Explicit model predictive control method based on connected graph for three-degree-of-freedom helicopter

Abstract

An explicit model predictive control method based on a connected graph for a three-degree-of-freedom helicopter comprises the following steps: step 1) modeling the three-degree-of-freedom helicopter to a model predictive control problem (MPC), and transforming the model predictive control problem (MPC) into a multi-parameter programming (MP-QP) problem, that is, a problem to be solved in offline calculation; step 2) introducing a critical domain, an effective constraint set and a concept of the connected graph; step 3) initializing a solving algorithm of the connected graph to obtain an initial critical domain and an optimal effective set; 4) judging feasibility of an effective candidate set; step 5) calculating on the feasible effective candidate set to obtain a critical domain and a control law; step 6) using the connected graph to generate a new candidate set and repeating the steps 4) - 6) until all solutions are generated; step 7) conducting the explicit model predictive control based on the connected graph for the three-degree-of-freedom helicopter system. The explicit model predictive control method based on connected graph proposed by the invention improves rate of off-linecalculation while ensuring good control performance of the system.

Description

technical field [0001] The invention relates to an explicit model prediction control method based on a connected graph, which is applied in the field of aircraft optimization control and provides an optimization control method for flight controllers. Background technique [0002] As one of the most distinctive creations of aviation technology in the 20th century, the helicopter has greatly expanded the application range of aircraft. Its outstanding feature is that it can do low-altitude (a few meters above the ground), low-speed (starting from hovering) and maneuvering flight with the same nose direction, especially vertical take-off and landing in a small area. Because of these characteristics, it has broad application and development prospects. In terms of military use, it has been widely used in ground attack, airborne landing, weapon delivery, logistics support, etc. In civil use, it is used in short-distance transportation, medical rescue, disaster relief, emergency r...

AI Technical Summary

Problems solved by technology

Traditional explicit model predictive control is usually based on geometry when solving multi-parameter programming (MP-QP) problems, that is, the state area of ​​the system is convexly divided, and the optimization problem corresponding to each state partition is established. The explicit functional relationship between the optimal control law and the state (for the linear control law of the state), the finally obtained parameter solution is the control law corresponding to each of the above state critical domains; but this method also has its limitations. On the one hand, it It is necessary to consider whether the adjacent surface properties are satisfied between the critical domains. On the other hand, it is necessary to set the step size reasonably, and at the same time, it is necessary to discriminate all constraint sets one by one, so the computational efficiency of solving the problem is not high.

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