Adaptive Control Of Robot Manipulators Research Articles

Adaptive control of robotic manipulator with input deadzone and disturbances/uncertainties

This work address the problem of trajectory tracking for robotic manipulator while encountering external disturbances and internal uncertainties. Robotic manipulators have been widely used in various industrial and research applications due to their versatility and efficiency. The precise control of these manipulators is essential for their proper functioning. However, due to the presence of uncertainties and disturbances in the system, it is often challenging to achieve accurate control. The proposed controller is capable of handling the uncertainties and disturbances present in the system, making it robust and efficient for practical applications. The controller is designed based on the concept of second-order nonsingular terminal sliding mode control (SNTSMC), which ensures that the system tracks the desired trajectory accurately. The substitution of the sign function with the saturation function effectively reduces the chattering that is caused by the high-frequency switching element in the conventional SMC approach. By utilizing an adaptive technique, uncertainties can be effectively mitigated without relying on any prior knowledge of an upper limit. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The simulation results show the effectiveness of the proposed algorithm in terms of tracking accuracy and robustness.

Adaptive control of robot manipulators with closed architecture

This paper focuses on the adaptive control of robot manipulators with closed architecture and parametric uncertainty. We propose a new class of adaptive controllers via exploiting the potential as applying the forwardstepping approach and the input–output properties of an inverted form of the dynamics of robots with closed architecture. The condition that ensures the stability and convergence of the closed-loop system becomes rather moderate due to the new design and analysis, and the result presented further enhances the connection between nonlinear control theory and most industrial/commercial robotic applications.

Finite-Time Command Filtered Adaptive Control for Robot Manipulators in Random Vibration Environment

This paper introduces a finite-time tracking control algorithm for robot manipulator systems in a random vibration environment, which addresses the challenges of parameter uncertainty and input saturation. The algorithm combines command filtered adaptive backstepping with neural networks to approximate unknown nonlinear dynamics and avoid the singularity problem of traditional finite-time backstepping methods. An error compensation mechanism based on the fractional power function is also introduced to improve trajectory tracking accuracy, and the algorithm is shown to ensure practical finite-time stability in mean square. Numerical simulations demonstrate that the effectiveness of proposed method.

Digital twin in the system of external adaptive control of robot manipulators

The article discusses the implementation of a digital twin in the system of adaptive control of robotic manipulators. The use of digital twins to plan, control and maintain the life cycle of production equipment forms the sustainable introduction of robotic systems into the technological processes of high-tech enterprises. The use of a digital twin in the collaboration of a robot system allows you to design more versatile workplaces and assembly lines, and optimize their workload. A new approach is proposed that allows solving problems of direct and inverse kinematics for robotic manipulators. The approach is to represent the kinematic diagram of a robotic arm with six rotational joints as a vector model. Due to the low computational load, the use of the proposed approach in control systems makes it possible to reduce delays when working in real time, with the ability to increase the number of robotic arms and control with higher accuracy.

Directly adaptive control for robotic manipulators with time varying uncertainties

The paper proposes an approach of directly adaptive control for manipulators, which is applicable to control the robotics perturbed by time varying uncertain parameters. This approach is established based on combining the well known conventional feedback linearization method and the here in this article proposed model based online observation of uncertainties. The tracking control performance of this proposed directly adaptive controller has been verified peer theoretical method and numerical simulation.

Vector model for solving the inverse kinematics problem in the system of external adaptive control of robotic manipulators

A new approach that allows to solve the problems of forward and inverse kinematics for robotic manipulators is proposed by the authors. The approach is to represent the kinematic model of a robotic arm with six rotational joints in the form of a vector model. The first 4 vectors make their movements and turn in the same plane, because of the geometric features of the robot. Thus, on the basis of a vector equation, a system of nonlinear algebraic equations was formed, which were solved by the numerical Newton method, at each iteration of which a system of linear algebraic equations was solved by the Seidel method. The algorithm was tested on a trajectory set in the form of an Archimedean spiral approximated by straight line segments. Trajectory was checked by a real robot KUKA KR6 R900 and it was shown that deviation between calculated and real position of flange point was less than 0.005 mm. It is shown that in comparison with the common Denavit–Hartenberg method the method proposed by the authors has an order of magnitude fewer mathematical operations (790 versus 52).

Adaptive control of robot manipulators driven by permanent magnet synchronous motors using orthogonal functions theorem

This paper is devoted to designing a simple tracking controller for robot manipulators driven by permanent magnet synchronous motors (PMSMs) using orthogonal functions. The whole control system including robot manipulator and PMSMs is formulated in such a way that the voltages along the “d” and “q” axes of the PMSMs are considered as the control inputs. In the core of the proposed approach, uncertainties including external disturbances and system dynamics are estimated and compensated using orthogonal functions. Based on the Lyapunov stability theory, the unknown coefficients of the basis functions are tuned online using the derived adaptation laws. A performance evaluation is presented to guarantee the satisfactory operation of the proposed controller in the transient state. The case study is a spherical manipulator driven by PMSMs. The comparative simulation results confirm that the proposed control approach shows a superior performance regarding tracking objectives with a less computational burden.

Parameter estimation and adaptive control of Euler–Lagrange systems using the power balance equation parameterisation

It is widely recognised that the existing parameter estimators and adaptive controllers for robot manipulators are extremely complicated, stymieing their practical use – in particular, for robots with many degrees of freedom. This is mainly due to the fact that the existing parameterisation includes the complicated signal and parameter relations introduced by the Coriolis and centrifugal forces matrix. In an insightful remark of their seminal paper, Slotine and Li suggested to use the parameterisation of the power balance equation, which avoids these terms – yielding significantly simpler designs. To the best of our knowledge, such an approach was never actually pursued in on-line implementations, because the excitation requirements for the consistent estimation of the parameters are ‘very high’. In this paper, we use a recent technique of generation of ‘exciting’ regressors developed by the authors to overcome this fundamental problem. The result is applied to general Euler–Lagrange systems and the fundamental advantages of the new parameterisation are illustrated with comprehensive simulations of a 2 degrees-of-freedom robot manipulator.

Adaptive Passivity-Based Multi-Task Tracking Control for Robotic Manipulators

Adaptive control of robot manipulators based on the use of a sliding variable and the passivity property of the dynamic equations was originally designed and successfully applied to task tracking in joint space few decades ago. Surprisingly, no extension is available to date for the multi-task case such that the controller tends towards enforcing strict task priorities as the parameters tend to their real values. Given the importance of both multi-task control and adaptive algorithms, an approach that deals with this situation has an important impact in the robotic field. This letter provides a solution to this problem leveraging on our recent formulation of hierarchical multi-task impedance control for trajectory tracking and on geometric methods for model identification. Experiments are used to validate the stability analysis.

Predefined performance adaptive control of robotic manipulators with dynamic uncertainties and input saturation constraints

In this paper, a novel adaptive control is investigated for robotic manipulators to unify the study of predefined performance control, input saturation and dynamic uncertainties. The focus is to achieve three user-defined performance indices of the closed-loop system with simultaneous existence of input constraints and model uncertainties, that is overshoot, precision within prescribed finite time and predefined steady-state error. To ensure the performance constraints, an error transformation is constructed for the manipulators by two auxiliary functions and embedded into the barrier Lyapunov function (BLF) in the backstepping analysis. Furthermore, the adaptive control strategies and the adaptive anti-saturation compensator are, respectively, developed to address the dynamics uncertainties and the actuator saturation. The Lyapunov analysis is employed to show that all the closed-loop signals are bounded. Finally, simulation studies and experiments on Baxter robot demonstrate the effectiveness of the proposed method.

Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion

Designing observer-controller structures for nonlinear system with unknown dynamics such as robotic systems is among popular research fields in control engineering. The novelty of this paper is in presenting an observer-based model-free controller for robot manipulators using reinforcement learning (RL). The proposed controller calculates the desired motor voltages that fulfil a satisfactory tracking performance. Moreover, the uncertainties and nonlinearities in the observer model and RL controller are estimated and compensated for by using the Fourier series expansion. Simulation results and comparison with the previous related works (extended state observer and radial basis function neural networks) indicate the satisfactory performance of the proposed method.

Adaptive control of parallel robots with uncertain kinematics and dynamics

One of the most challenging issues in adaptive control of robot manipulators with kinematic uncertainties is the requirement of inverse Jacobian matrix in regressor form. This requirement is inevitable in the case of the control of parallel robots, whose dynamics formulation are derived in the task space. In this paper, an adaptive controller is proposed for parallel robots based on representation of Jacobian matrix in regressor form with asymptotic trajectory tracking. The main idea of this paper is separation of determinant and adjugate of Jacobian matrix in order to represent them into a new regressor forms. Simulation and experimental results on a 2-DOF RPR and a 3-DOF redundant cable driven robot, respectively, verify promising performance of the proposed method in practice.

A New Recursive Composite Adaptive Controller for Robot Manipulators

In this paper, a new recursive implementation of composite adaptive control for robot manipulators is proposed. We investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form, which, to our knowledge, is the first result on this point in the literature. The proposed algorithm has an amount of computation O n , which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree manipulators. The manipulator of the Chinese Space Station is employed as a simulation example, and the results verify the effectiveness of this proposed recursive algorithm.

Parameter Estimation and Adaptive Control of Euler-Lagrange Systems Using the Power Balance Equation Parameterization

It is widely recognized that the existing parameter estimators and adaptive controllers for robot manipulators are extremely complicated to be of practical use. This is mainly due to the fact that the existing parameterization includes the complicated signal and parameter relations introduced by the Coriolis and centrifugal forces matrix. In an insightful remark of their seminal paper Slotine and Li suggested to use the parameterization of the power balance equation, which avoids these terms—yielding significantly simpler designs.To the best of our knowledge, such an approach was never actually pursued, mainly because the excitation requirements for the consistent estimation of the parameters is “very high". In this paper we use a recent technique of generation of “exciting" regressors developed by the authors to overcome this fundamental problem. The result is applied to general Euler-Lagrange systems and the fundamental advantages of the new parameterization are illustrated with comprehensive simulations of a 2 degrees-of-freedom robot manipulator.

Adaptive control of a two-link robot using batch least-square identifier

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties. Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties. Using conventional control algorithms on such systems can cause not only poor control performance, but also expensive computational costs and catastrophic instabilities. Therefore, system uncertainties need to be estimated through designing a computationally efficient adaptive control law. We focus on robot manipulators as an example of a highly nonlinear system. As a case study, a 2-DOF manipulator subject to four parametric uncertainties is investigated. First, the dynamic equations of the manipulator are derived, and the corresponding regressor matrix is constructed for the unknown parameters. For a general nonlinear system, a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters' estimations. Finally, simulation results are discussed for a two-link manipulator, and the performance of the proposed scheme is thoroughly evaluated.

On the adaptive control of robot manipulators with velocity observers

SummaryTo achieve accurate tracking control of robot manipulators many schemes have been proposed. A common approach is based on adaptive control techniques, which guarantee trajectory tracking under the assumption that the robot model structure is perfectly known and linear in the unknown parameters, while joint velocities are available. Despite tracking errors tend to zero, parameter errors do not unless some persistent excitation condition is fulfilled. There are few works dealing with velocity observation in conjunction with adaptive laws. In this note, an adaptive control/observer scheme is proposed for tracking position of robot manipulators. It is shown that tracking and observation errors are ultimately bounded, with the characteristic that when a persistent excitation condition is matched then they, as well as the parameter errors, tend to zero. Simulation results are in good agreement with the developed theory.

Robust adaptive control of robot manipulators using Bernstein polynomials as universal approximator

SummaryThis article presents a robust adaptive controller for electrically driven robots using Bernstein polynomials as universal approximator. The lumped uncertainties including unmodeled dynamics, external disturbances, and nonimplemented control signals (they assumed as a function of time, instead a function of several variables) are represented with this powerful mathematical tool. The polynomial coefficients are then tuned based on the adaptation law obtained in the stability analysis. A comprehensive approach is adopted to include the saturated and unsaturated areas and also the transition between these areas in the stability analysis. As a result, the stability and the performance of the proposed controller have been improved considerably in dealing with actuator saturation. Also, in comparison with a recent paper based on uncertainty estimation using Taylor series, the proposed controller is less computational due to reducing the size of the matrix of convergence rate. A performance evaluation has been carried out to verify satisfactory performance of transient response of the controller. Simulation results on a Puma560 manipulator actuated by geared permanent magnet dc motors have been presented to guarantee its satisfactory performance.

A Novel Adaptive Controller for Robot Manipulators Based on Active Inference

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This paper presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model-free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state-of-the-art model reference adaptive controller (MRAC) in an experimental setup with a real 7-DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.

An Adaptive Controller for Robotic Manipulators with Unknown Kinematics and Dynamics

The ability for kinematics to adapt is important for robotic systems applied in complex environments. This paper focuses on the trajectory control problems of a robotic system with varying kinematics at the end-effector. A new adaptive control approach is developed for robotic manipulators with unknown kinematics and dynamics. This is achieved by using a model-free adaptive controller combined with a kinematics observer. The introduced kinematics observer allows for estimating the kinematic parameters, even under some unconventional application scenarios where typical image processing based techniques are not applicable. Stability of the model-free control with kinematics observer is proven. Control performance and estimation results have been assessed for a wider range of scenarios in a simulation environment which incorporates full nonlinear arm dynamics, finite sampling time and sensor noise.

Experimental Results on the Robust and Adaptive Control of Robot Manipulators Without Velocity Measurements

To achieve accurate tracking control of robot manipulators, many schemes have been proposed. Some common approaches are based on robust and adaptive control techniques, while when necessary velocity observers are employed. Robust techniques have the advantage of requiring few prior information of the robot model parameters/structure or disturbances while tracking can be achieved, for instance, by using sliding mode control. On the contrary, adaptive techniques guarantee trajectory tracking but under the assumption that the robot model structure is perfectly known and it is linear in the unknown parameters, while joint velocities are also available. In this letter, some experiments are carried out to find out whether combining a robust and an adaptive controller may increase the performance of the system, as long as the adaptive term can be treated as a perturbation by the robust controller. The results are compared with an adaptive robust control law, showing that the proposed combined scheme performs better than the separated algorithms, working on their own and then the comparison laws.