Complexity
Volume 2018 (2018), Article ID 2763210, 6 pages
https://doi.org/10.1155/2018/2763210

Research Article

Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

Yun-Shan Wei¹ and Qing-Yuan Xu²

¹School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China
²School of Electrical and Computer Engineering, Nanfang College of Sun Yat-sen University, Guangzhou 510970, China

Correspondence should be addressed to Qing-Yuan Xu; pc31xqy@126.com

Received 6 August 2018; Accepted 18 October 2018; Published 1 November 2018

Guest Editor: Junpei Zhong

Copyright © 2018 Yun-Shan Wei and Qing-Yuan Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

1. Introduction

In practice, it is hard to obtain the precise mathematical model of robot system [1–4], rapid thermal processing [5], flexible system [6–8], etc. With simple recursive mode and model-free characteristic, by using the tracking error and control input of the previous iterations, iterative learning control (ILC) is widely used to the dynamical systems with repetitive operations in a fixed time interval [9–11].

Hitherto, in order to track the desired trajectory, most existing ILC works require that the trail length is fixed and uniform at each iteration [12–14]. Nevertheless, in many engineering applications of ILC, the requirement of fixed and uniform trail length might not be fulfilled. The trail length of system output, state, and control input would randomly vary from iteration to iteration due to the complex factors or randomly occurring events. Recently, there are some studies investigating the ILC issues with iteratively variable trail lengths [15–22]. By introducing the maximum pass length and adopting the lifted-system framework, Seel et al. [15] proposed the necessary and sufficient conditions of monotonic convergence for linear discrete-time single-input single-output (SISO) systems with varying trail lengths. For linear discrete-time multiple-input multiple-output (MIMO) systems, iteration-average operator and stochastic variable satisfying Bernoulli distribution are involved in ILC design to cope with the varying trail lengths issue [16]. It was proved that the system tracking errors can be driven to zero in mathematical expectation sense. Based on the Bernoulli stochastic variable, three varying trail lengths-based ILC schemes were proposed for linear discrete-time systems with vector relative degree [17]. In [18], an iteratively moving average operator, which contains the few most recent cycles, and Bernoulli stochastic variable are introduced into ILC scheme for nonlinear continuous-time SISO systems with iteratively varying trail lengths. Other iteratively moving average operator based ILC algorithms for dynamical systems with iteratively varying pass lengths could also be found in [19–21]. Recently, in [22], the tracking of ILC for discrete-time systems with varying trail lengths can be guaranteed in deterministic convergence way by a modified P-type ILC scheme. Among existing varying trail lengths-based ILC studies, it is commonly assumed that the trail lengths of state, control input, and output are identical at a specific iteration. Clearly, more efforts should be made in the varying pass lengths-based ILC algorithms for dynamical systems.

As far as the issue of varying trail lengths is concerned, in most ILC algorithms, it is commonly assumed that the lengths of state, control input, and output are identical at a specific iteration. However, in many practical applications, it is hoped that the controlled system could achieve the control objective with less control efforts. For example, in the speed control of vehicle or robot, when the speed is controlled in a neighborhood of the desired one, the system can operate freely without any control efforts. It implied that the control inputs in a terminal time interval can be removed at one repetitive operation process. This can be represented by a repetitive system with randomly variable control input lengths. Motivated by the above observation, this paper investigates the convergence of varying input lengths-based ILC for linear discrete-time MIMO systems. Based on the assumption that the initial states randomly fluctuate around the desired initial states with zero mean, by applying the proposed P-type ILC law, the ILC tracking errors can be driven to zero in mathematical expectation at the desirable trail length as the iteration number increases. The requirements on identical initial state and trail lengths in the conventional ILC schemes are mitigated in this paper.

The rest of this paper is organized as follows. The ILC issue with randomly variable input trail length is formulated in Section 2. Section 3 presents the P-type ILC law with convergence analysis. An illustrative example is given in Section 4. Section 5 concludes this paper.

2. Problem Formulation

Consider the linear discrete-time MIMO system with varying input length, which can be represented as the following two subsystems:

and

For systems (1) and (2), and denote the iteration index and time instant, respectively. Meanwhile, , , and represent the state, control input, and output, respectively. , , and . is the actual trail length of the control input at the -th iteration. It is assumed that is unknown and random variable with iteration, but its lower bound is given. The desired output of the linear discrete-time MIMO system represented by (1) and (2) is , , where is the desired state. Assume that, for any realizable output trajectory , there exists a unique control input such that

The ILC tracking error of the linear discrete-time MIMO system is thus defined as ,  .

Assumption 1. The iterative initial state is randomly variable, but its expectation satisfies

Remark 2. Different from the general ILC requirement on identical initial condition that the iterative initial state , Assumption 1 is relaxed, where can be randomly variable with a certain expectation .
In this paper, for system (1), the objective of ILC under iteratively varying input lengths and Assumption 1 is to look for an input sequence , such that the ILC tracking can be improved.

3. ILC Method and Its Convergence

In order to well address the ILC issue of the linear discrete-time MIMO system (1) and (2) with the iteration-varying input trail lengths, let us denote , () to be a stochastic variable satisfying Bernoulli distribution and taking binary values 0 and 1. And denotes the event that the control input of system (1) can continue to the time instant at the -th iteration, which occurs with a probability function of , (). denotes the event that the control input of system (1) cannot continue to the time instant at the -th iteration which occurs with a probability function of .

Obviously, the expectation of is .

Define a modified control input as

From the definition of Bernoulli stochastic variable , (5) can be rewritten as

From (5) and (6), systems (1) and (2) are presented as the following concise modified system for

In the following, for the modified system (7) under Assumption 1, a P-type ILC law is used for convergence analysis.

For , choose a P-type ILC law as follows:

where is the control gain. The control input sequence at is computed for each iteration by (8), which is a popular form in varying trail length based ILC literatures [18, 22].

Theorem 3. Suppose that the dynamic system with varying input length, which is represented by system (1) and system (2), satisfies Assumption 1, and the desired trajectory is realizable. Using the P-type ILC law (8), if the control gain is chosen to make

where

then for .

Proof. Let , , and be the control input error, modified input error, and the state error, respectively. Then, it is obtained from (3) and (7)

where . Subtracting both sides of (8) with , there is

According to (11) and (12), it yields

Then applying the mathematical expectation operator on both sides of (13), we have

where according to (6) and .
The solution of for is

For , the solution of is

According to (5), (15) and (16) are combined as for

Taking the mathematical expectation operator on both sides of (17) and considering (4) of Assumption 1, it is obtained that

Substituting (18) into (14), it yields from

As , it follows from (14) and (4) of Assumption 1

As , from (19) we have

Denote and as and . And let ; we can combine (20) and (21) as follows:

where is defined as in (10). Taking norm on both sides of (22), and then applying the condition (9), we obtain

Since , it yields from (23)

which implies for

On the other hand, from (11), (18), and , for , there is

Since (25) holds, taking limitation to (26), we can prove that for .

Remark 4. Regarding the selection of the control gain in the ILC law (8), the convergent condition (9) with (10) in Theorem 3 provides us a theoretical guideline. It is noticed that the convergent conditions (9) is an inequality, and usually, ILC design does not require the accurate knowledge about the controlled systems. Therefore, based on the estimated values of , , , and , we can decide the control gain to satisfy inequality (9).

4. Illustrative Example

Consider the following discrete-time linear system with randomly variable input trail length, which can be represented as

and

where the state and output trail length and the randomly control input length . The desired output is described by

Without loss of generality, we set the initial imposing time length in system (27); the actual input of the initial iteration , . The corresponding modified control input of the initial iteration , . For systems (27) and (28), the accuracy of iteratively varying input lengths-based ILC is evaluated by the sum of absolute error in mathematical expectation

The proposed P-type ILC law (8) is applied. According to the convergent conditions (9) and (10) provided in Theorem 3, the control gain for the ILC law is chosen as . The iterative initial states are randomly generated as shown in Figure 1, which satisfy . Figure 2 presents the performance index at different iterations. Figure 3 presents the actual tracking situation of the outputs in system (27) and system (28) to the desired trajectory (29) at iterations and with the P-type ILC law (8). The control inputs with randomly trail length at iterations and are depicted in Figure 4.

Figure 1: The variation profiles of at different iterations.

Figure 2: The performance index at different iterations by using the proposed P-type ILC law (8).

Figure 3: The output trajectories of the ILC system (27) and (28) for at iterations and .

Figure 4: For , the control input trajectories of the ILC system (27) at iterations and .

Figure 1 indicates that the initial condition in Assumption 1 is quite different from the identical initial condition of the general ILC schemes. It implies that Assumption 1 is very relaxed, where can randomly fluctuate around with zero mean. From Figure 2, it is illustrated that the performance index can be certainly driven to zero as the iteration number goes to infinity by the proposed P-type ILC law (8). Although the actual input length of system (27) is randomly variable with iteration number as shown in Figure 4 and the initial states fluctuate randomly satisfying as shown in Figure 1, a progressively improved tracking performance of the outputs in system (27) and system (28) to the desired trajectory has been accomplished for as shown in Figure 3.

5. Conclusions

For linear discrete-time systems with randomly variable input trail length, where the initial states fluctuate around the desired initial states with zero mean, this paper presents a modified P-type ILC law. Modified control input at the desirable trail length is introduced in ILC designs in order to tackle the randomly variable input trail length. The designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in mathematical expectation. The requirements on identical trail lengths at a specific iteration and identical initial condition in previous varying trail length based ILC schemes are mitigated. Future research will extend the varying input trail length based ILC results of this paper to the multiagent systems [23, 24] and other robot systems [25].

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work is partly supported by Initial Scientific Research Fund of Guangzhou University (69-18ZX10140), Science and Technology Planning Project of Guangzhou Municipal Government (201605030014), and Youth Innovation Project of Important Program for College of Guangdong Province (2017KQNCX260).

References

S. Zhang, Y. Dong, Y. Ouyang, Z. Yin, and K. Peng, “Adaptive neural control for robotic manipulators with output constraints and uncertainties,” IEEE Transactions on Neural Networks Learning Systems, vol. 29, no. 11, pp. 5554–5564, 2018.

C. Yang, H. Wu, Z. Li, W. He, N. Wang, and C.-Y. Su, “Mind control of a robotic arm with visual fusion technology,” IEEE Transactions on Industrial Informatics, vol. 14, no. 9, pp. 3822–3830, 2018.

W. He, Y. Dong, and C. Sun, “Adaptive neural impedance control of a robotic manipulator with input saturation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 3, pp. 334–344, 2016.

C. Yang, J. Luo, Y. Pan, Z. Liu, and C.-Y. Su, “Personalized variable gain control with tremor attenuation for robot teleoperation,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 10, pp. 1759–1770, 2018.

T. Xiao and H.-X. Li, “Sliding mode control design for a rapid thermal processing system,” Chemical Engineering Science, vol. 143, pp. 76–85, 2016.

Z. Zhao, Z. Liu, Z. Li, N. Wang, and J. Yang, “Control design for a vibrating flexible marine riser system,” Journal of The Franklin Institute, vol. 354, no. 18, pp. 8117–8133, 2017.

X. He and Z. Zhao, “Boundary control design for a vibrating flexible string system with input nonlinearities,” Nonlinear Dynamics, vol. 93, no. 2, pp. 323–333, 2018.

Z. Zhao, J. Shi, X. Lan, X. Wang, and J. Yang, “Adaptive neural network control of a flexible string system with non-symmetric dead-zone and output constraint,” Neurocomputing, vol. 283, pp. 1–8, 2018.

D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control: a learning-based method for high-performance tracking control,” IEEE Control Systems Magazine, vol. 26, no. 3, pp. 96–114, 2006.

T. Xiao and H.-X. Li, “Eigenspectrum-based iterative learning control for a class of distributed parameter system,” IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 824–836, 2017.

Y.-S. Wei and X.-D. Li, “Iterative learning control for linear discrete-time systems with high relative degree under initial state vibration,” IET Control Theory & Applications, vol. 10, no. 10, pp. 1115–1126, 2016.

Q.-Y. Xu and X.-D. Li, “Adaptive fuzzy {ILC} of nonlinear discrete-time systems with unknown dead zones and control directions,” International Journal of Systems Science, vol. 49, no. 9, pp. 1878–1894, 2018.

S.-T. Sun, X.-D. Li, and R.-X. Zhong, “An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control,” International Journal of Systems Science, vol. 48, no. 13, pp. 2752–2763, 2017.

D. Meng and K. L. Moore, “Robust iterative learning control for nonrepetitive uncertain systems,” IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 907–913, 2017.

T. Seel, T. Schauer, and J. Raisch, “Monotonic convergence of iterative learning control systems with variable pass length,” International Journal of Control, vol. 90, no. 3, pp. 409–422, 2017.

X. Li, J.-X. Xu, and D. Huang, “An iterative learning control approach for linear systems with randomly varying trial lengths,” IEEE Transactions on Automatic Control, vol. 59, no. 7, pp. 1954–1960, 2014.

Y.-S. Wei and X.-D. Li, “Varying trail lengths-based iterative learning control for linear discrete-time systems with vector relative degree,” International Journal of Systems Science, vol. 48, no. 10, pp. 2146–2156, 2017.

X. Li, J.-X. Xu, and D. Huang, “Iterative learning control for nonlinear dynamic systems with randomly varying trial lengths,” International Journal of Adaptive Control & Signal Processing, vol. 29, pp. 1341–1353, 2015.

S. Liu and J. Wang, “Fractional order iterative learning control with randomly varying trial lengths,” Journal of The Franklin Institute, vol. 354, no. 2, pp. 967–992, 2017.

S. Liu, A. Debbouche, and J. Wang, “On the iterative learning control for stochastic impulsive differential equations with randomly varying trial lengths,” Journal of Computational and Applied Mathematics, vol. 312, no. 1, pp. 47–57, 2017.

X. Li and D. Shen, “Two novel iterative learning control schemes for systems with randomly varying trial lengths,” Systems & Control Letters, vol. 107, pp. 9–16, 2017.

D. Meng and J. Zhang, “Deterministic convergence for learning control systems over iteration-dependent tracking intervals,” IEEE Transactions on Neural Networks Learning Systems, vol. 29, no. 8, pp. 3885–3892, 2018.

Q. Jia and W. K. S. Tang, “Event-triggered protocal for the consensus of multi-agent systems with state-dependent nonlinear coupling,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 2, pp. 723–732, 2018.

Q. Jia and W. K. S. Tang, “Consensus of multi-agents with event-based nonlinear coupling over time-varying digraphs,” IEEE Transactions on Circuits Systems II: Express Briefs, 2018.

C. Yang, K. Huang, H. Cheng, Y. Li, and C.-Y. Su, “Haptic identification by ELM-controlled uncertain manipulator,” IEEE Transactions on Systems Man Cybernetics Systems, vol. 47, no. 8, pp. 2398–2409, 2017.