Robust Adaptive Compensation Research Articles

Robust adaptive compensation of force-based RTHS testing with uncertain compliance spring and force measurement noise

Robust adaptive compensation of force-based RTHS testing with uncertain compliance spring and force measurement noise

Minimal learning parameters-based simplified robust adaptive saturated control for dynamic positioning ship with input magnitude and rate saturations

In this study, a simplified robust adaptive saturated control strategy based on minimal learning parameters is developed for the dynamic positioning of ship with input magnitude and rate saturations, unknown external environment disturbance and dynamic uncertainties. Firstly, an augment model is developed to restrict the boundedness of the actuators while the input magnitude and rate saturations can be shown in a high-order model. Then, radial basis function neural networks are applied to formulate a new control law via the velocities backstepping method to hand with the dynamic uncertainties. In particular, the minimal learning parameters method is used to reduce the computational complexity, with a single parameter needing to be updated in each step of backstepping. Meanwhile, robust adaptive compensation terms are introduced into the design of virtual and actual control while the error caused by neural networks is mitigated. In line with the Lyapunov theory, the uniformly ultimately boundedness of all signal in closed-loop control system is demonstrated. Finally, simulation is performed to illustrate the advantage and effectiveness of the proposed control strategy.

Disturbance observer–based backstepping sliding mode cascade control of 2–degrees of freedom hydraulic tunneling robot

For the 2–degrees of freedom position tracking problem of the robotized hydraulic-driven roadheader with high nonlinearities and strong uncertainties, a practical disturbance observer–based backstepping sliding cascade control method together with an adaptive compensator is proposed and investigated. The presented methodology mainly includes a continuous nonsingular fast terminal sliding mode with the backstepping technique and the power reaching law with time-varying coefficients used to achieve satisfactory performance against the multi-source disturbances, two disturbance observers used to approximately estimate the unknown dynamics in the mechanical and hydraulic subsystem, respectively. Simultaneously, a continuous robustifying term is also utilized to compensate for the residual disturbances and enhance the robustness. The presented control method doesn’t need the precise model thanks to the auxiliary disturbance observer, and can ensure fast convergence and small tracking error thanks to the backstepping sliding cascade control and the adaptive robust compensator. Based on Lyapunov theory, stability of the overall closed-loop system is proved rigorously, and asymptotically bounded tracking performance of the robotic manipulator is guaranteed. Finally, 2–degrees of freedom trajectory tracking experiments are conducted, and comparative results effectively verify the superiorities of the proposed method.

Pump-Controlled AGC Micro-Displacement Position Control of Lithium Battery Pole Strip Mill Based on Friction Model

Electrode roll-forming refers to rolling a battery electrode into a preset thickness through the electro-hydraulic servo pump-controlled hydraulic roll gap thickness automatic control system (known to as pump-controlled AGC). Compared with the motor servo system, the friction problem of the electro-hydraulic servo system is more serious and the friction problem of the actuator itself is very prominent. Moreover, low-speed performance is one of its core indicators, the friction phenomenon is the most abundant during the low-speed stage and the impact on the servo system is also the most obvious. Therefore, for high-performance electro-hydraulic servo control, friction compensation is not only unavoidable, but also a very difficult problem. Aiming to influence the friction on the position control of the pump-controlled system of a lithium battery pole strip mill, the rolling mechanism and process procedure under micro-displacement position control based on the friction model were studied and compared from the perspective of considering friction factors, and a friction compensation controller based on the LuGre model was designed. The control precision of a pump-controlled AGC system was improved through combination with an adaptive robust controller. Because of the diversity of unmeasurable states of the system, a dual observer was designed, and the known model of the system was added to the observer. In the final comparative experiment, the steady-state accuracy of the friction adaptive robust compensation controller system based on the LuGre model reached ±0.3 μm, which is superior to the fuzzy IMC compensation and traditional PID control strategies.

Robust Control of a Multi-Degree-of-Freedom Electromechanical Plant with Adaptive Disturbance Compensation

This paper considers the problem of control of a multi-degree-of-freedom (multi-DOF) nonlinear electromechanical plant with elastic properties and an external unknown disturbance. The unknown disturbance is assumed to be deterministic and is represented as an output of a linear autonomous model with unknown constant parameters. To solve the problem of adaptive disturbance compensation, an adaptive observer of an unknown deterministic disturbance is applied. A nonlinear robust control of a multi-DOF elastic electromechanical plant, synthesized on the basis of the integrator backstepping method and combined with adaptive robust disturbance compensation, is also being developed. The simulation of the designed nonlinear control of a multi-DOF plant with adaptive robust compensation of disturbances is performed using the MATLAB / Simulink software.

Decoupled Torque Control of Series Elastic Actuator With Adaptive Robust Compensation of Time-Varying Load-Side Dynamics

Modern robots are increasingly being designed to collaborate with human, and the safety of human-robot interaction has become more significant than ever. Series elastic actuator (SEA) is a potential actuator system in force control for various robotic applications with inherent safe property. However, achieving the good performances of SEA torque control especially with unknown load-side dynamics is still challenging. In this article, a high performance decoupled torque controller is proposed to control the SEA as an ideal torque source that is suitable for various application scenarios. A more general decoupled dynamics of the SEA system is first established to fit the various scenarios by considering the large parametric uncertainties, nonlinearities, and unmodeled uncertainties. The adaptive robust control algorithm with an effective compensation mechanism is then developed to attenuate the uncertainties. Theoretically, the proposed approach guarantees the stability and high performance of the SEA system. Comparative experiments are carried out on a SEA testbed, and the experimental results demonstrate that the proposed approach has significant performance improvements over previous approaches.

Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults

This paper studies a robust adaptive compensation Fault Tolerant Control (FTC) for the medium-scale Unmanned Autonomous Helicopter (UAH) in the presence of external disturbances, actuator faults and input saturation. To improve the disturbance rejection capacity of the UAH system in actuator healthy case, an adaptive control method is adopted to cope with the external disturbances and a nominal controller is proposed to stabilize the system. Meanwhile, compensation control inputs are designed to reduce the negative effects derived from actuator faults and input saturation. Based on the backstepping control and inner-outer loop control technologies, a robust adaptive FTC scheme is developed to guarantee the tracking errors convergence. Under the presented FTC controller, the uniform ultimate boundedness of all closed-loop signals is ensured via Lyapunov stability analysis. Simulation results demonstrate the effectiveness of the proposed control algorithm.

Adaptive Neural Compensator for Robotic Systems Control

In the area of robotics systems, there are numerous applications where robots are expected to move rapidly from one place to another, or follow desired trajectories while maintaining good dynamic behavior. However, certain non-linearities, uncertainties in dynamics and external perturbations make the design of ideal controllers a complicated task in many situations. In this paper, we propose a control scheme that combines a nominal feedback controller with a classical PD and a robust adaptive compensator based on artificial neural networks. Using this control scheme, it is possible to obtain a fully tuned compensation parameters and a strong robustness with respect to uncertain dynamics and different non-linearities, as well as to keep the output tracking error bounded to values close to zero. In order to show the performance of the proposed technique, a SCARA (Selective Compliant Articulated Robot Arm) type robot with two degrees of freedom is considered in this case; but this control proposal can be applied to different systems with dynamic variations. The efficiency and performance of the control law is demonstrated through simulation results and the stability analysis is carried out using Lyapunov's theory.

Precise motion control of an electromagnetic valve actuator with adaptive robust compensation of combustion force

Electromagnetic valve actuators (EMVA) can achieve independent fully flexible control of valve and thus reduce the fuel consumption significantly. In order to improve the performance of the engine, precise motion control of EMVA is requird. Most of the researches published so far focus on the compensations of nonlinearities such as frictions and parameters variations. However, the influence of the varying combustion force on the exhaust valve needs to be considered. An adaptive robust control (ARC) method was developed in this paper to compensate the major nonlinearities, including parameters variations and combustion force variations. The parameters of EMVA system were tuned online via certain adaptation law. Combustion force variations were compensated by robust control law. In addition, current saturation was caused by large combustion force, which was then solved by proposing an anti-windup compensation strategy. The effectiveness of the proposed ARC method was verified by simulations and experiments, and the results show that ARC method can adapt to parameters variations and large changes in combustion force.

ADAPTIVE-BACKSTEPPING POSITION CONTROL BASED ON RECURRENT-FWNNS FOR MOBILE MANIPULATOR ROBOT

In this paper, we proposed an adaptive-backstepping position control system for mobile manipulator robot (MMR). By applying recurrent fuzzy wavelet neural networks (RFWNNs) in the position-backstepping controller, the unknown-dynamics problems of the MMR control system are relaxed. In addition, an adaptive-robust compensator is proposed to eliminate uncertainties that consist of approximation errors and uncertain disturbances. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. The effectiveness of the proposed method is verified by comparative simulation results.

Adaptive robust control strategy for rhombus-type lunar exploration wheeled mobile robot using wavelet transform and probabilistic neural network

In this paper, we propose a stable tracking control rule for rhombus-type lunar exploration wheeled mobile robot (RLEWMR) with completely unknown dynamics and unmodeled disturbance. The control system adopts a wavelet transform and probabilistic neural network (WTPNN) with accurate approximation capability to represent the unknown dynamics of the RLEWMR, and it also uses an adaptive robust compensator to confront the inevitable approximation errors due to the finite number of wavelet bases functions and to disturbances. Adaptive learning algorithms are proposed to learn the parameters of WTPNN weight and robust compensator on line. Based on the Lyapunov stability theorem, the tracking stability of the closed-loop system, the convergence of the WTPNN weight-updating process, and boundedness of WTPNN weight estimation errors are all guaranteed. The effectiveness and efficiency of the proposed controller is demonstrated by simulation and experiment studies.

Neural adaptive output feedback formation control of type ( m , s ) wheeled mobile robots

This study introduces a general framework for the formation tracking control of all types of wheeled mobile robots (WMRs). On the basis of a coordinate transformation, a second-order input-output model is developed for the general type ( m , s ) WMR with the mobility m and the steerability s . Then, a saturated feedback linearising controller in conjunction with a non-linear velocity observer is proposed in order to leave out the velocity sensors in all agents. In addition, a radial basis function neural network and an adaptive robust compensator are incorporated in the formation controller design to improve the tracking performance in the presence of uncertain non-linearities and unknown parameters. Semi-global stability of the closed-loop formation system is proved using direct Lyapunov method. Moreover, a generalisation of the proposed controller is introduced for the cooperative control of mobile manipulators. Finally, simulation examples are given to illustrate the effectiveness of the proposed formation control system for two types of WMRs.

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control (ABFCNC) system for motion/force control of the mobile-manipulator robot (MMR) is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.

Robust Adaptive Compensation of Periodic Disturbances With Unknown Frequency

Robust Adaptive Compensation of Periodic Disturbances With Unknown Frequency

Adaptive Force/Motion Control System Based on Recurrent Fuzzy Wavelet CMAC Neural Networks for Condenser Cleaning Crawler-Type Mobile Manipulator Robot

In this paper, an adaptive motion/force control system is proposed for condenser cleaning crawler-type mobile manipulator robot (CCCMMR). With the merits of recurrent fuzzy wavelet cerebellar model articulation control neural networks, the unknown dynamics and parameter variations of the CCCMMR control system are relaxed by an approximation process. In addition, an adaptive robust compensator is also proposed to eliminate uncertainties that consist of the unknown approximation errors and disturbances. According to the adaptive position tracking control design, we develop an adaptive robust control strategy for the nonholonomic constraint force of the CCCMMR. The design of the adaptive online learning algorithms is derived using the Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability and robustness, but also the tracking performances of the CCCMMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation and experimental results.

Computed-torque plus robust adaptive compensation control for robot manipulator with structured and unstructured uncertainties

Computed-torque plus robust adaptive compensation control for robot manipulator with structured and unstructured uncertainties

Adaptive motion/force control strategy for non-holonomic mobile manipulator robot using recurrent fuzzy wavelet neural networks

In our study, we develop an adaptive position tracking system and a force control strategy for non-holonomic mobile manipulator robot, which combine the merits of Recurrent Fuzzy Wavelet Neural Networks (RFWNNs). In order to deal with the unknown knowledge problems of the robotic system, an adaptive RFWNNs control scheme with the dynamic structure and online learning ability is utilized to approximate unknown dynamics without the requirement of prior controlled system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances. According to the adaptive position tracking control design, an adaptive robust controller is also considered for the non-holonomic constraint force. The design of the adaptive online learning algorithms is derived by using the Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability but also the tracking performance of the mobile manipulator robot control system. The effectiveness and robustness of the proposed method are demonstrated by comparative simulation and experimental results that are implemented in an indoor cleaning crawler-type mobile manipulator robot system.

Adaptive-WFCNNs-backstepping force/motion control system for mobile-manipulator robot

Purpose – The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties. Design/methodology/approach – The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem. Findings – The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed. Originality/value – The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.

Adaptive Position Tracking System and Force Control Strategy for Mobile Robot Manipulators Using Fuzzy Wavelet Neural Networks

In this paper, we propose an adaptive position tracking system and a force control strategy for nonholonomic mobile robot manipulators, which incorporate the merits of Fuzzy Wavelet Neural Networks (FWNNs). In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of mobile robot manipulators control system, such as unknown dynamics, disturbances and parameter variations. To solve this problem, an adaptive FWNNs control scheme with the online learning ability is utilized to approximate the unknown dynamics without the requirement of prior system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances, optimal parameters and higher order terms in Taylor series. According to adaptive position tracking control design, an adaptive robust control strategy is also considered for nonholonomic constraint force. The design of adaptive online learning algorithms is derived using Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability of mobile robot manipulators control system but also guarantee tracking performance. The effectiveness and robustness of the proposed method are demonstrated by comparing simulations and experimental results that are implemented in an indoor cleaning crawler-type mobile robot manipulators system.

Adaptive Robust Quadratic Stabilization Tracking Control for Robotic System with Uncertainties and External Disturbances

An adaptive robust quadratic stabilization tracking controller with hybrid scheme is proposed for robotic system with uncertainties and external disturbances. The hybrid scheme combines computed torque controller (CTC) with an adaptive robust compensator, in which variable structure control (VSC) andH∞optimal control approaches are adopted. The uncertain robot manipulator is mainly controlled by CTC, the VSC is used to eliminate the effect of the uncertainties and ensure global stability, andH∞approach is designed to achieve a certain tracking performance of closed-loop system. A quadratic stability approach, which allows separate treatment of parametric uncertainties, is used to reduce the conservatism of the conventional robust control approach. It can be also guaranteed that all signals in closed-loop system are bounded. The validity of the proposed control scheme is shown by computer simulation of a two-link robotic manipulator.