Search results

The purpose of the paper is to design a robust control system for a generic hypersonic vehicle which includes dynamic nonlinear, open loop unstable and parametric uncertainties.

For a complex longitudinal model of a generic hypersonic vehicle which includes dynamic nonlinear, open loop unstable and parametric uncertainties, a nonlinear dynamic inverse (NDI) approach combined with proportional differential (PD) control is used to design a strong robust control system to deal with the sensitivity to changes of atmosphere condition. In this way, a simple genetic algorithm is used to search a group of parameters of the control system to satisfy the specific performance indices. Then parametric uncertainties are considered to verify the robustness of the control system.

The PD hypersonic vehicle control system using NDI approach can satisfy the specific flight performance. And it has strong robustness under the parametric uncertainties.

The paper fulfills a complete process of the nonlinear control system design for a generic hypersonic vehicle. And, the simulation results show the efficiency and robustness of the control system.

This study aims to investigate the dynamic motion of an ultrasonic machining system comprising two Duffing oscillators, each with a single degree of freedom. After derivation of the differential equations of the system using the Lagrange equations and dimensionless time, numerical analysis was used to observe changes in the system caused by differences in excitation frequency.

To suppress this effect and improve performance, proportional differential (PD) control was used. The integral absolute error was used as the fitness function, and particle swarm optimization was used to find the best value for the gain constant of the PD controller.

The results showed that with specific changes of excitation frequency, the dynamic motion of the system became nonlinear and chaotic behavior resulted. This made the system unstable and affected performance.

A range of methods, including fuzzy control, was used to analyze the results, and exhaustive laboratory work was carried out. Means of control were found that were effective in suppressing the chaotic behavior, and differences in response to control were investigated and verified. The findings of this study can be used as a basis for system parameter settings or control circuit design.

This paper aims to present a control method to realize the constant force grinding of automobile wheel hub.

A constant force control strategy combined by extended state observer (ESO) and backstepping control is proposed. ESO is used to estimate the total disturbance to improve the anti-interference and stability of the system and Backstepping control is used to improve the response speed of the system.

The simulation and grinding experimental results show that, compared with the proportional integral differential control and active disturbance rejection control, the designed controller can improve the dynamic response performance and anti-interference ability of the system and can quickly track the expected force and improve the grinding quality of the hub surface.

The main contribution of this paper lies in the proposed of a new constant force control strategy, which significantly improved the stability and precision of grinding force.

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.

A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.

Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.

In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

This study aims to construct a novel maximum power tracking control system for the direct drive permanent magnet synchronous generator (PMSG) of the wind energy conversion system (WECS) to solve the following problems: how to effectively eliminate the system’s model parameter disturbances and speed up the dynamic performance of the system; and how to eliminate harmonics in WECS under different wind speeds.

To obtain the maximum output power of PMSG at WECS under different wind speeds, the following issues should be considered: (1) how to effectively eliminate the system’s model parameter disturbances and speed up the dynamic performance of the system; and (2) how to suppress system harmonics. For Problem 1, adding d–q compensation factors to active disturbance rejection control (ADRC) for the current loop realizes the d–q axis decoupling control, which speeds up the dynamic performance of the system. For Problem 2, the resonant controller is introduced into the ADRC for the current loop to suppress harmonic current in WECS under different wind speeds.

The simulation results demonstrate that the proposed control method is simpler and more reliable than conventional controllers for maximum power tracking.

Compared with traditional controllers, the proposed controller can speed up the dynamic performance of the system and suppress the current harmonic effectively, thus better achieving maximum power tracking.

The purpose of this paper is to focus on the accurate and steady control on trajectory tracking for wafer transfer robot, suppress the vibration and reduce the contour error.

The wafer transfer robot dynamic model is modeled. Through analyzing the characteristics of wafer transfer robot, cross‐coupled synchronized control is proposed based on the contour error model in task space to improve synchronization of the joints; the shaping for the joints by input shaper in task space is applied to suppress the vibration of the end effector during trajectory tracking. Then combining the cross‐coupled synchronized control with input shaping is proposed to improve accuracy and suppress the vibration.

The combination of cross‐coupled synchronized control and input shaping control method can improve the contour accuracy and reduce the vibration simultaneously during trajectory tracking. And the control method can be used to control the trajectory of wafer transfer robot.

The transfer station is in the center of the robot body. When the transfer station may deviate from the center of the robot body, the synchronizing performance of three axes on the same plane must be considered.

The proposed method can be used to solve the vibration and synchronizing performance problems on similar SCARA robots in semi‐conductor and liquid crystal display industry.

The proposed control method takes advantage of the cross‐coupled synchronized control and input shaping control method. This combination has improved contour accuracy and reduced vibration than applying other methods, and it has achieved better performance than using single one control method only.

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.

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.

The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed.

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.

The purpose of this paper is to solve the ground verification and test method for space robot system capturing the target satellite based on visual servoing with time-delay in 3-dimensional space prior to space robot being launched.

To implement the approaching and capturing task, a motion planning method for visual servoing the space manipulator to capture a moving target is presented. This is mainly used to solve the time-delay problem of the visual servoing control system and the motion uncertainty of the target satellite. To verify and test the feasibility and reliability of the method in three-dimensional (3D) operating space, a set of ground hardware-in-the-loop simulation verification systems is developed, which adopts the end-tip kinematics equivalence and dynamics simulation method.

The results of the ground hardware-in-the-loop simulation experiment validate the reliability of the eye-in-hand visual system in the 3D operating space and prove the validity of the visual servoing motion planning method with time-delay compensation. At the same time, owing to the dynamics simulator of the space robot added in the ground hardware-in-the-loop verification system, the base disturbance can be considered during the approaching and capturing procedure, which makes the ground verification system realistic and credible.

The ground verification experiment system includes the real controller of space manipulator, the eye-in-hand camera and the dynamics simulator, which can veritably simulate the capturing process based on the visual servoing in space and consider the effect of time delay and the free-floating base disturbance.

The purpose of this paper is to develop an automatic control system for mechanical ventilation therapy based on the open lung concept (OLC) using artificial intelligence. In addition, mean arterial blood pressure (MAP) is stabilized by means of a decoupling controller with automated noradrenaline (NA) dosage to ensure adequate systemic perfusion during ventilation therapy for patients with acute respiratory distress syndrome (ARDS).

The aim is to develop an automatic control system for mechanical ventilation therapy based on the OLC using artificial intelligence. In addition, MAP is stabilized by means of a decoupling controller with automated NA dosage to ensure adequate systemic perfusion during ventilation therapy for patients with ARDS.

This innovative closed-loop mechanical ventilation system leads to a significant improvement in oxygenation, regulates end-tidal carbon dioxide for appropriate gas exchange and stabilizes MAP to guarantee proper systemic perfusion during the ventilation therapy.

Currently, this automatic ventilation system based on the OLC can only be applied in animal trials; for clinical use, such a system generally requires a mechanical ventilator and sensors with medical approval for humans.

For implementation of a closed-loop ventilation system, reliable signals from the sensors are a prerequisite for successful application.

The experiment with porcine dynamics demonstrates the feasibility and usefulness of this automatic closed-loop ventilation therapy, with hemodynamic control for severe ARDS. Moreover, this pilot study validated a new algorithm for implementation of the OLC, whereby all control objectives are fulfilled during the ventilation therapy with adequate hemodynamic control of patients with ARDS.

The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.

The robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.

The experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.

As the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.