Multi - parameter linear programming is utilized to obtain the explicit model predictive control law and the online computation associated with optimization can be removed completely . thus better real - time applicability is obtained in the presented predictive control profile 我們用多參線性規(guī)劃的方法得到預測控制律與系統(tǒng)當前狀態(tài)變量的顯式映射關(guān)系,使得預測控制無需再進行復雜的在線優(yōu)化計算,從而提升了預測控控制的實時性。
Nonlinear model based predictive control ( nmpc ) not only is a valuable approach for solving practical control problems , but also is the frontier of nonlinear control theory . the perceptible successes of mpc strategies can be attributed to several factors including its inherent ability to handle input and output constraints , time delay and incorporation of an explicit model of the plant into the optimization problem . this dissertation discusses two kinds of nonlinearity ( or nonlinear system ) 本文沿著理論研究與工程實際相結(jié)合的設計思路,較為系統(tǒng)和全面的研究了非線性模型預測控制理論,提出改進新算法;探討了非線性模型預測控制理論在自主水下航行器控制系統(tǒng)設計中的應用,豐富和發(fā)展了模型預測控制理論,本論文的主要工作及意義有以下幾個方面: 1 )從工程應用的角度研究有限域無終端約束廣義預測控制穩(wěn)定性充分條件,為有約束廣義預測控制穩(wěn)定性研究奠定了基礎。
The equations derived are more complicated if more precise model is employed for high accuracy . the technique , which does n ' t need to have an explicit model - calibration based on neural networks implicit vision model , is more effective . since bp neural network can implement any nonlinear relationship from input to output and need n ' t to model , and the classical stereo vision approach based on explicit model are very complicated , an algorithm of stereo vision based on bp neural networks implicit vision model is proposed 利用神經(jīng)網(wǎng)絡可以充分逼近任意的非線性關(guān)系且無須精確建模的特點,針對傳統(tǒng)的立體視覺方法過程繁瑣,對安裝精度要求高的不足,本文提出了一種基于bp神經(jīng)網(wǎng)絡隱式視覺模型的立體視覺方法,該算法實施起來比較簡便;針對已有的像差修正算法計算過程復雜的不足,提出了一種基于bp神經(jīng)網(wǎng)絡的修正成像誤差的算法;針對具有共面特征的點的三維重構(gòu)的應用,提出了一種基于徑向基函數(shù)網(wǎng)絡的二維平面測量算法。