Quantization is a significant technique in network control to save limited bandwidth. In this work, two new multi-lagged-input-based quantized iterative learning control (MLI-QILC) methods are proposed by using output...
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Quantization is a significant technique in network control to save limited bandwidth. In this work, two new multi-lagged-input-based quantized iterative learning control (MLI-QILC) methods are proposed by using output quantization and error quantization, respectively. The multi-lagged-input iterative dynamic linearization method (MLI-IDL) is introduced to build a linear data model of nonlinear systems using additional control inputs in lagged time instants and multiple parameters where the condition of nonzero input change is not required any longer. The MLI-QILC is proposed by designing two new objective functions utilizing the quantized data of the system outputs and tracking errors, respectively. With rigorous analysis, it is shown that the proposed MLI-QILC with output quantization guarantees that the tracking error converges to a bound which is related to the quantization density and the bound of the desired trajectory. Furthermore, an asymptotic convergence can be achieved for the proposed MLI-QILC method with error quantization. The theoretical results are verified by simulations.
This paper considers the quantized iterative learning control for differential inclusion systems with channel fading. The study aims to achieve desired control objectives in the unreliable networks with limited bandwi...
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This paper considers the quantized iterative learning control for differential inclusion systems with channel fading. The study aims to achieve desired control objectives in the unreliable networks with limited bandwidth and fading channel. For differential inclusion systems, the Steiner-type selection theorem in set-valued analysis is applied to transform the set-valued mapping into a single-valued mapping. Then, a uniform quantizer with a scaling sequence is introduced, and the quantized iterative learning control is studied in the case of known fading statistics and unknown fading statistics, respectively. We choose the appropriate scaling sequence to ensure the bounded quantization value, and use techniques such as Cauchy inequality, Gronwall inequality, and Jensen inequality to establish convergence results for the tracking error. For the case of unknown fading statistics, we design a novel iterativelearningcontrol scheme according to the continuous update step size with the aid of the test signal in each iteration. The results show that the control scheme can achieve stable convergence rate of tracking error in fading channel. Finally, two numerical examples are used to verify the theoretical results.
This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding-decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the ...
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This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding-decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learningcontrol scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.
In this work, the problems of predictive compensation, unknown nonlinearity, and nonaffine structure are considered simultaneously for a quantized iterative learning control (QILC) design and analysis under a data-dri...
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In this work, the problems of predictive compensation, unknown nonlinearity, and nonaffine structure are considered simultaneously for a quantized iterative learning control (QILC) design and analysis under a data-driven framework. The compensation strategy can avoid deteriorated data transmission quality owing to limited channel capacities. First, a dynamic linearization methodology is employed to transform the nonlinear plant into a virtual iterative linear data model (iLDM) which includes all the input signals over a time-window from the initial time instant to the current one. The iLDM is also used as a predictive model to estimate the unavailable information caused by the encoding-decoding mechanism. Then, a predictive compensation-based QILC is proposed by optimizing quadratic functions, which includes an output prediction mechanism, a quantizediterativelearning updating law, a quantizediterative parameter estimation law, and a resetting algorithm. The result is also extended to a class of MIMO nonlinear nonaffine discrete-time systems. The developed control laws are data-driven and independent of any system information. The theoretical results are proved by the use of contraction mapping principle and induction method. Examples are provided to verify the effectiveness of the proposed methods.
This paper addresses the optimization problem of quantized iterative learning control (ILC) for networked control systems (NCSs) with limited bandwidth. For linear time-invariant systems with quantized input signals, ...
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ISBN:
(纸本)9798350321050
This paper addresses the optimization problem of quantized iterative learning control (ILC) for networked control systems (NCSs) with limited bandwidth. For linear time-invariant systems with quantized input signals, a mathematical cost function is constructed to obtain a gradient-based ILC law that rests with the system model, and the learning gain is updated in the trial domain. By combining the infinite logarithmic quantizer with the encoding and decoding mechanism to encode and decode the signals, the quantization accuracy is enhanced and the system tracking capability is improved. Compared with the traditional gradient descent method with fixed learning gain, the gradient-based ILC law can obtain faster error convergence. Simulation based on industrial robot system is given to substantiate the suggested method.
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