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Advanced Control for Quadruple Tank Process

In the realm of control systems, the last three decades have witnessed significant advancements in model predictive control (MPC), an advanced technique renowned for its ability to optimize processes with constraints, handle multivariate systems, and incorporate future references when feasible. This paper introduces an innovative offset-free MPC approach tailored for the control of a complex nonlinear system—the quadruple tank process (QTP). The QTP, known for its deceptively simple yet challenging multivariate behavior, serves as an ideal benchmark for evaluating the efficacy of the proposed algorithm. In this work, we rigorously compare the performance of the PID and MPC controller when applied to both linear and nonlinear models of the QTP. Notably, our research sheds light on the advantages of MPC, particularly when confronted with constant disturbances. Our novel algorithm demonstrates exceptional capabilities, ensuring error-free tracking even in the presence of persistent load disturbances for both linear and nonlinear QTP models. Compared to the PID control, the proposed method can reduce the overall set point tracking error up to 32.1%, 27.6%, and 38.54% using the performance indices ISE, ITAE, and IAE, respectively, for the linear case. Furthermore, for the nonlinear case, the overall set point tracking error reduction is up to 93.4%, 94.9%, and 91.5%. This work contributes to bridging the gap in effective control strategies for nonlinear systems like the QTP, highlighting the potential of offset-free MPC to enhance control and stability in a challenging process industry involving automatic liquid level control.In the realm of control systems, the last three decades have witnessed significant advancements in model predictive control (MPC), an advanced technique renowned for its ability to optimize processes with constraints, handle multivariate systems, and incorporate future references when feasible. This paper introduces an innovative offset-free MPC approach tailored for the control of a complex nonlinear system—the quadruple tank process (QTP). The QTP, known for its deceptively simple yet challenging multivariate behavior, serves as an ideal benchmark for evaluating the efficacy of the proposed algorithm. In this work, we rigorously compare the performance of the PID and MPC controller when applied to both linear and nonlinear models of the QTP. Notably, our research sheds light on the advantages of MPC, particularly when confronted with constant disturbances. Our novel algorithm demonstrates exceptional capabilities, ensuring error-free tracking even in the presence of persistent load disturbances for both linear and nonlinear QTP models. Compared to the PID control, the proposed method can reduce the overall set point tracking error up to , , and  using the performance indices ISE, ITAE, and IAE, respectively, for the linear case. Furthermore, for the nonlinear case, the overall set point tracking error reduction is up to , , and . This work contributes to bridging the gap in effective control strategies for nonlinear systems like the QTP, highlighting the potential of offset-free MPC to enhance control and stability in a challenging process industry involving automatic liquid level control

Advanced Control for Quadruple Tank Process

In the realm of control systems, the last three decades have witnessed significant advancements in Model Predictive Control MPC), an advanced technique renowned for its ability to optimize processes with constraints, handle multivariate systems, and incorporate future references when feasible. This paper introduces an innovative offset-free MPC approach tailored for the control of a complex nonlinear system—the Quadruple Tank Process (QTP). The QTP, known for its deceptively simple yet challenging multivariate behavior, serves as an ideal benchmark for evaluating the efficacy of the proposed algorithm. In this work, we rigorously compare the performance of the PID and MPC controller when applied to both linear and nonlinear models of the QTP. Notably, our research sheds light on the advantages of MPC, particularly when confronted with constant disturbances. Our novel algorithm demonstrates exceptional capabilities, ensuring error-free tracking even in the presence of persistent load disturbances for both linear and nonlinear QTP models. Compared to the PID control, the proposed method can reduce the overall set point tracking error up to 32.1%, 27.6%, and 38.54% using the performance indices ISE, ITAE, and IAE, respectively, for the linear case. Furthermore, for the nonlinear case, the overall set point tracking error reduction is up to 93.4%, 94.9%, and 91.5%. This work contributes to bridging the gap in effective control strategies for nonlinear systems like the QTP, highlighting the potential of offset-free MPC to enhance control and stability in a challenging process industry involving automatic liquid level control