多智能体协调控制--一致性区间方法

This book consists of nine chapters. Chapter 1 introduces the background of cooperative control and reviews graph theory and some technical tools. Chapters 2 and 3 are concerned with the consensus control problem for continuous-time and discrete-time linear multi-agent systems, respectively, and present several fixed-gain consensus protocols. Chapter 4 studies the H∞ and H2 consensus problems for linear multi-agent systems subject to external disturbances. Chapter 5 continues Chapter 2 to investigate the consensus protocol design problem for continuous-time linear multi-agent systems and presents several fully distributed adaptive consensus protocols. Chapter 6 considers the distributed tracking control problem for linear multi-agent systems having a leader with nonzero control input. Chapter 7 studies the distributed containment control problem for the case with multiple leaders. Chapter 8 is concerned with the robust cooperative control problem for multi-agent systems with linear nominal agent dynamics subject to heterogeneous matching uncertainties. The global consensus problem for Lipschitz nonlinear multi-agent systems is finally discussed in Chapter 9.

Nonlinear Model Predictive Control_ Theory and Algorithms（2nd）

Nonlinear Model Predictive Control_ Theory and Algorithms：second edition

Cooperative Control of Multi Agent Systems

多智能体协调控制Cooperative Control of Multi Agent Systems：optimal and adaptive design approaches

Tomlab product sheet

ü Tlsqr: Large-scale sparse linear least squares. ü glcCluster: Mixed-integer nonlinear global optimization. ü glcDirect: Modified C implementation of the DIRECT method. ü PDCO: Primal-dual barrier method for convex objectives, handles linear constraints. ü slsSolve: Sparse least squares with nonlinear constraints.

USER'S GUIDE FOR TOMLAB /SOL

Welcome to the TOMLAB /SOL User's Guide. TOMLAB /SOL includes a wide range of solver and interfaces between The MathWorks' MATLAB and all solvers developed by Stanford Systems Optimization Laboratory. The solver package includes binaries for the following solvers: MINOS - For large-scale sparse general nonlinear programming problems. LP-MINOS - For large-scale sparse linear programming problems. QP-MINOS - For large-scale sparse quadratic programming problems. LPOPT - For dense linear programming problems. QPOPT - For dense convex quadratic programming problems. LSSOL - For dense linear and convex quadratic programs, and constrained linear least squares problems. NLSSOL - For nonlinear least squares with linear and nonlinear constraints. NPSOL - For dense linear, quadratic and nonlinear programming. SNOPT - For large-scale, sparse, linear and nonlinear programming. SQOPT - For sparse linear and quadratic programming. Please visit http://tomopt.com/tomlab/products/sol/ for more information. The interface between TOMLAB /SOL, Matlab and TOMLAB consists of two layers. The rst layer gives direct access from Matlab to SOL, via calling a Matlab function that calls a pre-compiled MEX le (DLL under Windows, shared library in UNIX) that denes and solves the problem in SOL. The second layer is a Matlab function that takes the input in the TOMLAB format, and calls the rst layer function. On return the function creates the output in the TOMLAB format.

User Guide for Mad - a Matlab Automatic Differentiation Toolbox TOMLAB /MAD

Mad is a Matlab library of functions and utilities for the automatic differentiation of Matlab functions/ statements via operator and function overloading. Currently the forward mode of automatic differentiation is supported via the fmad class. For a single directional derivative objects of the fmad class use Matlab arrays of the same size for a variable’s value and its directional derivative. Multiple directional derivatives are stored in objects of the derivvec class allowing for an internal 2-D, matrix storage so allowing the use of sparse matrix storage for derivatives and ensuring efficient linear combination of derivative vectors via high-level Matlab functions. This user guide covers: • installation of Mad on UNIX and PC platforms, • using TOMLAB /MAD, • basic use of the forward mode for differentiating expressions and functions • advanced use of the forward mode including: – dynamic propagation of sparse derivatives, – sparse derivatives via compression, – differentiating implicitly defined functions, – control of dependencies, • use of high-level interfaces for solving ODEs and optimization problems outside of the TOMLAB framework, • differentiating black-box functions for which derivatives are known.

USER’S GUIDE FOR TOMLAB /LGO

Welcome to the TOMLAB LGO User’s Guide TOMLAB LGO includes the LGO solver from Pint′er Consulting Services and an interface to MATLAB by MathWorks’ The Lipschitz Continuous Global Optimizer LGO solver suite serves for the analysis and global solution of general nonlinear programming NLP models The LGO solver system has been developed and gradually extended for more than a decade and it now incorporates a suite of robust and efficient global and local nonlinear solvers It can also handle smaller LP models LGO is documented elsewhere in detail: see for example Pint′er 1996 2001 2003 [1 2 3] TOMLAB LGO integrates the following global scope algorithms: Branch and bound adaptive partition and sampling based global search BB Adaptive global random search GARS Adaptive multistart global random search MS LGO also includes the following local solver strategies: Constrained local search based on a generalized reduced gradient approach GRG The overall solution approach followed by TOMLAB LGO is based on the seamless combination of the global and local search strategies This allows for a broad range of operations In particular a solver suite approach supports the flexible usage of the component solvers: one can execute fully automatic global and or local search based optimization and can design customized interactive runs TOMLAB LGO does not rely on any sub solvers and it does not require any in depth structural information about the model It is particularly suited to solve even ’black box’ closed confidential or other complex models in which the available analytical information may be limited TOMLAB LGO needs only computable function values without a need for higher order analytical information TOMLAB LGO can even solve models having constraints involving continuous but non differentiable functions Thus within TOMLAB LGO is well suited to solve nonlinear global and convex optimization models TOMLAB LGO can also be used in conjunction with other TOMLAB solvers Local solvers when available can be used to verify the solution found by LGO and to provide additional local information ">Welcome to the TOMLAB LGO User’s Guide TOMLAB LGO includes the LGO solver from Pint′er Consulting Services and an interface to MATLAB by MathWorks’ The Lipschitz Continuous Global Optimizer LGO solver suite serves for the analysis and global solution of general nonlinear programming NLP [更多]

USER'S GUIDE FOR TOMLAB /SNOPT

Welcome to the TOMLAB /SNOPT User's Guide. TOMLAB /SNOPT includes a set of solvers and MATLAB embedded interfaces. The solver package includes binaries for the following solvers: MINOS - For large-scale sparse general nonlinear programming problems. LP-MINOS - For large-scale sparse linear programming problems. QP-MINOS - For large-scale sparse quadratic programming problems. LPOPT - For dense linear programming problems. QPOPT - For dense convex quadratic programming problems. SNOPT - For large-scale, sparse, linear and nonlinear programming. SQOPT - For sparse linear and quadratic programming. Please visit http://tomopt.com/tomlab/products/sol/ for more information. The interface between TOMLAB /SNOPT, Matlab and TOMLAB consists of two layers. The rst layer gives direct access from Matlab to SNOPT, via calling a Matlab function that calls a pre-compiled MEX le (DLL under Windows, shared library in UNIX) that denes and solves the problem in SNOPT. The second layer is a Matlab function that takes the input in the TOMLAB format, and calls the rst layer function. On return the function creates the output in the TOMLAB format.

USER'S GUIDE FOR TOMLAB /NPSOL

Welcome to the TOMLAB /NPSOL User's Guide. TOMLAB /NPSOL includes a set of solvers and MATLAB embedded interfaces. The solver package includes binaries for the following solvers: MINOS - For large-scale sparse general nonlinear programming problems. LP-MINOS - For large-scale sparse linear programming problems. QP-MINOS - For large-scale sparse quadratic programming problems. LPOPT - For dense linear programming problems. QPOPT - For dense convex quadratic programming problems. LSSOL - For dense linear and convex quadratic programs, and constrained linear least squares problems. NLSSOL - For nonlinear least squares with linear and nonlinear constraints. NPSOL - For dense linear, quadratic and nonlinear programming. Please visit http://tomopt.com/tomlab/products/sol/ for more information. The interface between TOMLAB /NPSOL, Matlab and TOMLAB consists of two layers. The rst layer gives direct access from Matlab to NPSOL, via calling a Matlab function that calls a pre-compiled MEX le (DLL under Windows, shared library in UNIX) that denes and solves the problem in NPSOL. The second layer is a Matlab function that takes the input in the TOMLAB format, and calls the rst layer function. On return the function creates the output in the TOMLAB format.

USER'S GUIDE FOR TOMLAB /CPLEX v12.1

Welcome to the TOMLAB /CPLEX User's Guide. TOMLAB /CPLEX includes the ILOG CPLEX 12.1 (hereafter commonly referred to as CPLEX) solver and Matlab interfaces. The software allows for execution on any number of shared memory cores or cpus on a computer. The interface between ILOG CPLEX, Matlab and TOMLAB consists of two layers. The rst layer gives direct access from Matlab to CPLEX, via calling one Matlab function that calls a pre-compiled MEX le (DLL under Windows, shared library in UNIX) that denes and solves the problem in CPLEX . The second layer is a Matlab function that takes the input in the TOMLAB format, and calls the rst layer function. On return the function creates the output in the TOMLAB format. CPLEX has a whole set of callback routines. There is one predened Matlab routine for each callback. The user is in control of which ones to use, and should add his own code in Matlab for each callback. Conict rening, SA, warm start and solution pool control are supported by in the package.