Search results

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

The purpose of this paper is to propose a more efficient strategy, which is easier to implement, i.e. the engineer can directly operate the target object without the robot to do a demonstration, and the manipulator is regulated to track the trajectory using vision feedback repetitively. Generally, the applications of industrial robotic manipulators are based on teaching playback strategy, i.e. the engineer should directly operate the manipulator to perform a demonstration and then the manipulator uses the recorded driving signals to perform repetitive tasks.

In the teaching process, the engineer grasps the object with a camera on it to do a demonstration, during which a series of images are recorded. The desired trajectory is defined by the homography between the images captured at current and final poses. Tracking error is directly defined by the homography matrix, without 3D reconstruction. Model-free feedback-assisted iterative learning control strategy is used for repetitive tracking, where feed-forward control signal is generated by iterative learning control strategy and feedback control signal is generated by direct feedback control.

The proposed framework is able to perform precise trajectory tracking by iterative learning, and is model-free so that the singularity problem is avoided which often occurs in conventional Jacobean-based visual servo systems. Besides, the framework is robust to image noise, which is shown in simulations and experiments.

The proposed framework is model-free, so that it is more flexible for industrial use and easier to implement. Satisfactory tracking performance can be achieved in the presence of image noise. System convergence is analyzed and experiments are provided for evaluation.

This paper is concerned with the repetitive trajectory tracking control for space manipulators under model uncertainties and vibration disturbances.

The model uncertainties and link vibration of manipulators will degrade the tracking performance of space manipulators; in this paper, a new hybrid control scheme that consists of a composite hierarchical anti-disturbance controller and an iterative learning controller is developed to solve this problem.

The composite hierarchical controller can effectively attenuate model uncertainties and reject vibration disturbances, whereas the iterative learning controller is able to improve the tracking accuracy for repetitive reference trajectory.

The proposed scheme compensates for the shortcomings of iterative learning control which can only deal with repetitive disturbances, ensuring the accuracy and repeatability of space manipulators under model uncertainties and random disturbances.

The purpose of this paper is to provide a maximum speed algorithm for serial palletizing robots, which guarantees relatively low system modeling requirements and can be easily implemented in actual applications.

Operation speed is an important index of palletizing robots performance. In order to improve it, features of palletizing motions are analyzed, and a refined iterative learning control algorithm for maximum speed optimization is proposed. The refined algorithm learns to increase local speed when the following error does not exceed a predefined tolerance, unlike conventional applications which make actual output identical to its reference. Furthermore, experiments were developed to illustrate the new algorithm's ability to take full advantage of motor capacity, drive ability and repetitive link couplings to improve palletizing efficiency.

Experiments show that motion time decreases more than 20 percent after optimization.

The new iterative control algorithm can be easily applied to any repetitive handling operations where manipulating efficiency matters.

This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional-integral-derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique.

Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in-house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real-time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of linear synchronous motor with a PC-based real-time ILC.

Simulations and experiment results show that the proposed controller can reduce the error significantly after two learning iterations. The developed method using bounded constraints technique provides valuable programming tools to practicing engineers.

The proposed IPSO-ILC-PID controller overcomes the shortcomings of conventional ILC-PID controller with fixed gains. Simulation and experimental results show that the proposed IPSO-ILC-PID algorithm exhibits great speed convergence and robustness. Experimental results confirm that the proposed IPSO-ILC-PID algorithm is effective and achieves better control in real-time precision positioning.

When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios.

Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm.

Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm.

This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

Due to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved.

The hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency.

A series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled.

Due to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.

The purpose of this paper is to describe development and application of an iterative learning control (ILC) scheme for a tracking problem. The control objective is to achieve accurate tracking of a desired trajectory which is the path taken by a laser beam.

It involves formulating an ILC scheme in two‐dimensional (2D) representation on mathematical model of two degrees of freedom platform. The scheme was tested and fine tuned with the help of simulation results on that model. Subsequently, an experimental setup was prepared by mounting a camera on a six degree of freedom hexapod, M‐850 from Physik Instrumente. The experimental setup was made to track an arbitrarily positioned laser spot on a screen. For this purpose, a simple image processing module was also developed. The underlying algorithm implemented learning and tracking modes.

The tracking performance of the scheme is impressive. The simulations as well as practical results show that the scheme is robust and simple to implement.

The limitation is the time spent in learning mode before the control function is applied to the system under consideration. This, however, is an inherent aspect in any ILC scheme.

Its application can be in manufacturing processes, robotics, target tracking and even in bio engineering where growth of some specific bacteria population could also be tracked.

Little work, with practical implementations, has been reported in ILC. The authors perceive that this scheme has the potential to simplify a great number of control problems especially in the field of robotics and trajectory tracking.

The purpose of this paper is to propose a precise time-optimal path tracking approach for robots under kinematic and dynamic constraints to improve the work efficiency of robots and guarantee tracking accuracy.

In the proposed approach, the robot path is expressed by a scalar path coordinate and discretized into N points. The motion between two neighbouring points is assumed to be uniformly accelerated motion, so the time-optimal trajectory that satisfies constraints is obtained by using equations of uniformly accelerated motion instead of numerical integration. To improve dynamic model accuracy, the Coulomb and viscous friction are taken into account (while most publications neglect these effects). Furthermore, an iterative learning algorithm is designed to correct model-plant mismatch by adding an iterative compensation item into the dynamic model at each discrete point before trajectory planning.

An experiment shows that compared with the sequential convex log barrier method, the proposed numerical integration-like (NI-like) approach has less computation time and a smoother planning trajectory. Compared with the experimental results before iteration, the torque deviation, tracking error and trajectory execution time are reduced after 10 iterations.

As the proposed approach not only yields a time-optimal solution but also improves tracking performance, this approach can be used for any repetitive robot tasks that require more rapidity and less tracking error, such as assembly.

The purpose of this paper is to present a coordinated control strategy for stable walking of biped robot with heterogeneous legs (BRHL), which consists of artificial leg (AL) and intelligent bionic leg (IBL).

The original concentrated control in common biped robot system is replaced by a master‐slave dual‐leg coordinated control. P‐type open/closed‐loop iterative learning control is used to realize the time‐varying gait tracking for IBL to AL.

The new control architecture can simplify gait planning scheme of BRHL system with complicated closed‐chain mechanism and mixed driving mode.

Designing and constructing a suitable magneto‐rheological damper can greatly improve the control performance of IBL.

Master‐slave coordination strategy is suitable for BRHL stable walking control.

The concepts and methods of dual‐leg coordination have not been explicitly proposed in single biped robot control research before. Master‐slave coordinated control strategy is suitable for complicated BRHL.