Tracking Control Of Robot Manipulators Research Articles

Adaptive trajectory tracking control of robotic manipulators based on integral sliding mode

AbstractThis paper proposes an adaptive control scheme with finite‐time convergent property based on integral sliding mode to achieve trajectory tracking control of rigid robotic manipulators. The novel adaptive gain can be adjusted automatically with the system disturbance, as long as the disturbance and its derivative are bounded without requiring more information. Therefore, the control chattering effect can be mitigated obviously. The global robustness of system is guaranteed by Lyapunov stability analysis. Finally, the simulation of a two‐link manipulator is given to show that the desired trajectory tracking performance is obtained with the proposed adaptive sliding mode approach.

Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics

Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics

Event-triggered based predefined-time tracking control for robotic manipulators with state and input quantization

Event-triggered based predefined-time tracking control for robotic manipulators with state and input quantization

Event-triggered learning-based robust tracking control for robotic manipulators with uncertain dynamics and non-zero equilibrium

This paper presents a novel optimal tracking control method for robotic manipulators (RMs) with system uncertainties and non-zero equilibrium points. The existing tracking control methods typically require the precise system dynamics and assume the existence of a zero equilibrium point, which may not always be true in practice. To overcome this issue, a new cost function with respect to the first derivative of system states and a known upper bound on the system’s mismatched disturbances is designed, and then an optimal control problem of RMs is formulated. Furthermore, to solve this optimization, an event-triggered critic learning control framework is proposed to estimate the solution of Hamilton–Jacobi–Bellman equation, reducing the computational burden. In the critic learning control designs, novel weight updating laws related to system stability information are proposed to train the critic neural networks, which eliminates the initial stabilization requirement. Theoretical analysis shows that the stability of the closed-loop system in the sense of uniform ultimate boundedness is ensured. It should be emphasized that the proposed control method offers a simplification of the tracking controller design process and removes the restriction on system dynamics. Finally, simulation results are given to demonstrate the effectiveness of the proposed method.

Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

Event-triggered stochastic finite-time tracking control of robot manipulator with uncertain disturbance neural network estimation

Zeroing Neural Network With Coefficient Functions and Adjustable Parameters for Solving Time-Variant Sylvester Equation.

To solve the time-variant Sylvester equation, in 2013, Li et al. proposed the zeroing neural network with sign-bi-power function (ZNN-SBPF) model via constructing a nonlinear activation function. In this article, to further improve the convergence rate, the zeroing neural network with coefficient functions and adjustable parameters (ZNN-CFAP) model as a variation in zeroing neural network (ZNN) model is proposed. On the basis of the introduced coefficient functions, an appropriate ZNN-CFAP model can be chosen according to the error function. The high convergence rate of the ZNN-CFAP model can be achieved by choosing appropriate adjustable parameters. Moreover, the finite-time convergence property and convergence time upper bound of the ZNN-CFAP model are proved in theory. Computer simulations and numerical experiments are performed to illustrate the efficacy and validity of the ZNN-CFAP model in time-variant Sylvester equation solving. Comparative experiments among the ZNN-CFAP, ZNN-SBPF, and ZNN with linear function (ZNN-LF) models further substantiate the superiority of the ZNN-CFAP model in view of the convergence rate. Finally, the proposed ZNN-CFAP model is successfully applied to the tracking control of robot manipulator to verify its practicability.

Asymptotic consensus tracking control of robotic manipulators with disturbance and dead-zone via adaptive sliding mode control

This article aims at solving the difficulties of robotic manipulator systems with unknown disturbance and dead-zone in consensus trajectory tracking. For the purpose of accelerating the tracking process of leader and follower manipulators, a novel sliding mode control strategy is proposed. For compensating the influence of unknown lumped disturbance and control dead-zone, a novel adaptive law is designed for the construction of controller. With the help of adaptive sliding mode controller, based on the Lyapunov stability theory, the over-estimation is alleviated and the tracking errors converge to zero asymptotically. The simulations demonstrate the validity of the proposed strategy for consensus asymptotic trajectory tracking.

Observer-based adaptive tracking control of robotic manipulators with predefined time-guaranteed performance: Theory and experiment

This paper investigates the challenging problem of fixed time trajectory tracking for robotic manipulators under the presence of unavailable model perturbation, external disturbance and from different initial states. Firstly, a novel fixed-time extended state observer (FESO) is designed to estimate and compensate the lumped disturbance, which is analyzed and proved to be stable in the sense of fixed time bounded stability. Secondly, a new type of fixed-time prescribed performance control (FPPC) is constructed to guarantee the system convergences to stable state within a predefined time and enhance transient performance. Furthermore, a novel continuous fixed time nonsingular fast terminal sliding mode variable is established, which addresses singularity obstacle in terminal sliding mode. Together with FESO and FPPC, a new fixed-time adaptive nonsingular fast terminal sliding mode controller (FANFTSMC) is developed. Meanwhile, an adaptive terminal sliding mode reaching law adopted in FANFTSMC promotes the robustness and decreases the chattering phenomenon. Then, the Lyapunov approach is given to clarify the advantages of FANFTSMC in terms of predefined time stabilization and which is demonstrated on a 2-DOF robotic manipulators. Thirdly, theoretical analysis and experiments are presented to illustrate the formulated control strategy owns fine performance and stronger robustness.

Tracking control of robot manipulators actuated by brushless DC motors: Elimination of joint velocity measurements with a robust observer

AbstractThis work introduces an innovative robust partial state feedback controller designed for robotic manipulators actuated by brushless DC motors. The proposed controller/observer structure relies solely on actuator current and robot joint position measurements, while effectively compensating for dynamic uncertainties in both the electrical (actuator dynamics) and mechanical (robot's dynamical terms) subsystems. Specifically, a model–based robust observer, eliminating the need of joint velocity measurements, is combined with a backstepping‐type controller design. As opposed to the previous model based observers that depend on the actual model parameters, the proposed observer structure utilizes the best guest estimates of the system parameters supported with a robust compensation term. This approach eliminates the need for precise knowledge of system parameters of the observer design which is a significant improvement over most of the previous results. This approach ensures the semi‐global, uniformly ultimately bounded joint tracking error signal. The overall stability of the closed‐loop system is affirmed through Lyapunov‐based arguments. Experimental studies conducted on an in‐house‐built two‐link robotic manipulator, actuated by brushless DC motors, are presented to demonstrate the effectiveness and feasibility of the proposed method.

Uncalibrated and Unmodeled Image-Based Visual Servoing of Robot Manipulators Using Zeroing Neural Networks.

Neural networks have been widely investigated for the control of robot manipulators and recurrent neural network (RNN) is accepted as a powerful tool for visual servoing. Different from existing control schemes for robot-camera systems, this article proposes a novel image-based visual servoing (IBVS) control scheme for both the regulation and tracking control of robot manipulators in the framework of a special class of RNN, termed zeroing neural network (ZNN), which does not require prior knowledge about camera configuration and kinematic model parameters. The proposed control scheme is composed of a data-driven mapping estimator and a controller, both of which are designed based on ZNN. To facilitate the deployment of the proposed IBVS control scheme, a discrete-time version of the proposed control scheme is developed. Theoretical analysis for the proposed method is presented in terms of convergence, stability, and robustness. In addition, simulations and experiments are carried out based on different types of robot-camera systems to verify the efficacy and portability of the proposed control scheme for solving regulation and trajectory IBVS problems. Moreover, comparative studies are performed to reveal the merits of the proposed control scheme.

Event-Triggered-Based Sliding Mode Asymptotic Tracking Control of Robotic Manipulators

Event-Triggered-Based Sliding Mode Asymptotic Tracking Control of Robotic Manipulators

Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer

This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.

Tracking control for robotic manipulators with prescribed performance and adaptive sliding mode disturbance observer

This paper investigates the finite-time disturbance estimation and tracking control for robotic manipulators subjected to model perturbation and external disturbance. First, an adaptive sliding mode disturbance observer (ASMDO) is raised to estimate the lumped uncertainties, which does not need to consider the upper boundary prior knowledge of lumped uncertainties’ derivative, and excessive gain coefficients are avoided, meanwhile, which decrease the chattering phenomenon. Next, a finite-time prescribed performance control (FPPC) is established to guarantee the system converges within a finite time and promote transient performance. Then, together with the FPPC, a finite-time nonsingular fast terminal sliding mode variable is established, which handles singularity problem in terminal sliding mode. In the meantime, a finite-time adaptive nonsingular fast terminal sliding mode controller (FANFTSMC) is constructed based on ASMDO and FPPC. The Lyapunov theorem is given to clarify that the system can reach the stable state within a finite time. Theoretical analysis and experiments reveal that FANFTSMC has better tracking performance and stronger robustness.

Adaptive control of BLDC driven robot manipulators in task space

AbstractIn this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.

Adaptive consensus tracking control for robotic manipulators with nonlinear time‐varying fault‐tolerant actuator and unknown control input directions

SummaryThe consensus tracking mechanism of a multi‐agent system consisting of third‐order nonlinear single joint manipulators was investigated in this study based on the pertinent industrial context. Unlike many previous studies on consensus control that presumed a known control direction, this investigation addressed the complex challenges posed by unknown control directions and time‐varying actuators. Specifically, we explored both time‐invariant and time‐varying actuator faults in scenarios wherein all parameters are unknown. The Nussbaum function was utilized to resolve time‐varying nonlinear fault‐tolerance and unknown control direction problems. In the design process of the control law, the backstepping method combining with Nussbaum function is adopted to address unmatched nonlinearity and unmatched uncertainty system, and the radial basis function neural network is used to approximate the unknown parameters The resulting control law allows all robotic manipulators to track the angle and angular velocity of the leader. Simulation results demonstrate the effectiveness of the proposed control scheme.

Disturbance Rejection Event-Triggered Robust Model Predictive Control for Tracking of Constrained Uncertain Robotic Manipulators.

A novel hierarchical control framework combining computed-torque-like control (CTLC) with disturbance-observer-based event-triggered robust model predictive control (DO-ET-RMPC) is proposed for the trajectory tracking control of robotic manipulators with bounded disturbances and state and control input constraints. The CTLC approach is first used to cancel the exact nonlinear dynamics of the original tracking error system to obtain a set of decoupling linear tracking error subsystems, thus reducing the optimization complexity of model predictive control (MPC). The composite DO-ET-RMPC scheme is then developed based on the so-called dual-mode MPC approach to robustly stabilize the tracking error subsystems, which could improve the robustness of MPC and save its computational resources simultaneously. The continuous-time theoretical properties of the DO-ET-RMPC scheme, considering disturbances and state and control input constraints simultaneously, are provided for the first time, including the avoidance of Zeno behavior, robust constraint satisfaction, recursive feasibility, and stability. In the end, the superiorities of the proposed control scheme are verified by the comparative simulations.

Optimal Fully Actuated System Approach-Based Trajectory Tracking Control for Robot Manipulators.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

Analysis of Bilinear Force Tracking Control for Robot Manipulators Under Unknown Environment

Analysis of Bilinear Force Tracking Control for Robot Manipulators Under Unknown Environment

Fixed-time terminal sliding mode control for uncertain robot manipulators

This paper proposes a fixed-time tracking control for robot manipulators in the presence of parametric uncertainties and disturbances. An auxiliary function is first proposed for constructing a fixed-time sliding manifold. Benefited from this fixed-time sliding manifold, a singularity-free robust control is proposed to evade the effects of algebraic loop problem of the commonly-used sliding mode controls (SMC). The key advantages of the proposed approach are: (i) exact fixed-time stability featuring the convergence time does not relate to the initial conditions and is acquired in advance; (ii) the singularity and algebraic loop problems are eliminated completely; (iii) a simple and intuitive control structure is used for easy implementation of trajectory tracking control for uncertain robot manipulators with faster transient and higher steady-state precision. Simulations and experimental comparisons validate the improved tracking performance of the proposed approach.

A GES joint position trajectory tracking smooth controller of torque‐driven robot manipulators affected by disturbances

SummaryThis paper presents a controller for joint position tracking of torque‐driven robot manipulators affected by torque disturbances. In particular, the proposed approach allows concluding global exponential stability (GES) of the state‐space origin of the closed‐loop system when constant disturbances affect the robot dynamics. Besides, when time‐varying disturbances are presented, the trajectories of the closed‐loop system are proven to be bounded as result of the input‐to‐state stability. The main contribution is the design of the control law and a nonlinear observer based on an alternative energy shaping approach. The nonlinear observer is designed to compute and compensate for unknown disturbances, whether they are constant or time‐varying. Furthermore, a detailed stability analysis of the closed‐loop system based on Lyapunov theory and input‐to‐state stability is presented. As far as the authors know, this is the first energy‐shaping controller for trajectory tracking control of robot manipulators affected by constant disturbances that achieves global exponential stability. Real‐time experiments on a manipulator arm of two degrees‐of‐freedom illustrate the performance of the proposed controller.