Feedback controller design references

The tuning of the controller in the feedback loop can be theoretically performed independent of the feedforward loop (i.e., the feedforward loop does not introduce instability in the closed-loop response). For more information on feedforward/feedback control appications and design of such controllers, refer to the general references. 

Centralized control can be also designed based on disturbance rejection or robustness requirements. In this case, the controller is not a static linear feedback law, as (45), but a dynamic feedback controller is obtained. Additionally, two degree of freedom controllers allow for a better control behavior in tracking and regulation. All these alternatives are beyond the scope of this introductory local control design treatment and are the subject of specialized references (see, for instance, [19]). 

R.D. Bartusiak, C. Georgakis, and M.J. Reilly. Nonlinear feedforward/feedback control structures designed by reference synthesis. Chemical Engineering Science, 44 1837-1851, 1989. 

For an extensive discussion on the various types of performance criteria, and their advantages and shortcomings in designing feedback controllers, the reader can consult the following reference ... 

From the design equations (21.9) and (21.10) it is clear that a feedforward controller cannot be a conventional feedback controller (P, PI, PID). Instead, it should be viewed as a special-purpose computing machine. This is the reason it is sometimes referred to as a feedforward computer. 

On articulated vehicle modeling and SMC application, many researchers have revealed approaches. More analytically, in Ridley and Corke (2003), Lee and Yoo (2009), Nayl et al. (2012), kinematics model of articulated vehicle and error model between real and reference path are presented, and the path tracking simulation with model predictive control is applied, while in Petrov and Chakyrski (2009) the feedback controller based on Lyapunov approach is designed. The simulations in these literatures are non-real-time. The real-time feature is not verified. Moreover, in Korayem et al. (2012), the spatial cable robot path planning is presented, while in Aslam et al. (2014) the fuzzy SMC is used for path tracking of the four-wheel skid steer vehicle. Both the literatures have designed the controller of SMC. However, the models of the plants are distinct from the articulated vehicle. 

If all the state variables are not measured, an observer should be implemented. In the Figure 14, the jacket temperature is assumed as not measured, but it can be easily estimated by the rest of inputs and outputs and based on the separation principle, the observer and the control can be calculated independently. In this structure, the observer block will provide the missing output, the integrators block will integrate the concentration and temperature errors and, these three variables, together with the directly measured, will input the state feedback (static) control law, K. Details about the design of these blocks can be found in the cited references. 

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