Iterative state feedback control and its application to robot control

The repetitive processing and large quantity of single product represented by 3C products are urgently needed. However, for current processing operations, previous processing data have not been used in the optimization of control input. In order to utilize previous processing data to facilitate the next process and avoid adverse effects caused by repetitive disturbance and noise, the idea of iterative learning was introduced to improve the accuracy of machining. On the control level, since it is difficult to obtain high accuracy by traditional feedback control when faced with complex trajectories, an open-loop iterative learning controller and a position loop feedback controller were introduced, which worked fast with good convergence effects. Aiming at reducing the influence of accidental error, step type iterative learning was put forward. The iteration mechanism was stopped when the accuracy converged to the allowable range so as to reduce computational complexity, store the current iterative part of the control input, and make constant value compensation. However, in simulation and experiment, it was found that after superposition of the iterative learning controller, the phenomenon of partial divergence of the system tracking error occurred. Therefore, the speed and acceleration characteristics of input trajectories in time domain and frequency domain were analyzed. High-frequency noise was introduced in frequency domain, which was found to be the cause of the abovementioned phenomenon, and high-frequency components were filtered to solve the problem. To further improve the accuracy of convergence and avoid filtering effective high-frequency information in some area, a switchable filter based on the analysis of the frequency characteristics of input trajectory was proposed. Through SIMULINK simulation and dSPACE experimental verification, it was proved that the iterative learning controller of modifying controlled quantity and filter based iterative learning control method are effective.

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

An iterative learning control algorithm within prescribed input-output subspace

The feedback control based on the model and method of iterative learning control, which in turn is based on the macroscopic fundamental diagram (MFD), mostly belongs to the classification of single-layer boundary control method. However, the feedback control method has the problem of time delay. Therefore, a feedforward feedback iterative learning control (FFILC) method based on MFD of the multi-layer boundary of single-area oversaturated intersections is proposed. The FFILC method can improve the effectiveness of boundary control and avoid the time-delay problem of feedback control. Firstly, MFD theory is used to determine the MFD of the control area; the congestion zone and the transition zone of the control area are identified; and the two-layer boundary of the control area is determined. Then, the FFILC controllers are established at the two-layer boundary of the control area. When the control area enters into a congestion state, the control ratio of traffic flow in and out of the two-layer boundary is adjusted. The cumulative number of vehicles in the control area continues to approach the optimal cumulative number of vehicles, and it maintains high traffic efficiency with high flow rates. Finally, The actual road network is taken as the experimental area, and the road network simulation platform is built. The controller of the feedforward iterative learning control (FILC) is selected as the comparative controller and used to analyse the iterative effect of FFILC. Improvements in the use of traffic signal control indicators for the control area are analysed after the implementation of the FFILC method. Results show that the FFILC method considerably reduces the number of iterations, and it can effectively improve convergence speed and the use of traffic signal evaluation indicators for the control area.

Enhancing trajectory tracking for a class of process control problems using iterative learning

In this paper, we studied the Position&Force servo control of the voice coil motor commonly used in semiconductor packaging equipment and analyzed its Position&Force servo control switching strategy. System identification is the basis for high-speed, high-precision motion control. We use the frequency domain identification method to identify the voice coil motor system. The frequency domain identification method uses sine sweep frequency and PRBS method to compare and verify. Feedback and feedforward servo control structures are used to ensure quick response and precise control of voice coil motors while ensuring system stability. In the feedforward control, we use an iterative learning control method. In semiconductor packaging equipment, the movement of the voice coil motor has always been a similar process cycle. Iterative learning control (ILC) has prominent control effects in situations where control objectives and external disturbances are periodic or repetitive. The iterative learning control algorithm is applied to the feedforward plus feedback servo control structure of the voice coil motor. From the actual effect point of view, the trajectory tracking ability of the voice coil motor during high-speed motion is significantly increased. The voice coil motor servo control algorithm described above is written on the DSP and verified on the motion control card. The experimental results show that the above servo control algorithm realizes the high speed and high precision motion control of the voice coil motor.

In iterative learning control (ILC), it is usually assumed that the disturbances, uncertainties and the desired trajectories are invariant with respect to the iteration number or iteration-independent. In practice, this may not be true. How to accommodate the iteration-dependent disturbances, uncertainties and the desired trajectories is practically important for any successful application of ILC. In practice, it is observed that the baseline performance of ILC is limited mainly by the nonrepetitiveness factors. In this paper, by the proposed two methods, it is shown that one can harness or make use of the nonrepetitiveness in ILC to reduce the baseline errors. When the pattern of the nonrepetitiveness is known, an internal model principle (IMP) in the iteration domain can be applied. When the pattern of the nonrepetitiveness is unknown in advance, a disturbance observer in the iteration domain is proposed. It is noted that to harness the nonrepetitiveness in ILC, usually, the ILC updating law has to be high-order in the iteration direction. To facilitate our discussion, a supervector notion is adopted in a fairly general setting. Simulation examples are provided to illustrate the fact that nonrepetitiveness in ILC, if properly handled, can be harnessed to achieve a better performance previously not achievable.

This work explores the problem of uniform quantization of iterative learning control (ILC) for nonlinear nonaffine systems under a data‐driven design and analysis framework. First, to deal with the strong nonlinearity and nonaffine structure of the systems, an iterative linear data model (iLDM) utilizing more additional parameter information is developed consequently bypassing modeling process. The iLDM only serves for the controller design and analysis without any mechanistic interpretation. Then, an encoding–decoding mechanism (E‐DM) is employed to deal with the bounded tracking performance caused by the uniform quantizer. Using the iLDM, an E‐DM based quantized data‐driven ILC (E‐D QDDILC) method is developed with a quantized learning control law and a quantized parameter estimation law, both of which only utilize the quantized output estimations obtained from the E‐DM. The quantized parameter estimation law enhances the robustness of the proposed E‐D QDDILC as an adaptive mechanism to tune the learning gain in real‐time. A mathematical induction approach and the contraction mapping principle are introduced for the convergence analysis as the basic tools. When the scaling function is bounded, one shows the tracking error is bounded convergent. When the scaling function approaches zero iteratively, a zero convergence can be guaranteed in the iteration domain. The main results are verified through simulation examples.

Since they were first introduced by Arimoto, iterative learning control (ILC) methods have received lots of attention because of their effectiveness and briefness. In this paper, an open-closed-loop PID-type ILC scheme with bounded learning matrixes for the control of nonlinear time-varying systems are studied. Under a few reasonable restrictions that are very common, as in other ILC research papers on the convergence problem, sufficient conditions for guaranteeing the convergence of ILC systems are given and then proved with linear operator theory. Both the system and the ILC scheme described have very general forms. It is shown that the convergence condition is weaker than some known results for a similar ILC scheme.

This work presents a two-dimensional (2-D) system theory based iterative learning control (ILC) method for linear continuous-time multivariable systems. We demonstrate that a 2-D continuous-discrete model can be successfully applied to describe both the dynamics of the control system and the behavior of the learning process. We successfully exploited the 2-D continuous-discrete Roesser's linear model by extending the ILC technique from discrete control systems to continuous control systems. Three learning rules for ILC are derived. Necessary and sufficient conditions are given for convergence of the proposed learning rules. Compared to the learning rule suggested by Arimoto et al. (1984), our developed learning rules are less restrictive and have wider applications. The third learning rule proposed ensures the reference output trajectory can be accurately tracked after only one learning trial. Three numerical examples are used to illustrate the proposed control procedures.

In this brief, a novel spatial iterative learning control (ILC) method is proposed for a class of high-order nonlinear motion systems in the presence of both parametric and nonparametric uncertainties. Since the system performs the tracking task iteratively in the spatial domain and the uncertainties are functions of spatial coordinates, the traditional ILC that updates the control signals in the time domain is not applicable. The proposed spatial ILC aims at improving the system tracking performance by making full use of the spatial periodicity. First, the dynamics of the motion systems are converted from the temporal domain to the spatial domain via feedback linearization. Then, the learning law is designed to tackle the system uncertainties. By utilizing a newly defined space-based barrier composite energy function, the boundedness and the convergence of state tracking error are ensured rigorously. In the end, an illustrative example is shown to demonstrate the efficacy of the proposed ILC scheme.

To deal with an iterative learning control (ILC) system with plant uncertainty, a set of new terms related with robust convergence is first defined. This paper proposes a sufficient condition for not only robust convergence but also robust stability of ILC for uncertain linear systems, including plant uncertainty. Thus, to find a new condition unrelated to the uncertainty, we first separate it into a known part and uncertainty one using linear fractional transformations (LFTs). Then, robust convergence and robust stability of an ILC system is determined by structured singular value (μ) of only the known part. Based on the novel condition, a learning controller and a feedback controller are developed at the same time to ensure robust convergence and robust stability of the ILC system under plant uncertainty. Lastly, the feasibility of the proposed convergence condition and design method are confirmed through computer simulation on an one-link flexible arm.

Adaptive iterative learning consensus control for second-order multi-agent systems with unknown control gains

Hydraulic press as a kind of very important press machining equipment has been widely used in national defense and civilian industry. This paper developed a new kind of hydraulic press that synthesizes the advantages of both hydraulic and SRM (Switched Reluctance Motor) driving technology. Aiming at the serious nonlinearity and time-variability existing in electro-hydraulic servo system of pump-controlled cylinder, the ILC (Iterative Learning Control) method was applied to track the shift curve. The fuzzy strategy was utilized to adjust the iterative learning gain adaptively in order to improve the convergence speed and precision of ILC. The simulation and experimental results show that the iterative learning speed is raised enormously by this method, and tracking of the slider shift curve with high response speed and control accuracy is realized.

With the development of SoC technology, the integration of FPGA+ARM has become the development direction of SoC. For the characteristics of integrated multi-axis motion control and motor drive, this paper selects Xilinx ZYNQ-7020 System-onchip (SOC) with integrated dual-core ARM CPU and FPGA as the hardware platform control chip, an arm core motor drive algorithm. Another ARM core completes the interactive function and motion control, and the FPGA to complete the multi-axis hardware current loop and improve the system bandwidth. The iterative learning control (ILC) technology is widely used in robots to reduce the trajectory error of robots performing repetitive tasks. Using P-type iterative learning ILC combined with feedback control , which can compensate for nonrepeating disturbance. Finally, analysis of experimental results shows that iterative learning control combined with feedback control can significantly improve tracking accuracy.