lagrange multiplier and the pseudo generalized velocity. The method was applied to 2-link flexible joint manipulator systems to solve the flexible oscillation and constraint following problems. Through simulations on MATLAB, and was compared with the traditional PID control, to verify the effectiveness and superiority.

Intelligent Control of Flexible Joint Manipulator Based on Dynamic Pattern Recognition

With the rapid development of intelligent manufacturing and Internet technology, the industrial system has entered a new stage of development. As an indispensable carrier for intelligent manufacturing and industrial development, robots are expanding their applications. Among them, the flexible mechanical arm has the advantages of light weight, low energy consumption and low inertia compared with the bulky rigid mechanical arm, and has been increasingly valued. The flexible manipulator is a very complex dynamic system whose dynamic equations are characterized by nonlinearity, strong coupling and time-varying. Therefore, this paper uses the most common and effective method to establish the dynamic model of the flexible manipulator using the Lagrange equation. Due to the uncertain system parameters, lack of control of the trajectory and the influence of load changes and external disturbances, the flexible manipulator has great uncertainty in its control process, and the traditional control methods have not very good control effect. Based on this, this paper proposes a combination of dynamic pattern recognition theory and flexible joint manipulator intelligent control method for the two-link flexible manipulator, and uses the new GA-RBF neural network closed-loop adaptive control method to achieve high precision. Trajectory tracking ensures stability in a shorter time. The simulation results show that the intelligent joint control method based on dynamic pattern recognition has better trajectory tracking and autonomous fast recognition dynamic mode.

Investigation of feasible controller for position control of flexible joint manipulator using multiple control techniques

With the advancement in technology, industries are moving from automation to robotics in the era of robotization. Different control techniques are being used in a different sector of production to control the whole machinery with special attention towards control the position and vibrations. In this research article, the feasible controller has been investigated for position control of flexible joint manipulator. For this, an effective version of a single linked flexible joint robotic manipulator has been used as a platform to control the position of manipulator using different control approaches. Euler’s–Lagrange equations have been used to obtain the effective version of the system and the position control has been performed by proportional-integral-derivative (PID) controller, pole –placement and linear quadratic regulator (LQR) methods. The main target of this research work is to maintain the rotational angle of the joint at an appropriate position and to remove the fluctuations at a specific robotic tool which is known as end effectors. The gains of PID controller have been delineated with the help of genetic algorithm and tuned for obtaining the most suitable gains to control the position of flexible joint manipulator and vibrations. As the settling time of the system was high so genetic algorithm has been used to optimize it. The Matlab®/simulation results depict that genetic algorithm tuned PID controller performed better correlated to both pole placement and LQR method of control.

Backstepping control of flexible joint manipulator based on hyperbolic tangent function with control input and rate constraints

AbstractIn this paper, we investigate the trajectory tracking problems of the link angle and angle speed of the flexible joint manipulator model based on external disturbance, the control input and rate constraints. The controller of the flexible joint manipulator model is designed using the backstepping control scheme. To achieve this objective, the smooth hyperbolic tangent function is used to solve the problems of control input and rate constraints, and the stability is proved using Lyapunov function in the design procedure of the backstepping control scheme. Finally, the effectiveness of the proposed backstepping controller is verified by numerical simulation.

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

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.

Backstepping Controller for Electrically Driven Flexible Joint Manipulator Under Uncertainties

The grown complexity of the robot manipulators dynamics taking into account the jointflexibility, parameter uncertainties and unknown bounded disturbances makes conventionalcontrol strategies difficult and complex to synthesize. This paper focuses on the investiga-tion into backstepping control of flexible joint manipulator driving by Brushless DC Motor(BDCM) in the presence of parameter uncertainties and unknown bounded disturbances fortracking trajectory. The goal of this paper is to compensate all uncertainties and distur-bances for flexible joint manipulator. To study the effectiveness of the controllers, backstep-ping controller has been developed for position control and an hysteresis controller has beentreated for current control. Simulation results of the response of the flexible joint manipu-lators associated with their controllers have been presented. The high performances of thebackstepping control are examined in terms of tracking accuracy and error reduction.

Recursive methods in control of flexible joint manipulators

This paper considers recursive solving methods in the control of modular and reconfigurable robot systems. The focus lies on the end-point accuracy and recursive methods for its improvement. The modeling of the robot systems under consideration is done by the Projection Equation in Subsystem representation. Due to gear elasticities and the lack of position sensors at the gearbox output shaft, deflections of the end-point cannot be compensated by standard PD motor control. Therefore, a novel approach is presented in this contribution to correct the resulting position errors of the end-points of the robot systems. This proposed method enables to do a flat parameterization of such elastically modeled systems in a recursive manner, which is contrasted to the generally used non-recursive exact feed-forward linearization method in minimal form for such under-actuated systems. The ability to do the computation in such a recursive way offers the possibility to do the calculations systems for systems with many degrees-of-freedom (DOF) as well, where standard analytical methods reach their limits. And for modular systems, it is applicable as well. For validation, the proposed method is implemented for a robot arm with seven joints and simulation results are presented.

Observer-Based Stability Control of Flexible Joint Manipulator Based on T-S Fuzzy Model

Observer-Based Stability Control of Flexible Joint Manipulator Based on T-S Fuzzy Model

Control of Flexible Joint Manipulator via Reduced Rule-Based Fuzzy Control with Experimental Validation

A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator. The rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section are the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of mentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic are low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy logic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results are compared with LQR method. In this paper, experimental validation of proposed method is presented.

Stability analysis and robust composite controller synthesis for flexible joint robots

In this paper the control of flexible joint manipulators is studied in detail. The model of N-axis flexible joint manipulators is derived and reformulated in the form of singular perturbation theory and an integral manifold is used to separate fast dynamics from slow dynamics. A composite control algorithm is proposed for the flexible joint robots, which consists of two main parts. Fast control, u f, guarantees that the fast dynamics remains asymptotically stable and the corresponding integral manifold remains invariant. Slow control, u s, consists of a robust PID designed based on the rigid model and a corrective term designed based on the reduced flexible model. The stability of the fast dynamics and robust stability of the PID scheme are analyzed separately, and finally, the closed-loop system is proved to be uniformly ultimately bounded (UUB) stable by Lyapunov stability analysis. Finally, the effectiveness of the proposed control law is verified through simulations. The simulation results of single- and two-link flexible joint manipulators are compared with the literature. It is shown that the proposed control law ensures robust stability and performance despite the modeling uncertainties.

Adaptive tracking control of flexible joint manipulator based on fuzzy model reference approach

A direct model reference adaptive control via Takagi–Sugeno fuzzy modelling and parallel distributed compensation (PDC) is developed for the MIMO plant model to control an uncertain flexible joint manipulator. The proposed control scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain parameters. From Lyapunov stability analysis and simulation results, the developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

Adaptive neural network control of flexible joint manipulators in constrained motion

This paper addresses the control problem of flexible joint manipulators in constrained motion. The singular perturb ation method is applied to reduce the original system into slow and fast subsystems. By adopting the composite control strategy, an adaptive neural network controller is first developed to control the slow subsystem. Then a fast feedback control is designed to stabilise the fast subsystem along its equilibrium trajectory. The system stability is proven using Lyapunov theory. It is shown that the motion tracking error converges to zero asymptotically, whereas the force tracking error remains bounded and can be made arbitrarily small. Numerical simulations are provided to show the effectiveness of the approach.

Tracking control of flexible joint manipulators using only position measurements

A dynamic feedback controller will be proposed to treat the trajectory tracking control problem of robotic manipulators with flexible joints that use only position measurements on the link and motor sides. Previously published results for the tracking control problem without velocity measurements address only the rigid robot design. Moreover, these results are based on a full-order observer whose dimension is equal to the dimension of the robotic systems. In contrast, our solution can be applied to treat the robotic systems with flexible joints. It does not require any restriction on the joint stiffness. The proposed controller is based on a reduced-order observer which is constructed only to estimate the velocity signal and whose dimension is half of the dimension of flexible-joint robotic systems. We shall show that the resulting closed-loop error system is locally exponentially stable with an arbitrarily large region of attraction. The convergence rate can be arbitrarily pre-assigned and the attraction...

Linear robust trajectory control of flexible joint manipulators

SUMMARYThis paper presents a practical approach for the point-to-point control of elastic-jointed robot manipulators. With the proposed approach only position and velocity feedback are referenced, as opposed to most of the existing control schemes of elastic-jointed manipulators which require additional acceleration and/or jerk feedback. To guarantee the robustness of the controller, it is designed on extreme parameter uncertainties due to highly elastic joints of manipulators and energy motivated Lyapunov functions are used to derive the control law. Four pertinent controller gains are chosen in light of the on-line position and velocity feedback of the links and joint sensors. Through a simulated experimental verification, it is demonstrated that the designed simple position and velocity feedback controller, similar to that used for rigid-jointed robots, can globally stabilize the elastic-jointed robot for a bounded reference position. In addition, the tracking performance of the controller reveals that this simple control algorithm is robust in terms of joint flexibility. And the simplicity of the presented control algorithm, as compared to other model-based techniques for flexiblejoint robots, is particularly advantageous. Even though the simulated experiments are conducted on a single-link flexible joint robot, control law derived in this paper has general meaning for multi-link flexible joint robots.

Rigid model-based fuzzy control of flexible-joint manipulators

This paper considers the application of fuzzy control to achieve global trajectory tracking and active damping of flexible-joint manipulators. A reasonable approach based on the combined computed torque control using rigid robot model and fuzzy control is suggested for flexible-joint manipulators. Sets of ‘four input variables — one output variable’ fuzzy control rules for each actuator respectively, are suggested for the perturbation control. The simulation results show that the transient performance and steady-state accuracy are near those of the feedback linearization approach. The simulation results with payload or joint stiffness variation for the single-link case are also shown to demonstrate the robustness of the control law.

Adaptive control of flexible joint manipulators: Comments on two papers

We discuss two papers [Spong, M. W. (1989) Syst. Control. Lett., 13, 15–21; Chang, Y.-Z. and R. W. Daniel (1992) Automatica, 28, 969–974] on the adaptive control of flexible joint robot manipulators. Our goal is to clarify the contributions of the two papers by correcting some technical errors and to provide some additional insight into the application of singular perturbation methods for the control of flexible joint robots.

Nonlinear feedback control of flexible joint manipulators: a single link case study

A nonlinear feedback control for a flexible joint manipulator is investigated. It is shown that if the elastic (parasitic) modes are weakly observable from the output of the system, a state-space coordinate transformation and a static state feedback and control space transformation will turn the flexible system into a linear controllable and observable system. In contrast, if the parasitics are strongly observable, a dynamic state feedback is required for input-output linearization. Numerical simulations for a single link flexible joint manipulator are reported, illustrating the application of the methodology. >

Adaptive control of flexible joint manipulators

This paper presents the first adaptive control result for flexible joint robot manipulators. Under the assumption of weak joint elasticity a singular perturbation argument is used to show that recent adaptive control results for rigid robots may be used to control flexible joint robots provided a simple correction term is added to the control law to damp out the elastic oscillations at the joints. In this way fundamental properties of rigid robot dynamics, such as passivity, may be exploited to design robust adaptive control laws for flexible joint robots.