Abstract
This paper tackles the control problem of nonlinear disturbed polynomial systems using the formalism of output feedback linearization and a subsequent sliding mode control design. This aims to ensure the asymptotic stability of an unstable equilibrium point. The class of systems under investigation has an equivalent Byrnes–Isidori normal form, which reveals stable zero dynamics. For the case of modeling uncertainties and/or process dynamic disturbances, conventional feedback linearizing control strategies may fail to be efficient. To design a robust control strategy, meta-heuristic techniques are synthesized with feedback linearization and sliding mode control. The resulting control design guarantees the decoupling of the system output from disturbances and achieves the desired output trajectory tracking with asymptotically stable dynamic behavior. The effectiveness and efficiency of the designed technique were assessed based on a benchmark model of a continuous stirred tank reactor (CSTR) through numerical simulation analysis.
Highlights
Stabilization and tracking are two important issues in the field of nonlinear control with the former being more intricate than the latter
There is a short description of the primary factors comprising the I/O feedback linearization (FBL) technique in order to touch upon the fundamental topics
The results clearly demonstrate that all investigated control schemes provide an acceptable achievement with respect to the hard constrained functional variations and fast-changing dynamics of the process temperature
Summary
Stabilization and tracking are two important issues in the field of nonlinear control with the former being more intricate than the latter. Its goal is to ensure the global asymptotical stability of an uncertain system operating on feedback It is characterized by output monitoring and disturbance decoupling for a set of nonlinear control plants having parameters and model uncertainties. The feedback linearization technique (FBL) has satisfactory performance, provided that disturbances, modeling errors and parameter uncertainties are ignored. This is acceptable in an ideal scenario. The computed values of the state variables, which influence the SMC through the reaching law, remain incorrect In this respect and in order to attain the optimal controller gains associated with the above problems, one must reduce the long gap of the sliding surface, which is a delicate control task.
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