Study on adaptive control of nonlinear dynamical systems based on quansi-ARX models

制度:新 ; 報告番号:甲3441号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576

Convex Identifcation of Stable Dynamical Systems

This thesis concerns the scalable application of convex optimization to data-driven modeling of dynamical systems, termed system identi cation in the control community. Two problems commonly arising in system identi cation are model instability (e.g. unreliability of long-term, open-loop predictions), and nonconvexity of quality-of- t criteria, such as simulation error (a.k.a. output error). To address these problems, this thesis presents convex parametrizations of stable dynamical systems, convex quality-of- t criteria, and e cient algorithms to optimize the latter over the former. In particular, this thesis makes extensive use of Lagrangian relaxation, a technique for generating convex approximations to nonconvex optimization problems. Recently, Lagrangian relaxation has been used to approximate simulation error and guarantee nonlinear model stability via semide nite programming (SDP), however, the resulting SDPs have large dimension, limiting their practical utility. The rst contribution of this thesis is a custom interior point algorithm that exploits structure in the problem to signi cantly reduce computational complexity. The new algorithm enables empirical comparisons to established methods including Nonlinear ARX, in which superior generalization to new data is demonstrated. Equipped with this algorithmic machinery, the second contribution of this thesis is the incorporation of model stability constraints into the maximum likelihood framework. Speci - cally, Lagrangian relaxation is combined with the expectation maximization (EM) algorithm to derive tight bounds on the likelihood function, that can be optimized over a convex parametrization of all stable linear dynamical systems. Two di erent formulations are presented, one of which gives higher delity bounds when disturbances (a.k.a. process noise) dominate measurement noise, and vice versa. Finally, identi cation of positive systems is considered. Such systems enjoy substantially simpler stability and performance analysis compared to the general linear time-invariant iv Abstract (LTI) case, and appear frequently in applications where physical constraints imply nonnegativity of the quantities of interest. Lagrangian relaxation is used to derive new convex parametrizations of stable positive systems and quality-of- t criteria, and substantial improvements in accuracy of the identi ed models, compared to existing approaches based on weighted equation error, are demonstrated. Furthermore, the convex parametrizations of stable systems based on linear Lyapunov functions are shown to be amenable to distributed optimization, which is useful for identi cation of large-scale networked dynamical systems

Sistem Bongkar-Muat Muatan Truk Menggunakan Lengan Robot Berbasis Arduino-PC Dengan Metode Back Propagation Neural Network

Bongkar-muat merupakan sebuah proses pemindahan suatu barang dari kendaraan pengangkut menuju gudang maupun sebaliknya. Padasektor pergudangan terutama, dalam proses bongkar-muat masih dilakukan dengan cara konvensional. Hal tersebut menjadi kurang efisien dalam penggunaan tenaga kerja karena barang yang dipindahkan jumlahnya tidak sedikit terlebih jika barang tersebut berat. Dari permasalahan diatas untuk mengurangi beban kerja maka dibuatlah sebuah sistem bongkar-muat menggunakan lengan robot untuk proses pengambilan dan peletakan barang. Lengan robot yang terdiri dari base joint, shoulder joint, elbow joint, wrist joint dan vacuum gripper ini menerapkan metode back propagation neural network untuk menentukan sudut pergerakan motor servo pada tiap sendi terhadap koordinat pixel x, y yang dideteksi oleh kamera dengan penentuan lokasi kardus melalui proses klik operator ada hasil capture kamera pada proses bongkar. Hasil pelatihan back propagation pada lengan robot saat proses bongkar muatan mampu melakukan prediksi sudut pergerakan lengan robot dengan baik. Dengan 15 kali percobaan pergerakan lengan robot didapatkan error rata-rata sebesar1.588% pada koordinat x dan 1.982% pada koordinat y. Sehingga lengan robot mampu menuju kardus dan memindahkannya ke atas konveyor dengan baik. Pada penelitian ini dengan muatan sebanyak 24 kardus memiliki tingkat keberhasilan sebesar 100% pada proses bongkar dan 83.33% pada proses muat. Kata Kunci— bongkar-muat, lengan robot; motor servo;back propagation neural network

A Quasi-ARX Model for Multivariable Decoupling Control of Nonlinear MIMO System

This paper proposes a multiinput and multioutput (MIMO) quasi-autoregressive eXogenous (ARX) model and a multivariable-decoupling proportional integral differential (PID) controller for MIMO nonlinear systems based on the proposed model. The proposed MIMO quasi-ARX model improves the performance of ordinary quasi-ARX model. The proposed controller consists of a traditional PID controller with a decoupling compensator and a feed-forward compensator for the nonlinear dynamics based on the MIMO quasi-ARX model. Then an adaptive control algorithm is presented using the MIMO quasi-ARX radial basis function network (RBFN) prediction model and some stability analysis of control system is shown. Simulation results show the effectiveness of the proposed control method

Data-based PID controller designs for nonlinear systems

Master'sMASTER OF ENGINEERIN

Precision Control of a Sensorless Brushless Direct Current Motor System

Sensorless control strategies were first suggested well over a decade ago with the aim of reducing the size, weight and unit cost of electrically actuated servo systems. The resulting algorithms have been successfully applied to the induction and synchronous motor families in applications where control of armature speeds above approximately one hundred revolutions per minute is desired. However, sensorless position control remains problematic. This thesis provides an in depth investigation into sensorless motor control strategies for high precision motion control applications. Specifically, methods of achieving control of position and very low speed thresholds are investigated. The developed grey box identification techniques are shown to perform better than their traditional white or black box counterparts. Further, fuzzy model based sliding mode control is implemented and results demonstrate its improved robustness to certain classes of disturbance. Attempts to reject uncertainty within the developed models using the sliding mode are discussed. Novel controllers, which enhance the performance of the sliding mode are presented. Finally, algorithms that achieve control without a primary feedback sensor are successfully demonstrated. Sensorless position control is achieved with resolutions equivalent to those of existing stepper motor technology. The successful control of armature speeds below sixty revolutions per minute is achieved and problems typically associated with motor starting are circumvented.Research Instruments Ltd