This paper concerns an improved model-free adaptive fractional-order control with a high-order pseudo-partial derivative for uncertain discrete-time nonlinear systems. Firstly, a new equivalent model is obtained by employing the Grünwald–Letnikov (G-L) fractional-order difference of the input in a compact-form dynamic linearization. Then, the pseudo-partial derivative (PPD) is derived using a high-order estimation algorithm, which provides more PPD information than the previous time. A discrete-time model-free adaptive fractional-order controller is proposed, which utilizes more past input–output data information. The ultimate uniform boundedness of the tracking errors are demonstrated through formal analysis. Finally, the simulation results demonstrate the effectiveness of the proposed method.

Fractional-Order Model-Free Adaptive Control with High Order Estimation

Cite This Article Zhao, S., Luan, X., Liu, J., Tan, R. (2023). Introduction to the Special Issue on Advances on Modeling and State Estimation for Industrial Processes. CMES-Computer Modeling in Engineering & Sciences, 135(1), 1–3. 

Introduction to the Special Issue on Advances on Modeling and State Estimation for Industrial Processes

Phase noise is a critical parameter that affects the performance of communication receivers, especially in high-speed and high-frequency systems. This paper presents a novel approach to measure phase noise, which aims to improve the performance of receivers. Conventional methods often suffer from limitations, such as the need for a high-quality reference, sensitivity to environmental conditions, and inaccuracies in signal degradation due to the dynamic range of a logarithmic detector. Our proposed method utilizes the delay line method and advanced signal processing techniques to eliminate these problems. By integrating calibration algorithms and eliminating the need for a reference signal, we achieve more accurate and reliable phase noise measurements. Thus makes the method attractive for the characterization of modern oscillators, amplifiers with low phase noise and receivers with high-order modulation schemes whose phase noise performance is better than that of standard references. With the increased sensitivity of the dual-channel cross-correlation technique these refined measurements can be used to optimize receiver designs, leading to significant improvements in signal detection, demodulation and overal system efficiency. Experimental results confirm the effectiveness of the proposed photonic delay phase noise meter approach This research contributes to the advancement of phase noise characterization techniques and we believe it will provide practical insights to improve the robustness and reliability of communication systems in real-world environments. 

Phase Noise Measurement for Improving the Performance of High-Order Modulation Scheme Wireless Links

In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative (PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the maximum tracking error is not limited by the initial value of PPD. The convergence of the maximum tracking error is deduced in detail. This method can track the desired output with enhanced convergence and improved tracking performance. Two examples are used to verify the convergence and effectiveness of the proposed method. 

Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

. Phase noise (PN) consists of common phase error (CPE) and inter carrier interference (ICI). In an OFDM symbol, CPE has the same impact on each subcarrier, which is easy to be suppressed. However, ICI destroys the orthog-onality of subcarriers, which is diﬃcult to be eliminated. Therefore, an additional method is needed to be performed in the OFDM receiver to compensate the ICI. The interpolation method is considered an eﬀective way to eliminate the ICI caused by PN in the OFDM system. To enhance the accuracy of the PN estimation and compensation, we propose a linear method, LI-ICI-EE1 method based on LI-ICI-E1. Multiple interpolation slopes are ﬁrst calculated by selecting multiple pairs of observation samples, then the slope with the maximal linear ﬁtting degree based on the least square (LS) criterion is selected to improve the LI precision. Fur-thermore, to improve the estimation accuracy of PN in the LI-ICI-EE1, we propose a Shrinkage-based on LI-ICI-E1 method named SLI-EE1, which is implemented by adding an 𝑙 2 norm penalty term to the error function. At last, to optimize the low accuracy of LI-ICI-EE1 and SLI-EE1 when the PN compensation problem is a high-order problem, we propose a non-linear method Shrinkage-based Third-order Lagrange method named STL. Simulation results show that the improved methods have better BER performance.

/pdf/improved-phase-noise-compensation-in-ofdm-systems-1r480iah.pdf

Improved Phase Noise Compensation in OFDM Systems

The high proportion of renewable distributed power in the distribution network has a series of impacts on the safety and economy of the power grid. Based on the output characteristics and cost characteristics of distributed energy, this paper proposes the evaluation model of the aggregated output range of VPP and the quotation model of VPP. An optimization method is proposed to encourage distributed energy to participate in voltage regulation auxiliary services. In this paper, a multi-objective optimization method is presented, which takes minimizing system voltage deviation, network loss and auxiliary service cost as objectives. The Pareto solution set is solved by the improved particle swarm optimization algorithm. Taking an area with a high proportion of distributed energy as an example, the effectiveness of the model and algorithm is verified, the relationship between optimization objectives is discussed, and flexible optimization options are provided. 

Source-grid collaborated multi-objective optimization based on virtual power plant

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Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

Improved Phase Noise Compensation in OFDM Systems

Source-grid collaborated multi-objective optimization based on virtual power plant