Multiple-degree-of-freedom controlled variable design-based output tracking robustness forecast control method

Abstract

The present invention discloses a multiple-degree-of-freedom controlled variable design-based output tracking robustness forecast control method. The method aims at a first-class smooth nonlinear system and adopts an RBF-ARX modeling method. In this way, a system mathematical model containing the bounded uncertain interference is identified. The structural characteristics of the model are fully utilized, and a plurality of variable linear polyhedron models capable of wrapping the non-linear dynamic system are constructed. Considering the state feedback control rate of a future input increment during the design of single-degree-of-freedom controlled variables in an infinite time-domain objective function, a possibly caused conservative property is given. The invention provides a multiple-degree-of-freedom controlled variable design-based and RBF-ARX model-based output tracking robustness forecast control method. Compared with the prior art, the unknown bounded interference of the system is taken into consideration. The information of system steady-state equilibrium points is not required for the design of a controller, while the degree-of-freedom of optimal control increments is effectively increased. Therefore, the method is better in robustness and higher in practical value.

Description

technical field [0001] The invention belongs to the field of automatic control, and relates to an output tracking robust predictive control method based on multi-degree-of-freedom control quantity design. Background technique [0002] Model predictive control is a new type of computer control algorithm that appeared in the field of industrial process control in the 1970s. As a typical representative of advanced process control, because of the adaptability of its control mechanism to complex industrial processes, it has become the most popular advanced control algorithm in the field of industrial process control and has achieved a large number of successful applications. In practical applications, predictive control algorithms based on linear models were first applied to the control of complex systems. However, strictly speaking, almost all practical control systems are nonlinear, and predictive control based on linear models cannot solve the nonlinear problems that exist in...

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Problems solved by technology

However, the external disturbance of the system is not considered in the controller design of this method, and in practical applications, the control system will inevitably be affected by various complex external uncertain disturbances, so this method is only suitable for weak external disturbances Or in the control of the CSTR system that does not require high control robustness, it has obvious limitations

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