FPGA hardware accelerated controller for nonlinear predictive control and its accelerated realization method

Abstract

The invention, which belongs to the field of the FPGA realization technology, relates to a non-linear-model-predictive-control-based FPGA hardware acceleration controller and an acceleration realization method. The invention aims at extending application of non-linear model predictive control (NMPC) in rapid dynamic system; and a non-linear-model-predictive-control-based FPGA hardware acceleration controller and an acceleration realization method in non-linear planning can be realized by using a PSO algorithm. For a hardware acceleration controller, a WMR prediction control model is established; according to a control requirement of target model WMR trajectory tracking, an optimization problem is solved; and an NMPC-PSO algorithm flow is executed. According to the invention, on the basis of a one-to-one correspondence relationship between codes and bottom circuits, the parallel computing structure of the FPGA and the parallel computing characteristics of the PSO algorithm can be combined well, so that the rapid computing capability of the NMPC is improved, the real-time requirement of the controller can be met well, and NMPC application in the actual rapid dynamic system can be extended. Meanwhile, on-line flexible reducing, extending, and upgrading of the scheme can be realized; and thus the controller and the realization method can adapt to the current situation of the fast product updating speed; and the controller can be verified rapidly.

Description

technical field [0001] The invention belongs to the technical field of FPGA realization. Background technique [0002] Model predictive control (Model Predictive Control, MPC), also known as rolling time domain control, is a widely discussed feedback control strategy. Because model predictive control has a feedforward-feedback structure, it can deal with multi-variable, multi-input, multi-output high-dimensional systems, and can explicitly and actively deal with time-domain hard constraints in the sense of optimization, and has been widely used in many fields. Attention and discussion, especially the successful application in complex industrial processes in the past 30 years, has fully demonstrated the great potential of predictive control to deal with complex constraint optimization control problems. In recent years, with the rapid development of the economy and society, many application areas have put forward higher requirements for constrained optimization problems, and ...

AI Technical Summary

Problems solved by technology

However, in these new neighborhoods, many systems have strong nonlinear factors. The control performance of traditional linear predictive controllers cannot meet the actual requirements in many cases. Therefore, it is necessary to design corresponding nonlinear predictive controllers for different systems. Predictive control (Nonlinear Model Predictive Control, NMPC) has good control performance for large, complex and fast dynamic systems that are constrained by nonlinearity and a wide range of operating points

[0003] After retrieving the relevant information of the existing technology, it is found that most control systems currently use linear model predictive control. However, NMPC needs to repeatedly solve nonlinear programming problems online within a limited sampling time, which has a large computational burden and poor real-time performance. At present, it is mainly used in slow dynamic systems such as petrochemical, metallurgical and pharmaceutical industries; on the other hand, with the increase in the complexity of control systems and the development of mechatronics, the requirements for high real-time performance, high reliability, and miniaturization of electronic control units are increasing. Realizing high-performance controllers on low-cost chips is a major challenge for electronic control systems; on the other hand, modern products are updated very quickly, and circuits may need to be changed in a short period to meet new functional requirements. For the design circuit system of general-purpose digital integrated circuits, it means redesigning and rewiring, which cannot meet the actual needs in the design cycle. Therefore, it is necessary to develop an electronic control system that is easy to upgrade, maintain, and expand to reduce product costs. Development cost, shorten development cycle, extend life cycle

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