A series cascade control structure with modified Smith predictor is presented for the control of open-loop unstable time delay processes. Another new parallel cascade control scheme is proposed for the control of stable and unstable processes with time delay.
Dead time compensator
The Smith Predictor [9] and its many extensions, generally based on process model control schemes [6], and the finite spectrum allocation methodology [10] can be considered the first control methods for SISO linear processes that show a delay in their input or output [2,4] . Various modifications have been proposed to simplify and improve the adjustment of the controller based on the use of the Smith predictor to reject load disturbances to process with.
Cascade control structures
A parallel cascade control (as shown in Figure 1.4) is one in which both the manipulated variable and the disturbances affect the primary and secondary outputs simultaneously. Parallel cascade control is appropriate when the secondary loop has a faster dynamic response and rejection of the disturbance i.
Load frequency control
Typically, the secondary controller is first set up by setting the primary controller to manual mode. Due to the wide application of cascade control in the process industry, several researchers have focused on sequential cascade control [37–42] and parallel cascade control [43–49] .
Motivation
Most available control theories and design methods assume that the control structure is provided prior to design. However, due to the waterbed effect between the set point response and the load disturbance response, the improvement of the disturbance response is not significant.
Contributions of this Thesis
Based on the improved structure, the proposed control scheme decouples the servo response from the regulatory response in the case of nominal systems, i.e. the setpoint tracking controller and the disturbance rejection controllers can be tuned independently. A modified Smith predictor scheme is used in the primary loop to improve the closed-loop performance of the system.
Thesis Organization
Introduction
19] but with an additional feedback path from the difference of the plant output and the model output to the reference input. Several simulation examples are provided to show the superiority of the proposed design method, compared to recently reported methods.
Modified Smith predictor
Disturbance rejection in process industries is commonly much more important than setpoint tracking for many process control applications [72]. In Section 2.3, the controller design methods for six typical industrial processes are discussed, followed by Section 2.4, in which the choice of tuning parameter is given.
Controller design procedures
W(s) is selected as 1s for the step changes of the setpoint input and load disturbances in industrial and chemical practice. The ideal feedback controller equivalent to the IMC controller can be expressed in terms of the internal model, Gm, and the IMC controller, Q as.
Selection of the tuning parameter λ cd
Robustness analysis and performance
Because simple performance criteria use only the isolated characteristics of the dynamic response, time integral performance criteria are such as integral squared error (ISE=). The TV displays the sum of all control movements, both up and down.
Simulation study
From Figure 2.11 and Figure 2.12, it can be observed that especially the load disturbance response of the proposed method is excellent. A −30% error in the estimation of the process time constant was considered and the corresponding responses are shown in Figure 2.17.
Conclusions
Introduction
To overcome this problem, a predictive Smith scheme can be used in the external circuit of the cascade control system. The design and analysis of cascade control strategies for sustainable processes are addressed by many researchers.
Proposed cascade control scheme
Gp2(= ˜Gp2e−θ2s) are the outer and the inner loop processes and ˜Gp1 and ˜Gp2 are the delay-free parts and θ1 and θ2 are the time delays of Gp1 and Gp2, respectively. Gm1 = ˜Gm1e−θm1s is the primary process model and Gm2 = ˜Gm2e−θm2s is the secondary process model in which ˜Gm1 and ˜Gm2 are the delay-free parts.
Controller design procedures
The nominal complementary sensitivity function of the inner loop for disturbance rejection can be obtained as. From (3.12), the nominal regulatory response transfer function of the outer loop is given by Hd1(s) = y1.
Find the parameters of the primary loop disturbance rejection controller (Gcd1) from (3.39) after choosing the tuning parameter λ1 in the range 0.5θm−1.5θm. Then determine the setpoint tracking controller (Gcs) using (3.42) after choosing the tuning parameter λcs in the range 0.1θm−θm.
Internal stability analysis
Robustness analysis and performance
It is observed that all the roots of the Kharitonov polynomials (Table 3.1) have negative real part, i.e. it is observed that the closed-loop characteristic equation of the system is stable in the complex plane in Mikhailov's sense.
Simulation study
The nominal closed-loop responses for all of the above controller settings are shown in Figure 3.6, and the corresponding control signals are given in Figure 3.7. It can be seen from Figure 3.8 that the proposed cascaded control structure gives improved responses in both setpoint and load disturbance rejection.
Conclusions
Introduction
The selection of tuning parameters is addressed in Section 4.4 followed by stability analysis in Section 4.5 and simulation results in Section 4.6.
Series cascade control structure
Controller design procedures
Depending on the nature of the integration process, the desired complementary closed-loop sensitivity function is selected and the controller is designed accordingly. In order to avoid this phenomenon, the Pad´e approximation with different degrees is taken into account (Rn−1,n(s), the degree of the numerator is one less than the degree of the denominator).
Selection of the tuning parameters
Robustness analysis and performance
It is observed that all the roots of the Kharitonov polynomials (see Table 4.2) have negative real part. It is observed that the closed-loop characteristic equation of the system is stable in complex.
Simulation results
The closed-loop responses for these controller settings are shown in Figure 4.3, and the corresponding control signals are given in Figure 4.4. In the present work, a +30% perturbation in the time delay and gain of the primary process was considered, and the corresponding responses are shown in Figures 4.5 and 4.6.
Conclusions
Introduction
Later, Shen and Yu [44] proposed a method for selecting secondary measurements for parallel cascade control. The proposed scheme combines the features of both the parallel cascade scheme and the modified Smith predictor structures.
Problem formulation
The closed-loop transfer function of the primary loop for the servo response is given by. 1 +Gm) (1 +Gp1Gcd1+Gp2Gcd2+Gp1Gcd1Gm2Gcd2) (5.1) Similarly, the closed-loop transfer function of the primary loop for the regulatory responses is given by. it has two degrees of freedom and the loops can be tuned independently.
Controller design procedures
The slope of the Nyquist curve of the secondary loop transfer function L2(jω) at the frequency ωgc is equal to the phase of the derivative of L2(jω) at the frequency ωgc. Since the primary and secondary loops are interconnected, the selection of the gain crossover frequency for the individual loop can be made by minimizing the performance indices.
Stability analysis
Robustness analysis and performances
The complementary sensitivity function of the closed-loop system consists of Gcd1 whose parameters Kc1,Ti1 and Td1 are functions of the tuning parametersωgcand Ψ1. It is observed that all the roots of the Kharitonov polynomials (Table 5.5) have negative real part ie.
Simulation study
The responses of the closed-loop system for these controller settings are shown in Figure 5.10 and Figure 5.11 for the actual process. Magnitude plots for modeling error and uncertainty (uncertainty +30% ink1, θ1 and −30% inτ1) are considered in further robust stability analysis and are shown in Figure 5.15.
Conclusions
Introduction
A new parallel cascade control structure
It can also be seen from (6.5) and (6.6) that the new structure decouples the servo response from the regulating response for the nominal system. In practice, in parallel cascade control, the inner loop process has no or negligible time delay, while the outer loop process has a large time delay compared to the inner loop.
Controller design procedures
Based on the nature of the primary process and the load disturbance transfer functions, the desired complementary closed-loop sensitivity function is selected and the controller is designed accordingly. It should be noted that V(s) primarily helps improve the overall servo tracking performance of the closed-loop system.
Simulation results
With these controller settings, the closed-loop system performances are evaluated by introducing a unit-scale load perturbation at time t = 0. To investigate the robustness of the proposed controller, a +30% perturbation in the primary process and the load Disturbance time delays are considered and the closed-loop performances are given in Figure 6.5.
Conclusions
Introduction
In the proposed method, a Laurent series was used to derive the expression for the controller's parameters. The modeling of power system dynamics, controller design, simulation results and robustness analysis for single area power system is given in Section 7.3.
Proposed control structure
Single-area power system
Now we will investigate the closed-loop stability property of the proposed controller design, where the factory parameters are slightly mismatched. It is noted that all roots of the Kharitonov polynomials (see Table 7.2 and 7.3) have a negative real part, i.e.
Multi-area power system
Since the origin is excluded from the Kharitonov rectangles (Figure 7.8 and Figure 7.9), it is concluded that the closed-loop control system is quite stable. The balance between connected control areas is achieved by detecting frequency and link power deviations to generate the control area error (ACE) signal, which in turn is used in the control strategy as shown in Figure 7.10.
Conclusions
Concluding remarks
A parallel cascade control on the improved modified Smith predictor structure is proposed for the control of steady, unsteady and integrative processes with time delay. A new parallel cascade control structure is presented for the control of stable and unstable processes with time delay.
Suggestions for further work
It will be interesting if the modified Smith predictor for periodic disturbance rejection can be proposed. The proposed series and parallel cascade control structures can be extended to non-minimum phase stable and unstable systems.
Detailed explanation for the statement ‘ x 1 always gives a neg- ative value’of 3.3.1
From figure f-1 it is clear that the graph starts from the point indicating that α2/θ2 >8.2 and therefore x1 is always negative, regardless of the value of the normalized time delay.
Detailed explanation for the statement ‘ m 1 always gives a neg- ative value’of 3.3.2
Use of orthogonality property of H 2 norm in (2.18)
Detailed derivation for (3.44) of the section 3.5
