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A practical parameter tuning algorithm for super-twisting algorithm-based differentiator and its application in altitude ground test facility

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第3期47卷 610-620页

作者： Wang, Juan Zhang, Hehong Dan, Zhihong Zhang, Song Xiao, Gaoxi Zhai, Chao Fuzhou Univ Coll Math & Stat Fuzhou Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Aero Engine Corp China Sichuan Gas Turbine Estab Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore China Univ Geosci Coll Automat Wuhan Peoples R China

The super-twisting algorithm (STA)-based robust exact differentiator (RED) proposed by Levant has been widely applied in control, observation, and fault detection. With properly set parameters, STA-RED is able to estimate exactly and robustly the derivatives of an input. With a given input signal, however, a practical parameter tuning rule still lacks. To this end, a general parameter tuning algorithm with the full stability proof for STA-RED is proposed. In particular, the time linear transformation and the coordinate transformation are performed on a given base signal, allowing the deterministic relationship between the parameters of STA-RED and the amplitude and the frequency of the input to be obtained. The full stability of the STA-RED based on the proposed parameter tunning algorithm (denoted as PSTA-RED) with perturbations is given using the Lyapunov function method. The simulations are carried out using given input signals and the pressure signals from the altitude ground test facility to illustrate the effectiveness of the proposed PSTA-RED.

关键词： Differentiator parameter tuning algorithm stability altitude ground test facility filtering differentiation

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TSVMPath: Fast Regularization parameter tuning algorithm for Twin Support Vector Machine

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NEURAL PROCESSING LETTERS 2022年第6期54卷 5457-5482页

作者： Zhou, Kanglei Zhang, Qiyang Li, Juntao Beihang Univ Sch Comp Sci & Engn 37 Xueyuan Rd Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching 10 Xitucheng Rd Beijing 100876 Peoples R China Henan Normal Univ Coll Math & Informat Sci 46 Jianshe East Rd Xinxiang 453007 Henan Peoples R China

Twin support vector machine (TSVM) has attracted much attention in the field of machine learning with good generalization ability and computational performance. However, the conventional grid search method is very time-consuming to obtain the optimal regularization parameter. To address this problem, we develop a novel fast regularization parameter tuning algorithm for TSVM, named TSVMPath. After transforming the models of two sub-optimization problems, we divide the two classes of samples into different sets. Lagrangian multipliers are then proved to be piecewise linear concerning the corresponding regularization parameters, greatly extending the search space of the solution. By proving that the Lagrangian multipliers of two sub-optimization models are 1 when the regularization parameters approach infinity, we design a simple yet effective initialization. As a result, the entirely regularized solution path can be obtained without solving quadratic programming problems. Four types of events are finally defined to update the solution path. Experiments on 8 UCI datasets show that the prediction accuracy of TSVMPath is superior to the best competing methods, with up to four orders of magnitude speed-up for the computational overhead compared with the grid search method.

关键词： Statistical machine learning Twin support vector machine parameter tuning algorithm Regularized solution path

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A tune sliding control algorithm for angle following of electric motor steer-by-wire system

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ASIAN JOURNAL OF CONTROL 2025年第2期27卷 962-972页

作者： He, Lin Xu, Ziang Wei, Yujiang Wang, Mingwei Huang, Chunrong Shi, Qin Hefei Univ Technol Lab Automot Intelligence & Electrificat Hefei 230009 Peoples R China HeFei Univ Technol Sch Automot & Transportat Engn Hefei Peoples R China

The sliding mode control has to design a sliding manifold for manipulating the system motion in engineering practice, making system asymptotic stability paramount. This is particularly challenging for using variable sliding manifold parameters to formulate the sliding manifold for fast convergence and precise control. While much of the research on sliding mode control has focused on constant sliding manifold parameters, comparatively little is known about the variable approach of the sliding manifold parameters. Therefore, sliding manifold parameters are treated as variables and are computed by a parameter tuning algorithm. Regarding the parameter tuning algorithm, its input is the sliding mode control law with variable sliding manifold parameters, and its output is the computed sliding manifold parameters that will be transmitted back to the sliding mode control law. Through tuning the sliding manifold parameters by an optimal method of lowest cost with the measuring value and model computing value of system states based on the historical information, the difference between the nominal model and the real system will be removed. Here we discuss a series of studies on the algorithm of tune sliding control that, collectively, develop an application of how the tune sliding controller steers the front wheels of the full self-driving vehicle. The designed approach has been tested in a steering test vehicle to realize a good angle tracking performance of the electric motor steer-by-wire system.

关键词： parameter tuning algorithm sliding mode control steer-by-wire variable sliding manifold parameter

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Nonlinear Process Control Using a Direct Adaptive PI Controller

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2012年第4期45卷 298-304页

作者： Chen, Chyi-Tsong Peng, Shih-Tien Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan

This paper pertains to the direct adaptive Proportional-Integral (PI) control of nonlinear chemical processes. A simple yet effective PI controller parameter tuning algorithm, which has different structures separated by a defined zero-level set of process output errors, is derived. This algorithm enables a PI controller to control chemical processes in an interactive and autonomous way by simply observing the process output errors. To show the stability of the resultant PI control system, a rigorous analysis involving the use of a Lyapunov approach is presented. The effectiveness and applicability of the proposed direct adaptive PI control schemes are demonstrated by considering the control of a nonlinear continuous stirred tank reactor (CSTR) in the presence of a plant/model mismatch, an unmeasured disturbance, and parameter variations. Comparisons with several conventional PI controllers and an indirect adaptive Proportional-Integral-Derivative (PID) controller are performed for performance evaluation. Simulation results indicate that the proposed PI control scheme in associated with the derived parameter tuning algorithm is promising and that it offers flexibility and excellent capability for the direct adaptive control of nonlinear chemical processes.

关键词： Direct Adaptive Control PI Controller Variable Structure parameter tuning algorithm Lyapunov Approach

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Multivariable process control using decentralized single neural controllers

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 1998年第1期31卷 14-20页

作者： Chen, CT Yen, JH Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan

This paper develops a learning-type multi-loop control system for interacting multi-input/multi-output industrial process systems, The recently developed single neural controller (SNC) is adopted as the decentralized controller, With a simple parameter tuning algorithm, the SNC in each loop is able to learn to control a changing process by merely observing the process output error in the same loop, To circumvent loop interactions, static decouplers are incorporated in the presented scheme, The only a priori knowledge of the controlled plant is the process steady state gains, which can be easily obtained from open-loop test, Extensive comparisons with decentralized PI controllers were performed, Simulation results show that the presented decentralized nonlinear control strategy appears to be a simple and promising approach to interacting multivariable process control.

关键词： multivariable process control decentralized control learning control single neural controller parameter tuning algorithm static decoupling

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Direct adaptive process control based on using a single neuron controller: Survey and some new results

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2006年第2期29卷 289-299页

作者： Chen, CT Hwang, CY Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan I Shou Univ Dept Chem Engn Kaohsiung 840 Taiwan

This paper surveys the process control techniques for using an adaptive single neuron controller (SNC). In addition to an introduction to a derived parameter tuning algorithm, this paper focuses on how the SNC can be implemented in combination with existing, strategies to deal with processes in the presence of diversified dynamics. Following this lead, some alternative SNC control schemes are presented and the related convergence analyses are provided. Besides, some new results on the development of model-based SNC control systems are established, which enables the SNC to start in a systematic way by the assignment of a pre-specified performance index. To demonstrate the effectiveness and applicability of the proposed SNC control schemes, examples are provided. Extensive simulation results show that the proposed model-based SNC control system is a simple, easy-to-implement, and promising approach to the direct adaptive control of chemical processes.

关键词： single neuron controller parameter tuning algorithm direct adaptive control model-based control

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Intelligent process control using a fuzzy single neuron controller

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2007年第4期40卷 333-340页

作者： Chen, Chyi-Tsong Wu, Ching-Kuo Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan

In this paper, an intelligent control scheme using a fuzzy single neuron controller (FSNC) is proposed for nonlinear process control. Different from the conventional three-parameter single neuron controller (SNC), the FSNC uses the fuzzy logic concept to build its nonlinearity rather than just mapping through a nonlinear saturation function. This effort, though it introduces additional tuning parameters, can greatly enhance the nonlinear capability for process control. A simple yet efficient parameter tuning algorithm has been developed, which enables the FSNC to learn to control the nonlinear process adaptively with merely observing the process output errors. Both the convergence of the proposed parameter tuning algorithm and the stability of the presented FSNC-based control system are guaranteed by utilizing the Lyapunov stability theorem. To demonstrate the applicability and effectiveness of the proposed intelligent control scheme, we apply it to the direct adaptive control of an unstable nonlinear CSTR. Comparisons with an adaptive SNC and PID controllers were performed. Extensive simulation results reveal that the proposed FSNC is promising and is well suited to the direct adaptive control of processes having severe nonlinearity.

关键词： fuzzy single neuron controller parameter tuning algorithm intelligent process control Lyapunov stability

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Optimal design of interval type 2 fuzzy controllers based on a simple tuning algorithm

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APPLIED SOFT COMPUTING 2014年 23卷 270-285页

作者： Cortes-Rios, J. C. Gomez-Ramirez, E. Ortiz-de-la-Vega, H. A. Castillo, O. Melin, P. La Salle Univ Intelligent Syst Grp Mexico City DF Mexico Tijuana Inst Technol Div Grad Studies Tijuana Mexico

Evolutionary algorithms are one of the most common choices reported in the literature for the tuning of fuzzy logic controllers based on either type-1 or type-2 fuzzy systems. An alternative to evolutionary algorithms is the simple tuning algorithm (STA-FLC), which is a methodology designed to improve the response of type-1 fuzzy logic controllers in a practical, intuitive and simple ways. This paper presents an extension of the simple tuning algorithm for fuzzy logic controllers based on the theory of type-2 fuzzy systems by using a parallel model implementation, it also includes a mechanism to calculate the feedback gain, new integral criteria parameters, and the effect of the AND/OR operator combinations on the fuzzy rules to improve the algorithm applicability and performance. All these improvements are demonstrated with experiments applied to different types of plants. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Type-2 fuzzy logic Fuzzy controllers parameter tuning algorithm

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A simple adaptive control strategy for temperature trajectory tracking in batch processes

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 1998年第6期76卷 1118-1127页

作者： Chen, CT Peng, ST Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan

In this paper, a simple adaptive control strategy is suggested for temperature tracking control of batch processes. A nonlinear controller, which is in structure very simple and consists of a single parameter, is proposed. To enable this controller to control a batch process adaptively, a simple parameter tuning algorithm is derived based on the Lyapunov stability theorem. The proposed adaptive control scheme is directly operational, which does not depend on process model and the only a priori process information required is the system response direction. To demonstrate the effectiveness and applicability of the proposed scheme, illustrative examples are provided. Extensive simulation results reveal that the proposed adaptive control strategy appears to be a simple and effective approach to batch process control, which provides robust control despite the wide range of operating conditions and nonlinear dynamics of the system.

关键词： adaptive control parameter tuning algorithm trajectory tracking control batch reactor

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Optimal design and control of CPU heat sink processes

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IEEE TRANSACTIONS ON COMPONENTS AND PACKAGING TECHNOLOGIES 2008年第1期31卷 184-195页

作者： Chen, Chyi-Tsong Wu, Ching-Kuo Hwang, Chyi Feng Chia Univ Dept Chem Engn Taichung 407 Taiwan Solar Appl Mat Technol Corp Tainan 709 Taiwan I Shou Univ Dept Chem Engn Kaohsiung 804 Taiwan

This paper considers the optimal plate fin design and control for central processing unit (CPU) heat sink processes. First, we apply a finite element method to investigate the heat transfer phenomena of a heat sink process. To have a better heat dispersion performance, a real-coded genetic algorithm is then utilized to search for an optimal set of plate-fin shape parameters. The objective function to be minimized is the entropy generation rate which can take simultaneously the two major factors, heat transfer rate and air resistance, into consideration in the design. The present optimization scheme is able to achieve a better design for heat dispersion than existing methods. To attenuate environmental and time-varying disturbances, a direct adaptive control scheme is then developed for the CPU heat sink process. It is based on using a bounded single neuron controller (SNC) along with a parameter tuning algorithm to regulate the temperature of a selected control point. Extensive comparisons of the SNC-based control performance with the on-off control as well as a PI controller show that the proposed scheme provides excellent control performance despite the existence of unexpected process uncertainties.

关键词： heat sink process optimal design and control parameter tuning algorithm partial differential equation system real-coded genetic algorithm single neuron controller (SNC)

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