Scholarlink

<p>Hexadic tank system represents an extension of quadruple tank system for controlling non-growth-associated product dynamics in bioprocess industries, including two stage continuous fermentations, multiple distillation columns, pharmaceutical, and food processing applications. This study presents a comprehensive analysis encompassing theoretical foundations, simulation frameworks, hardware implementation, and experimental validation of three control algorithms: LQR, Linear MPC, and Robust MPC, evaluated under disturbance and non-disturbance conditions. Among the three control algorithms, Linear MPC with disturbances (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\hbox {LMPC}_{\text {D}}\)</EquationSource> </InlineEquation>) achieves superior performance with the lowest mean error (1.67), maximum error (2.00), control variance (3.47), and overall sensitivity (2.52), with high settling times. <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\hbox {RMPC}_{\text {D}}\)</EquationSource> </InlineEquation> shows the fastest minimum response (1.93&#xa0;s) but exhibits higher mean error (2.5) and maximum error (5.0), and overall sensitivity (3.94). LQR controllers exhibit poor performance, with high sensitivity (94.08–226.47), large errors, and longer settling times (especially for <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\hbox {LQR}_{\text {D}}\)</EquationSource> </InlineEquation>), rendering them unsuitable for practical implementation. All controllers maintain zero steady-state error with stable eigenvalues (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(-6.76\times 10^{-3}\)</EquationSource> </InlineEquation> to <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(-4.34\times 10^{-19}\)</EquationSource> </InlineEquation>). This confirms that the model predictive control strategies are optimal for tracking precision, disturbance rejection, and parameter insensitivity in bioprocess applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design and analysis of a hexadic tank system: classical and advanced control algorithms

  • 期刊论文

  • 发表日期：

    2026年6月24日

查看全文
  • Sagnik Mitra,
  • Ganti Suryanarayana Murthy

摘要

Hexadic tank system represents an extension of quadruple tank system for controlling non-growth-associated product dynamics in bioprocess industries, including two stage continuous fermentations, multiple distillation columns, pharmaceutical, and food processing applications. This study presents a comprehensive analysis encompassing theoretical foundations, simulation frameworks, hardware implementation, and experimental validation of three control algorithms: LQR, Linear MPC, and Robust MPC, evaluated under disturbance and non-disturbance conditions. Among the three control algorithms, Linear MPC with disturbances ( \(\hbox {LMPC}_{\text {D}}\) ) achieves superior performance with the lowest mean error (1.67), maximum error (2.00), control variance (3.47), and overall sensitivity (2.52), with high settling times. \(\hbox {RMPC}_{\text {D}}\) shows the fastest minimum response (1.93 s) but exhibits higher mean error (2.5) and maximum error (5.0), and overall sensitivity (3.94). LQR controllers exhibit poor performance, with high sensitivity (94.08–226.47), large errors, and longer settling times (especially for \(\hbox {LQR}_{\text {D}}\) ), rendering them unsuitable for practical implementation. All controllers maintain zero steady-state error with stable eigenvalues ( \(-6.76\times 10^{-3}\) to \(-4.34\times 10^{-19}\) ). This confirms that the model predictive control strategies are optimal for tracking precision, disturbance rejection, and parameter insensitivity in bioprocess applications.