carsim与matlab联合仿真遇到的问题汇总（龚建伟的书）（1）
一、相关资源
【1】龚建伟书彩色版教程
感谢博主提供了彩色版教程，可以辅助书中的设置，彩色看起来更舒服
【2】CarSim&Simulink 联合仿真案例 知乎相关小问题汇总如下图是实际内容
二、实际遇到的问题（关于龚建伟书网络资源代码的错误）
1、运动学模型仿真
新版matlab不提供有效集法，需要改写为内点法。
具体报错是：The ‘active-set’ algorithm has been removed from quadprog. To avoid this error, choose a different algorithm: ‘interior-point-convex’ or ‘trust-region-reflective’.
应该进行如下图位置更改
这里的options需要更改到第二项，也可以找到matlab2011a版的quadprog函数对新版进行替换。这里给出旧版2011a的代码供替换使用：
function [X,fval,exitflag,output,lambda] = quadprog(H,f,A,B,Aeq,Beq,lb,ub,X0,options,varargin)
%QUADPROG Quadratic programming.
% X = QUADPROG(H,f,A,b) attempts to solve the quadratic programming
% problem:
%
% min 0.5*x'*H*x + f'*x subject to: A*x <= b
% x
%
% X = QUADPROG(H,f,A,b,Aeq,beq) solves the problem above while
% additionally satisfying the equality constraints Aeq*x = beq.
%
% X = QUADPROG(H,f,A,b,Aeq,beq,LB,UB) defines a set of lower and upper
% bounds on the design variables, X, so that the solution is in the
% range LB <= X <= UB. Use empty matrices for LB and UB if no bounds
% exist. Set LB(i) = -Inf if X(i) is unbounded below; set UB(i) = Inf if
% X(i) is unbounded above.
%
% X = QUADPROG(H,f,A,b,Aeq,beq,LB,UB,X0) sets the starting point to X0.
%
% X = QUADPROG(H,f,A,b,Aeq,beq,LB,UB,X0,OPTIONS) minimizes with the
% default optimization parameters replaced by values in the structure
% OPTIONS, an argument created with the OPTIMSET function. See OPTIMSET
% for details. Used options are Display, Diagnostics, TolX, TolFun,
% HessMult, LargeScale, MaxIter, PrecondBandWidth, TypicalX, TolPCG, and
% MaxPCGIter. Currently, only 'final' and 'off' are valid values for the
% parameter Display ('iter' is not available).
%
% X = QUADPROG(PROBLEM) finds the minimum for PROBLEM. PROBLEM is a
% structure with matrix 'H' in PROBLEM.H, the vector 'f' in PROBLEM.f,
% the linear inequality constraints in PROBLEM.Aineq and PROBLEM.bineq,
% the linear equality constraints in PROBLEM.Aeq and PROBLEM.beq, the
% lower bounds in PROBLEM.lb, the upper bounds in PROBLEM.ub, the start
% point in PROBLEM.x0, the options structure in PROBLEM.options, and
% solver name 'quadprog' in PROBLEM.solver. Use this syntax to solve at
% the command line a problem exported from OPTIMTOOL. The structure
% PROBLEM must have all the fields.
%
% [X,FVAL] = QUADPROG(H,f,A,b) returns the value of the objective
% function at X: FVAL = 0.5*X'*H*X + f'*X.
%
% [X,FVAL,EXITFLAG] = QUADPROG(H,f,A,b) returns an EXITFLAG that
% describes the exit condition of QUADPROG. Possible values of EXITFLAG
% and the corresponding exit conditions are
%
% All algorithms:
% 1 First order optimality conditions satisfied.
% 0 Maximum number of iterations exceeded.
% -2 No feasible point found.
% -3 Problem is unbounded.
% Interior-point-convex only:
% -6 Non-convex problem detected.
% Trust-region-reflective only:
% 3 Change in objective function too small.
% -4 Current search direction is not a descent direction; no further
% progress can be made.
% Active-set only:
% 4 Local minimizer found.
% -7 Magnitude of search direction became too small; no further
% progress can be made. The problem is ill-posed or badly
% conditioned.
%
% [X,FVAL,EXITFLAG,OUTPUT] = QUADPROG(H,f,A,b) returns a structure
% OUTPUT with the number of iterations taken in OUTPUT.iterations,
% maximum of constraint violations in OUTPUT.constrviolation, the
% type of algorithm used in OUTPUT.algorithm, the number of conjugate
% gradient iterations (if used) in OUTPUT.cgiterations, a measure of
% first order optimality (large-scale algorithm only) in
% OUTPUT.firstorderopt, and the exit message in OUTPUT.message.
%
% [X,FVAL,EXITFLAG,OUTPUT,LAMBDA] = QUADPROG(H,f,A,b) returns the set of
% Lagrangian multipliers LAMBDA, at the solution: LAMBDA.ineqlin for the
% linear inequalities A, LAMBDA.eqlin for the linear equalities Aeq,
% LAMBDA.lower for LB, and LAMBDA.upper for UB.
%
% See also LINPROG, LSQLIN.
% Copyright 1990-2010 The MathWorks, Inc.
% $Revision: 1.1.6.14 $ $Date: 2010/11/01 19:41:32 $
defaultopt = struct( ...
'Algorithm','trust-region-reflective', ...
'Diagnostics','off', ...
'Display','final', ...
'HessMult',[], ...
'LargeScale','on', ...
'MaxIter',[], ...
'MaxPCGIter','max(1,floor(numberOfVariables/2))', ...
'PrecondBandWidth',0, ...
'TolCon',1e-8, ...
'TolFun',[], ...
'TolPCG',0.1, ...
'TolX',100*eps, ...
'TypicalX','ones(numberOfVariables,1)' ...
);
% If just 'defaults' passed in, return the default options in X
if nargin == 1 && nargout <= 1 && isequal(H,'defaults')
X = defaultopt;
return
end
if nargin < 10
options = [];
if nargin < 9
X0 = [];
if nargin < 8
ub = [];
if nargin < 7
lb = [];
if nargin < 6
Beq = [];
if nargin < 5
Aeq = [];
if nargin < 4
B = [];
if nargin < 3
A = [];
end
end
end
end
end
end
end
end
% Detect problem structure input
if nargin == 1
if isa(H,'struct')
[H,f,A,B,Aeq,Beq,lb,ub,X0,options] = separateOptimStruct(H);
else % Single input and non-structure.
error(message('optim:quadprog:InputArg'));
end
end
if nargin == 0
error(message('optim:quadprog:NotEnoughInputs'))
end
% Check for non-double inputs
% SUPERIORFLOAT errors when superior input is neither single nor double;
% We use try-catch to override SUPERIORFLOAT's error message when input
% data type is integer.
try
dataType = superiorfloat(H,f,A,B,Aeq,Beq,lb,ub,X0);
catch ME
if strcmp(ME.identifier,'MATLAB:datatypes:superiorfloat')
dataType = 'notDouble';
end
end
if ~strcmp(dataType,'double')
error(message('optim:quadprog:NonDoubleInput'))
end
% Set up constant strings
activeSet = 'active-set';
trustRegReflect = 'trust-region-reflective';
interiorPointConvex = 'interior-point-convex';
if nargout > 4
computeLambda = true;
else
computeLambda = false;
end
if nargout > 3
computeConstrViolation = true;
computeFirstOrderOpt = true;
else
computeConstrViolation = false;
computeFirstOrderOpt = false;
end
% Options setup
largescale = isequal(optimget(options,'LargeScale',defaultopt,'fast'),'on');
Algorithm = optimget(options,'Algorithm',defaultopt,'fast');
diagnostics = isequal(optimget(options,'Diagnostics',defaultopt,'fast'),'on');
display = optimget(options,'Display',defaultopt,'fast');
detailedExitMsg = ~isempty(strfind(display,'detailed'));
switch display
case {'off', 'none'}
verbosity = 0;
case {'iter','iter-detailed'}
verbosity = 2;
case {'final','final-detailed'}
verbosity = 1;
case 'testing'
verbosity = 3;
otherwise
verbosity = 1;
end
% Determine algorithm user chose via options. (We need this now to set
% OUTPUT.algorithm in case of early termination due to inconsistent
% bounds.) This algorithm choice may be modified later when we check the
% problem type.
algChoiceOptsConflict = false;
if strcmpi(Algorithm,'active-set')
output.algorithm = activeSet;
elseif strcmpi(Algorithm,'interior-point-convex')
output.algorithm = interiorPointConvex;
elseif strcmpi(Algorithm,'trust-region-reflective')
if largescale
output.algorithm = trustRegReflect;
else
% Conflicting options Algorithm='trust-region-reflective' and
% LargeScale='off'. Choose active-set algorithm.
algChoiceOptsConflict = true; % Warn later, not in case of early termination
output.algorithm = activeSet;
end
else
error(message('optim:quadprog:InvalidAlgorithm'));
end
mtxmpy = optimget(options,'HessMult',defaultopt,'fast');
% Check for name clash
functionNameClashCheck('HessMult',mtxmpy,'hessMult_optimInternal','optim:quadprog:HessMultNameClash');
if isempty(mtxmpy)
% Internal Hessian-multiply function
mtxmpy = @hessMult_optimInternal;
usrSuppliedHessMult = false;
else
usrSuppliedHessMult = true;
end
% Set the constraints up: defaults and check size
[nineqcstr,numberOfVariablesineq] = size(A);
[neqcstr,numberOfVariableseq] = size(Aeq);
if isa(H,'double') && ~usrSuppliedHessMult
% H must be square and have the correct size
nColsH = size(H,2);
if nColsH ~= size(H,1)
error(message('optim:quadprog:NonSquareHessian'));
end
else % HessMult in effect, so H can be anything
nColsH = 0;
end
% Check the number of variables. The check must account for any combination of these cases:
% * User provides HessMult
% * The problem is linear (H = zeros, or H = [])
% * The objective has no linear component (f = [])
% * There are no linear constraints (A,Aeq = [])
% * There are no, or partially specified, bounds
% * There is no X0
numberOfVariables = ...
max([length(f),nColsH,numberOfVariablesineq,numberOfVariableseq]);
if numberOfVariables == 0
% If none of the problem quantities indicate the number of variables,
% check X0, even though some algorithms do not use it.
if isempty(X0)
error(message('optim:quadprog:EmptyProblem'));
else
% With all other data empty, use the X0 input to determine
% the number of variables.
numberOfVariables = length(X0);
end
end
ncstr = nineqcstr + neqcstr;
if isempty(f)
f = zeros(numberOfVariables,1);
else
% Make sure that the number of rows/columns in H matches the length of
% f under the following conditions:
% * The Hessian is passed in explicitly (no HessMult)
% * There is a non-empty Hessian
if ~usrSuppliedHessMult && ~isempty(H)
if length(f) ~= nColsH
error(message('optim:quadprog:MismatchObjCoefSize'));
end
end
end
if isempty(A)
A = zeros(0,numberOfVariables);
end
if isempty(B)
B = zeros(0,1);
end
if isempty(Aeq)
Aeq = zeros(0,numberOfVariables);
end
if isempty(Beq)
Beq = zeros(0,1);
end
% Expect vectors
f = f(:);
B = B(:);
Beq = Beq(:);
if ~isequal(length(B),nineqcstr)
error(message('optim:quadprog:InvalidSizesOfAAndB'))
elseif ~isequal(length(Beq),neqcstr)
error(message('optim:quadprog:InvalidSizesOfAeqAndBeq'))
elseif ~isequal(length(f),numberOfVariablesineq) && ~isempty(A)
error(message('optim:quadprog:InvalidSizesOfAAndF'))
elseif ~isequal(length(f),numberOfVariableseq) && ~isempty(Aeq)
error(message('optim:quadprog:InvalidSizesOfAeqAndf'))
end
[X0,lb,ub,msg] = checkbounds(X0,lb,ub,numberOfVariables);
if ~isempty(msg)
exitflag = -2;
X=X0; fval = []; lambda = [];
output.iterations = 0;
output.constrviolation = [];
output.algorithm = ''; % Not known at this stage
output.firstorderopt = [];
output.cgiterations = [];
output.message = msg;
if verbosity > 0
disp(msg)
end
return
end
% Check that all data is real
if ~(isreal(H) && isreal(A) && isreal(Aeq) && isreal(f) && ...
isreal(B) && isreal(Beq) && isreal(lb) && isreal(ub) && isreal(X0))
error(message('optim:quadprog:ComplexData'))
end
caller = 'quadprog';
% Check out H and make sure it isn't empty or all zeros
if isa(H,'double') && ~usrSuppliedHessMult
if norm(H,'inf')==0 || isempty(H)
% Really a lp problem
warning(message('optim:quadprog:NullHessian'))
[X,fval,exitflag,output,lambda]=linprog(f,A,B,Aeq,Beq,lb,ub,X0,options);
return
else
% Make sure it is symmetric
if norm(H-H',inf) > eps
if verbosity > -1
warning(message('optim:quadprog:HessianNotSym'))
end
H = (H+H')*0.5;
end
end
end
% Determine which algorithm and make sure problem matches.
hasIneqs = (nineqcstr > 0); % Does the problem have any inequalities?
hasEqsAndBnds = (neqcstr > 0) && (any(isfinite(ub)) || any(isfinite(lb))); % Does the problem have both equalities and bounds?
hasMoreEqsThanVars = (neqcstr > numberOfVariables); % Does the problem have more equalities than variables?
hasNoConstrs = (neqcstr == 0) && (nineqcstr == 0) && ...
all(eq(ub, inf)) && all(eq(lb, -inf)); % Does the problem not have equalities, bounds, or inequalities?
if (hasIneqs || hasEqsAndBnds || hasMoreEqsThanVars || hasNoConstrs) && ...
strcmpi(output.algorithm,trustRegReflect) || strcmpi(output.algorithm,activeSet)
% (has linear inequalites OR both equalities and bounds OR has no constraints OR
% has more equalities than variables) then call active-set code
if algChoiceOptsConflict
% Active-set algorithm chosen as a result of conflicting options
warning('optim:quadprog:QPAlgLargeScaleConflict', ...
['Options LargeScale = ''off'' and Algorithm = ''trust-region-reflective'' conflict. ' ...
'Ignoring Algorithm and running active-set algorithm. To run trust-region-reflective, set ' ...
'LargeScale = ''on''. To run active-set without this warning, set Algorithm = ''active-set''.']);
end
if strcmpi(output.algorithm,trustRegReflect)
warning('optim:quadprog:SwitchToMedScale', ...
['Trust-region-reflective algorithm does not solve this type of problem, ' ...
'using active-set algorithm. You could also try the interior-point-convex ' ...
'algorithm: set the Algorithm option to ''interior-point-convex'' ', ...
'and rerun. For more help, see %s in the documentation.'], ...
addLink('Choosing the Algorithm','choose_algorithm'))
end
output.algorithm = activeSet;
Algorithm = 'active-set';
if issparse(H) || issparse(A) || issparse(Aeq) % Passed in sparse matrices
warning(message('optim:quadprog:ConvertingToFull'))
end
H = full(H); A = full(A); Aeq = full(Aeq);
else
% Using trust-region-reflective or interior-point-convex algorithms
if ~usrSuppliedHessMult
H = sparse(H);
end
A = sparse(A); Aeq = sparse(Aeq);
end
if ~isa(H,'double') || usrSuppliedHessMult && ...
~strcmpi(output.algorithm,trustRegReflect)
error(message('optim:quadprog:NoHessMult', Algorithm))
end
if diagnostics
% Do diagnostics on information so far
gradflag = []; hessflag = []; line_search=[];
constflag = 0; gradconstflag = 0; non_eq=0;non_ineq=0;
lin_eq=size(Aeq,1); lin_ineq=size(A,1); XOUT=ones(numberOfVariables,1);
funfcn{1} = [];ff=[]; GRAD=[];HESS=[];
confcn{1}=[];c=[];ceq=[];cGRAD=[];ceqGRAD=[];
msg = diagnose('quadprog',output,gradflag,hessflag,constflag,gradconstflag,...
line_search,options,defaultopt,XOUT,non_eq,...
non_ineq,lin_eq,lin_ineq,lb,ub,funfcn,confcn,ff,GRAD,HESS,c,ceq,cGRAD,ceqGRAD);
end
% Trust-region-reflective
if strcmpi(output.algorithm,trustRegReflect)
% Call sqpmin when just bounds or just equalities
[X,fval,output,exitflag,lambda] = sqpmin(f,H,mtxmpy,X0,Aeq,Beq,lb,ub,verbosity, ...
options,defaultopt,computeLambda,computeConstrViolation,varargin{:});
if exitflag == -10 % Problem not handled by sqpmin at this time: dependent rows
warning(message('optim:quadprog:SwitchToMedScale'))
output.algorithm = activeSet;
if ~isa(H,'double') || usrSuppliedHessMult
error('optim:quadprog:NoHessMult', ...
'H must be specified explicitly for active-set algorithm: cannot use HessMult option.')
end
H = full(H); A = full(A); Aeq = full(Aeq);
end
end
% Call active-set algorithm
if strcmpi(output.algorithm,activeSet)
if isempty(X0)
X0 = zeros(numberOfVariables,1);
end
% Set default value of MaxIter for qpsub
defaultopt.MaxIter = 200;
% Create options structure for qpsub
qpoptions.MaxIter = optimget(options,'MaxIter',defaultopt,'fast');
% A fixed constraint tolerance (eps) is used for constraint
% satisfaction; no need to specify any value
qpoptions.TolCon = [];
[X,lambdaqp,exitflag,output,~,~,msg]= ...
qpsub(H,f,[Aeq;A],[Beq;B],lb,ub,X0,neqcstr,...
verbosity,caller,ncstr,numberOfVariables,qpoptions);
output.algorithm = activeSet; % have to reset since call to qpsub obliterates
end
if strcmpi(output.algorithm,interiorPointConvex)
defaultopt.MaxIter = 200;
defaultopt.TolFun = 1e-8;
% If the output structure is requested, we must reconstruct the
% Lagrange multipliers in the postsolve. Therefore, set computeLambda
% to true if the output structure is requested.
flags.computeLambda = computeFirstOrderOpt;
flags.detailedExitMsg = detailedExitMsg;
flags.verbosity = verbosity;
[X,fval,exitflag,output,lambda] = ipqpcommon(H,f,A,B,Aeq,Beq,lb,ub,X0, ...
flags,options,defaultopt,varargin{:});
% Presolve may have removed variables and constraints from the problem.
% Postsolve will re-insert the primal and dual solutions after the main
% algorithm has run. Therefore, constraint violation and first-order
% optimality must be re-computed.
%
% If no initial point was provided by the user and the presolve has
% declared the problem infeasible or unbounded, X will be empty. The
% lambda structure will also be empty, so do not compute constraint
% violation or first-order optimality if lambda is missing.
% Compute constraint violation if the output structure is requested
if computeFirstOrderOpt && ~isempty(lambda)
output.constrviolation = norm([Aeq*X-Beq; max([A*X - B;X - ub;lb - X],0)],Inf);
end
end
% Compute fval and first-order optimality if the active-set algorithm was
% run, or if the interior-point-convex algorithm was run (not stopped in presolve)
if (strcmpi(output.algorithm,interiorPointConvex) && ~isempty(lambda)) || ...
strcmpi(output.algorithm,activeSet)
% Compute objective function value
fval = 0.5*X'*(H*X)+f'*X;
% Compute lambda and exit message for active-set algorithm
if strcmpi(output.algorithm,activeSet)
if computeLambda || computeFirstOrderOpt
llb = length(lb);
lub = length(ub);
lambda.lower = zeros(llb,1);
lambda.upper = zeros(lub,1);
arglb = ~isinf(lb); lenarglb = nnz(arglb);
argub = ~isinf(ub); lenargub = nnz(argub);
lambda.eqlin = lambdaqp(1:neqcstr,1);
lambda.ineqlin = lambdaqp(neqcstr+1:neqcstr+nineqcstr,1);
lambda.lower(arglb) = lambdaqp(neqcstr+nineqcstr+1:neqcstr+nineqcstr+lenarglb);
lambda.upper(argub) = lambdaqp(neqcstr+nineqcstr+lenarglb+1: ...
neqcstr+nineqcstr+lenarglb+lenargub);
end
if exitflag == 1
normalTerminationMsg = sprintf('Optimization terminated.');
if verbosity > 0
disp(normalTerminationMsg)
end
if isempty(msg)
output.message = normalTerminationMsg;
else
% append normal termination msg to current output msg
output.message = sprintf('%s\n%s',msg,normalTerminationMsg);
end
else
output.message = msg;
end
end
% Compute first order optimality if needed
if computeFirstOrderOpt && ~isempty(lambda)
output.firstorderopt = computeKKTErrorForQPLP(H,f,A,B,Aeq,Beq,lb,ub,lambda,X);
else
output.firstorderopt = [];
end
output.cgiterations = [];
end
程序中给的约束条件注意是否有错误
发现程序在第一次运算后也就是0.05s后终止，flag=3，报维数错误
大概率应该是约束条件问题，可以尝试将松弛因子扩大，或者将限制适当放宽，预测长度控制长度变小等尝试。
我遇到的代码有问题：
要检查上述位置是否和我写的一样，因为网上的代码大多是被人调试过的，会出现错误。
2、动力学模型仿真
新版matlab会出现怎么更改参数都不能运行的情况
解决方法就是将上文提到的 quadprog 函数内容替换成2011a版本的就可以了。