MATLAB C 生成器能否生成适合嵌入式系统的 C 代码？
Can MATLAB C generation coder generate C-code that fits embedded system?
我需要将这段代码转换成 C 代码。
问题：
- MATLAB Coder 是否会生成内存安全的 C 代码，例如，它们不使用 calloc 或 malloc。 Misra C 标准不允许编码器使用动态内存分配。由于内存泄漏，这对嵌入式系统很危险。
- MATLAB Coder 会生成以动态矩阵作为参数的 C 代码吗？带参数的函数 foo(float* A, int m, int n) 或 foo(int m, int n, float A[m][n]) 或者固定大小示例 foo(float A[3][5])，仅作为选项提供？
- MATLAB Coder 能否生成可安装到嵌入式系统中的 C 代码。 .m 文件中的内部 C++ 命令如何，例如 horzcat、size 和 vertcat？它们会成为 100% 可移植的 C 代码吗？
MATLAB Coder 会生成具有引用调用的函数吗？示例 foo(float* input, float* output) 而不是 float* output = foo(float* input)
function [U] = mpc (A, B, C, x, N, r, lb)
## Find matrix
PHI = phiMat(A, C, N);
GAMMA = gammaMat(A, B, C, N);
## Solve first with no constraints
U = solve(PHI, GAMMA, x, N, r, 0, 0, false);
## Then use the last U as upper bound
U = solve(PHI, GAMMA, x, N, r, lb, U(end), true);
end
function U = solve(PHI, GAMMA, x, N, r, lb, ub, constraints)
## Set U
U = zeros(N, 1);
## Iterate Gaussian Elimination
for i = 1:N
## Solve u
if(i == 1)
u = (r - PHI(i,:)*x)/GAMMA(i,i)
else
u = (r - PHI(i,:)*x - GAMMA(i,1:i-1)*U(1:i-1) )/GAMMA(i,i)
end
## Constraints
if(constraints == true)
if(u > ub)
u = ub;
elseif(u < lb)
u = lb;
end
end
## Save u
U(i) = u
end
end
function PHI = phiMat(A, C, N)
## Create the special Observabillity matrix
PHI = [];
for i = 1:N
PHI = vertcat(PHI, C*A^i);
end
end
function GAMMA = gammaMat(A, B, C, N)
## Create the lower triangular toeplitz matrix
GAMMA = [];
for i = 1:N
GAMMA = horzcat(GAMMA, vertcat(zeros((i-1)*size(C*A*B, 1), size(C*A*B, 2)),cabMat(A, B, C, N-i+1)));
end
end
function CAB = cabMat(A, B, C, N)
## Create the column for the GAMMA matrix
CAB = [];
for i = 0:N-1
CAB = vertcat(CAB, C*A^i*B);
end
end
我的 C 代码。是的，它的工作！
/*
* Generalized_Predictive_Control.c
*
* Created on:
* Author:
*/
#include "Generalized_Predictive_Control.h"
/*
* Parameters
*/
int adim;
int ydim;
int rdim;
int horizon;
/*
* Deceleration
*/
static void obsv(float* PHI, const float* A, const float* C);
static void kalman(float* x, const float* A, const float* B, float* u, const float* K, float* y, const float* C);
static void mul(float* A, float* B, float* C, int row_a, int column_a, int column_b);
static void tran(float* A, int row, int column);
static void CAB(float* GAMMA, float* PHI, const float* A, const float* B, const float* C);
static void solve(float* GAMMA, float* PHI, float* x, float* u, float* r, float lb, float ub, int constraintsON);
static void print(float* A, int row, int column);
void GPC(int adim_, int ydim_, int rdim_, int horizon_, const float* A, const float* B, const float* C, const float* D, const float* K, float* u, float* r, float* y, float* x){
/*
* Set the dimensions
*/
adim = adim_;
ydim = ydim_;
rdim = rdim_;
horizon = horizon_;
/*
* Identify the model - Extended Least Square
*/
int n = 5;
float* phi;
float* theta;
//els(phi, theta, n, y, u, P);
/*
* Create a state space model with Observable canonical form
*/
/*
* Create the extended observability matrix
*/
float PHI[horizon*ydim*adim];
memset(PHI, 0, horizon*ydim*adim*sizeof(float));
obsv(PHI, A, C);
/*
* Create the lower triangular toeplitz matrix
*/
float GAMMA[horizon*rdim*horizon*ydim];
memset(GAMMA, 0, horizon*rdim*horizon*ydim*sizeof(float));
CAB(GAMMA, PHI, A, B, C);
/*
* Solve the best input value
*/
solve(GAMMA, PHI, x, u, r, 0, 0, 0);
solve(GAMMA, PHI, x, u, r, 0, *(u), 1);
/*
* Estimate the state vector
*/
kalman(x, A, B, u, K, y, C);
}
/*
* Identify the model
*/
static void els(float* P, float* phi, float* theta, int polyLength, int totalPolyLength, float* y, float* u, float* e){
/*
* move phi with the inputs, outputs, errors one step to right
*/
for(int i = 0; i < polyLength; i++){
*(phi + i+1 + totalPolyLength*0) = *(phi + i + totalPolyLength*0); // Move one to right for the y's
*(phi + i+1 + totalPolyLength*1) = *(phi + i + totalPolyLength*1); // Move one to right for the u's
*(phi + i+1 + totalPolyLength*2) = *(phi + i + totalPolyLength*2); // Move one to right for the e's
}
/*
* Add the current y, u and e
(*phi + totalPolyLength*0) = -*(y + 0); // Need to be negative!
(*phi + totalPolyLength*1) = *(u + 0);
(*phi + totalPolyLength*2) = *(e + 0);
*/
/*
* phi'*theta
*/
float y_est = 0;
for(int i = 0; i < totalPolyLength; i++){
y_est += *(phi + i) * *(theta + i);
}
float epsilon = *(y + 0) - y_est; // In this case, y is only one element array
/*
* phi*epsilon
*/
float phi_epsilon[totalPolyLength];
memset(phi_epsilon, 0, totalPolyLength*sizeof(float));
for(int i = 0; i < totalPolyLength; i++){
*(phi_epsilon + i) = *(phi + i) * epsilon;
}
/*
* P_vec = P*phi_epsilon
*/
float P_vec[totalPolyLength];
memset(P_vec, 0, totalPolyLength*sizeof(float));
mul(P, phi_epsilon, P_vec, totalPolyLength, totalPolyLength, 1);
/*
* Update our estimated vector theta = theta + P_vec
*/
for(int i = 0; i < totalPolyLength; i++){
*(theta + i) = *(theta + i) + *(P_vec + i);
}
/*
* Update P = P - (P*phi*phi'*P)/(1 + phi'*P*phi)
*/
// Create phi'
float phiT[totalPolyLength];
memset(phiT, 0, totalPolyLength*sizeof(float));
memcpy(phiT, phi, totalPolyLength*sizeof(float));
tran(phiT, totalPolyLength, 1);
// phi'*P
float phiT_P[totalPolyLength];
memset(phiT_P, 0, totalPolyLength*sizeof(float));
mul(phiT, P, phiT_P, 1, totalPolyLength, totalPolyLength);
// phi*phi'*P
float phi_phiT_P[totalPolyLength*totalPolyLength];
memset(phi_phiT_P, 0, totalPolyLength*totalPolyLength*sizeof(float));
mul(phi, phiT_P, phi_phiT_P, totalPolyLength, 1, totalPolyLength);
// P*phi*phi'*P
float P_phi_phiT_P[totalPolyLength*totalPolyLength];
memset(P_phi_phiT_P, 0, totalPolyLength*totalPolyLength*sizeof(float));
mul(P, phi_phiT_P, P_phi_phiT_P, totalPolyLength, totalPolyLength, totalPolyLength);
// P*phi
float P_phi[totalPolyLength];
memset(P_phi, 0, totalPolyLength*sizeof(float));
mul(P, phi, P_phi, totalPolyLength, totalPolyLength, 1);
// phi'*P*phi
float phiT_P_phi[1];
memset(phiT_P_phi, 0, 1*sizeof(float));
mul(phiT, P_phi, phiT_P_phi, 1, totalPolyLength, 1);
// P = P - (P_phi_phiT_P) / (1+phi'*P*phi)
for(int i = 0; i < totalPolyLength*totalPolyLength; i++){
*(P + i) = *(P + i) - *(P_phi_phiT_P + i) / (1 + *(phiT_P_phi));
}
}
/*
* This will solve if GAMMA is square!
*/
static void solve(float* GAMMA, float* PHI, float* x, float* u, float* r, float lb, float ub, int constraintsON){
/*
* Now we are going to solve on the form
* Ax=b, where b = (R*r-PHI*x) and A = GAMMA and x = U
*/
/*
* R_vec = R*r
*/
float R_vec[horizon*ydim];
memset(R_vec, 0, horizon*ydim*sizeof(float));
for(int i = 0; i < horizon*ydim; i++){
for (int j = 0; j < rdim; j++) {
*(R_vec + i + j) = *(r + j);
}
i += rdim-1;
}
/*
* PHI_vec = PHI*x
*/
float PHI_vec[horizon*ydim];
memset(PHI_vec, 0, horizon * ydim * sizeof(float));
mul(PHI, x, PHI_vec, horizon*ydim, adim, 1);
/*
* Solve now (R_vec - PHI_vec) = GAMMA*U
* Notice that this is ONLY for Square GAMMA with lower triangular toeplitz matrix e.g SISO case
* This using Gaussian Elimination backward substitution
*/
float U[horizon];
float sum = 0.0;
memset(U, 0, horizon*sizeof(float));
for(int i = 0; i < horizon; i++){
for(int j = 0; j < i; j++){
sum += *(GAMMA + i*horizon + j) * *(U + j);
}
float newU = (*(R_vec + i) - *(PHI_vec + i) - sum) / (*(GAMMA + i*horizon + i));
if(constraintsON == 1){
if(newU > ub)
newU = ub;
if(newU < lb)
newU = lb;
}
*(U + i) = newU;
sum = 0.0;
}
//print(U, horizon, 1);
/*
* Set last U to u
*/
if(constraintsON == 0){
*(u + 0) = *(U + horizon - 1);
}else{
*(u + 0) = *(U + 0);
}
}
/*
* Lower traingular toeplitz of extended observability matrix
*/
static void CAB(float* GAMMA, float* PHI, const float* A, const float* B, const float* C){
/*
* First create the initial C*A^0*B == C*I*B == C*B
*/
float CB[ydim*rdim];
memset(CB, 0, ydim*rdim*sizeof(float));
mul((float*)C, (float*)B, CB, ydim, adim, rdim);
/*
* Take the transpose of CB so it will have dimension rdim*ydim instead
*/
tran(CB, ydim, rdim);
/*
* Create the CAB matrix from PHI*B
*/
float PHIB[horizon*ydim*rdim];
mul(PHI, (float*) B, PHIB, horizon*ydim, adim, rdim); // CAB = PHI*B
tran(PHIB, horizon*ydim, rdim);
/*
* We insert GAMMA = [CB PHI;
* 0 CB PHI;
* 0 0 CB PHI;
* 0 0 0 CB PHI] from left to right
*/
for(int i = 0; i < horizon; i++) {
for(int j = 0; j < rdim; j++) {
memcpy(GAMMA + horizon*ydim*(i*rdim+j) + ydim*i, CB + ydim*j, ydim*sizeof(float)); // Add CB
memcpy(GAMMA + horizon*ydim*(i*rdim+j) + ydim*i + ydim, PHIB + horizon*ydim*j, (horizon-i-1)*ydim*sizeof(float)); // Add PHI*B
}
}
/*
* Transpose of gamma
*/
tran(GAMMA, horizon*rdim, horizon*ydim);
//print(CB, rdim, ydim);
//print(PHIB, rdim, horizon*ydim);
//print(GAMMA, horizon*ydim, horizon*rdim);
}
/*
* Transpose
*/
static void tran(float* A, int row, int column) {
float B[row*column];
float* transpose;
float* ptr_A = A;
for (int i = 0; i < row; i++) {
transpose = &B[i];
for (int j = 0; j < column; j++) {
*transpose = *ptr_A;
ptr_A++;
transpose += row;
}
}
// Copy!
memcpy(A, B, row*column*sizeof(float));
}
/*
* [C*A^1; C*A^2; C*A^3; ... ; C*A^horizon] % Extended observability matrix
*/
static void obsv(float* PHI, const float* A, const float* C){
/*
* This matrix will A^(i+1) all the time
*/
float A_pow[adim*adim];
memset(A_pow, 0, adim * adim * sizeof(float));
float A_copy[adim*adim];
memcpy(A_copy, (float*) A, adim * adim * sizeof(float));
/*
* Temporary matrix
*/
float T[ydim*adim];
memset(T, 0, ydim * adim * sizeof(float));
/*
* Regular T = C*A^(1+i)
*/
mul((float*) C, (float*) A, T, ydim, adim, adim);
/*
* Insert temporary T into PHI
*/
memcpy(PHI, T, ydim*adim*sizeof(float));
/*
* Do the rest C*A^(i+1) because we have already done i = 0
*/
for(int i = 1; i < horizon; i++){
mul((float*) A, A_copy, A_pow, adim, adim, adim); // Matrix power A_pow = A*A_copy
mul((float*) C, A_pow, T, ydim, adim, adim); // T = C*A^(1+i)
memcpy(PHI + i*ydim*adim, T, ydim*adim*sizeof(float)); // Insert temporary T into PHI
memcpy(A_copy, A_pow, adim * adim * sizeof(float)); // A_copy <- A_pow
}
}
/*
* x = Ax - KCx + Bu + Ky % Kalman filter
*/
static void kalman(float* x, const float* A, const float* B, float* u, const float* K, float* y, const float* C) {
/*
* Compute the vector A_vec = A*x
*/
float A_vec[adim*1];
memset(A_vec, 0, adim*sizeof(float));
mul((float*) A, x, A_vec, adim, adim, 1);
/*
* Compute the vector B_vec = B*u
*/
float B_vec[adim*1];
memset(B_vec, 0, adim*sizeof(float));
mul((float*) B, u, B_vec, adim, rdim, 1);
/*
* Compute the vector C_vec = C*x
*/
float C_vec[ydim*1];
memset(C_vec, 0, ydim*sizeof(float));
mul((float*) C, x, C_vec, ydim, adim, 1);
/*
* Compute the vector KC_vec = K*C_vec
*/
float KC_vec[adim*1];
memset(KC_vec, 0, adim*sizeof(float));
mul((float*) K, C_vec, KC_vec, adim, ydim, 1);
/*
* Compute the vector Ky_vec = K*y
*/
float Ky_vec[adim*1];
memset(Ky_vec, 0, adim*sizeof(float));
mul((float*) K, y, Ky_vec, adim, ydim, 1);
/*
* Now add x = A_vec - KC_vec + B_vec + Ky_vec
*/
for(int i = 0; i < adim; i++){
*(x + i) = *(A_vec + i) - *(KC_vec + i) + *(B_vec + i) + *(Ky_vec + i);
}
}
/*
* C = A*B
*/
static void mul(float* A, float* B, float* C, int row_a, int column_a, int column_b) {
// Data matrix
float* data_a = A;
float* data_b = B;
for (int i = 0; i < row_a; i++) {
// Then we go through every column of b
for (int j = 0; j < column_b; j++) {
data_a = &A[i * column_a];
data_b = &B[j];
*C = 0; // Reset
// And we multiply rows from a with columns of b
for (int k = 0; k < column_a; k++) {
*C += *data_a * *data_b;
data_a++;
data_b += column_b;
}
C++; // ;)
}
}
}
/*
* Print matrix or vector - Just for error check
*/
static void print(float* A, int row, int column) {
for (int i = 0; i < row; i++) {
for (int j = 0; j < column; j++) {
printf("%0.18f ", *(A++));
}
printf("\n");
}
printf("\n");
}
免责声明：我从事 MATLAB Coder
有一个 configuration setting 告诉 MATLAB Coder 在不使用动态分配的内存的情况下生成代码，或者发出错误告诉您为什么它不能这样做。
cfg = coder.config('lib');
cfg.DynamicMemoryAllocation = 'Off';
codegen -config cfg ...
MATLAB Coder 支持使用固定大小数组、可变大小数组和动态分配数组生成代码。 the documentation中显示了各种生成的签名格式。对于非动态分配的可变大小数组，常见的签名类似于：foo(x_data[100], x_size[2])
是的，对于您在生成代码时指定的硬件，生成的代码通常是可移植的并且独立于 MATLAB。代码生成支持的可用函数和 类 的完整列表是 listed here。在极少数情况下，生成的代码需要依赖 MATLAB 中的库。这些案例将在文档中列出。 horzcat 和 vertcat 等基本操作生成独立于 MATLAB 的可移植代码。
是的。对于数组输出和具有多个输出的 MATLAB 函数，生成的代码将 return 通过引用输出。它还支持在某些情况下通过引用传递参数，当相应的 MATLAB 函数具有 same variable as an input and output 时：function A = foo(A,B) 调用如：y = foo(y,z); 可以产生类似 void foo(double A[100], const double B[20]); 的内容，其中 A是一个输入输出。
我需要将这段代码转换成 C 代码。
问题：
- MATLAB Coder 是否会生成内存安全的 C 代码，例如，它们不使用 calloc 或 malloc。 Misra C 标准不允许编码器使用动态内存分配。由于内存泄漏，这对嵌入式系统很危险。
- MATLAB Coder 会生成以动态矩阵作为参数的 C 代码吗？带参数的函数 foo(float* A, int m, int n) 或 foo(int m, int n, float A[m][n]) 或者固定大小示例 foo(float A[3][5])，仅作为选项提供？
- MATLAB Coder 能否生成可安装到嵌入式系统中的 C 代码。 .m 文件中的内部 C++ 命令如何，例如 horzcat、size 和 vertcat？它们会成为 100% 可移植的 C 代码吗？
MATLAB Coder 会生成具有引用调用的函数吗？示例 foo(float* input, float* output) 而不是 float* output = foo(float* input)
function [U] = mpc (A, B, C, x, N, r, lb) ## Find matrix PHI = phiMat(A, C, N); GAMMA = gammaMat(A, B, C, N); ## Solve first with no constraints U = solve(PHI, GAMMA, x, N, r, 0, 0, false); ## Then use the last U as upper bound U = solve(PHI, GAMMA, x, N, r, lb, U(end), true); end function U = solve(PHI, GAMMA, x, N, r, lb, ub, constraints) ## Set U U = zeros(N, 1); ## Iterate Gaussian Elimination for i = 1:N ## Solve u if(i == 1) u = (r - PHI(i,:)*x)/GAMMA(i,i) else u = (r - PHI(i,:)*x - GAMMA(i,1:i-1)*U(1:i-1) )/GAMMA(i,i) end ## Constraints if(constraints == true) if(u > ub) u = ub; elseif(u < lb) u = lb; end end ## Save u U(i) = u end end function PHI = phiMat(A, C, N) ## Create the special Observabillity matrix PHI = []; for i = 1:N PHI = vertcat(PHI, C*A^i); end end function GAMMA = gammaMat(A, B, C, N) ## Create the lower triangular toeplitz matrix GAMMA = []; for i = 1:N GAMMA = horzcat(GAMMA, vertcat(zeros((i-1)*size(C*A*B, 1), size(C*A*B, 2)),cabMat(A, B, C, N-i+1))); end end function CAB = cabMat(A, B, C, N) ## Create the column for the GAMMA matrix CAB = []; for i = 0:N-1 CAB = vertcat(CAB, C*A^i*B); end end
我的 C 代码。是的，它的工作！
/*
* Generalized_Predictive_Control.c
*
* Created on:
* Author:
*/
#include "Generalized_Predictive_Control.h"
/*
* Parameters
*/
int adim;
int ydim;
int rdim;
int horizon;
/*
* Deceleration
*/
static void obsv(float* PHI, const float* A, const float* C);
static void kalman(float* x, const float* A, const float* B, float* u, const float* K, float* y, const float* C);
static void mul(float* A, float* B, float* C, int row_a, int column_a, int column_b);
static void tran(float* A, int row, int column);
static void CAB(float* GAMMA, float* PHI, const float* A, const float* B, const float* C);
static void solve(float* GAMMA, float* PHI, float* x, float* u, float* r, float lb, float ub, int constraintsON);
static void print(float* A, int row, int column);
void GPC(int adim_, int ydim_, int rdim_, int horizon_, const float* A, const float* B, const float* C, const float* D, const float* K, float* u, float* r, float* y, float* x){
/*
* Set the dimensions
*/
adim = adim_;
ydim = ydim_;
rdim = rdim_;
horizon = horizon_;
/*
* Identify the model - Extended Least Square
*/
int n = 5;
float* phi;
float* theta;
//els(phi, theta, n, y, u, P);
/*
* Create a state space model with Observable canonical form
*/
/*
* Create the extended observability matrix
*/
float PHI[horizon*ydim*adim];
memset(PHI, 0, horizon*ydim*adim*sizeof(float));
obsv(PHI, A, C);
/*
* Create the lower triangular toeplitz matrix
*/
float GAMMA[horizon*rdim*horizon*ydim];
memset(GAMMA, 0, horizon*rdim*horizon*ydim*sizeof(float));
CAB(GAMMA, PHI, A, B, C);
/*
* Solve the best input value
*/
solve(GAMMA, PHI, x, u, r, 0, 0, 0);
solve(GAMMA, PHI, x, u, r, 0, *(u), 1);
/*
* Estimate the state vector
*/
kalman(x, A, B, u, K, y, C);
}
/*
* Identify the model
*/
static void els(float* P, float* phi, float* theta, int polyLength, int totalPolyLength, float* y, float* u, float* e){
/*
* move phi with the inputs, outputs, errors one step to right
*/
for(int i = 0; i < polyLength; i++){
*(phi + i+1 + totalPolyLength*0) = *(phi + i + totalPolyLength*0); // Move one to right for the y's
*(phi + i+1 + totalPolyLength*1) = *(phi + i + totalPolyLength*1); // Move one to right for the u's
*(phi + i+1 + totalPolyLength*2) = *(phi + i + totalPolyLength*2); // Move one to right for the e's
}
/*
* Add the current y, u and e
(*phi + totalPolyLength*0) = -*(y + 0); // Need to be negative!
(*phi + totalPolyLength*1) = *(u + 0);
(*phi + totalPolyLength*2) = *(e + 0);
*/
/*
* phi'*theta
*/
float y_est = 0;
for(int i = 0; i < totalPolyLength; i++){
y_est += *(phi + i) * *(theta + i);
}
float epsilon = *(y + 0) - y_est; // In this case, y is only one element array
/*
* phi*epsilon
*/
float phi_epsilon[totalPolyLength];
memset(phi_epsilon, 0, totalPolyLength*sizeof(float));
for(int i = 0; i < totalPolyLength; i++){
*(phi_epsilon + i) = *(phi + i) * epsilon;
}
/*
* P_vec = P*phi_epsilon
*/
float P_vec[totalPolyLength];
memset(P_vec, 0, totalPolyLength*sizeof(float));
mul(P, phi_epsilon, P_vec, totalPolyLength, totalPolyLength, 1);
/*
* Update our estimated vector theta = theta + P_vec
*/
for(int i = 0; i < totalPolyLength; i++){
*(theta + i) = *(theta + i) + *(P_vec + i);
}
/*
* Update P = P - (P*phi*phi'*P)/(1 + phi'*P*phi)
*/
// Create phi'
float phiT[totalPolyLength];
memset(phiT, 0, totalPolyLength*sizeof(float));
memcpy(phiT, phi, totalPolyLength*sizeof(float));
tran(phiT, totalPolyLength, 1);
// phi'*P
float phiT_P[totalPolyLength];
memset(phiT_P, 0, totalPolyLength*sizeof(float));
mul(phiT, P, phiT_P, 1, totalPolyLength, totalPolyLength);
// phi*phi'*P
float phi_phiT_P[totalPolyLength*totalPolyLength];
memset(phi_phiT_P, 0, totalPolyLength*totalPolyLength*sizeof(float));
mul(phi, phiT_P, phi_phiT_P, totalPolyLength, 1, totalPolyLength);
// P*phi*phi'*P
float P_phi_phiT_P[totalPolyLength*totalPolyLength];
memset(P_phi_phiT_P, 0, totalPolyLength*totalPolyLength*sizeof(float));
mul(P, phi_phiT_P, P_phi_phiT_P, totalPolyLength, totalPolyLength, totalPolyLength);
// P*phi
float P_phi[totalPolyLength];
memset(P_phi, 0, totalPolyLength*sizeof(float));
mul(P, phi, P_phi, totalPolyLength, totalPolyLength, 1);
// phi'*P*phi
float phiT_P_phi[1];
memset(phiT_P_phi, 0, 1*sizeof(float));
mul(phiT, P_phi, phiT_P_phi, 1, totalPolyLength, 1);
// P = P - (P_phi_phiT_P) / (1+phi'*P*phi)
for(int i = 0; i < totalPolyLength*totalPolyLength; i++){
*(P + i) = *(P + i) - *(P_phi_phiT_P + i) / (1 + *(phiT_P_phi));
}
}
/*
* This will solve if GAMMA is square!
*/
static void solve(float* GAMMA, float* PHI, float* x, float* u, float* r, float lb, float ub, int constraintsON){
/*
* Now we are going to solve on the form
* Ax=b, where b = (R*r-PHI*x) and A = GAMMA and x = U
*/
/*
* R_vec = R*r
*/
float R_vec[horizon*ydim];
memset(R_vec, 0, horizon*ydim*sizeof(float));
for(int i = 0; i < horizon*ydim; i++){
for (int j = 0; j < rdim; j++) {
*(R_vec + i + j) = *(r + j);
}
i += rdim-1;
}
/*
* PHI_vec = PHI*x
*/
float PHI_vec[horizon*ydim];
memset(PHI_vec, 0, horizon * ydim * sizeof(float));
mul(PHI, x, PHI_vec, horizon*ydim, adim, 1);
/*
* Solve now (R_vec - PHI_vec) = GAMMA*U
* Notice that this is ONLY for Square GAMMA with lower triangular toeplitz matrix e.g SISO case
* This using Gaussian Elimination backward substitution
*/
float U[horizon];
float sum = 0.0;
memset(U, 0, horizon*sizeof(float));
for(int i = 0; i < horizon; i++){
for(int j = 0; j < i; j++){
sum += *(GAMMA + i*horizon + j) * *(U + j);
}
float newU = (*(R_vec + i) - *(PHI_vec + i) - sum) / (*(GAMMA + i*horizon + i));
if(constraintsON == 1){
if(newU > ub)
newU = ub;
if(newU < lb)
newU = lb;
}
*(U + i) = newU;
sum = 0.0;
}
//print(U, horizon, 1);
/*
* Set last U to u
*/
if(constraintsON == 0){
*(u + 0) = *(U + horizon - 1);
}else{
*(u + 0) = *(U + 0);
}
}
/*
* Lower traingular toeplitz of extended observability matrix
*/
static void CAB(float* GAMMA, float* PHI, const float* A, const float* B, const float* C){
/*
* First create the initial C*A^0*B == C*I*B == C*B
*/
float CB[ydim*rdim];
memset(CB, 0, ydim*rdim*sizeof(float));
mul((float*)C, (float*)B, CB, ydim, adim, rdim);
/*
* Take the transpose of CB so it will have dimension rdim*ydim instead
*/
tran(CB, ydim, rdim);
/*
* Create the CAB matrix from PHI*B
*/
float PHIB[horizon*ydim*rdim];
mul(PHI, (float*) B, PHIB, horizon*ydim, adim, rdim); // CAB = PHI*B
tran(PHIB, horizon*ydim, rdim);
/*
* We insert GAMMA = [CB PHI;
* 0 CB PHI;
* 0 0 CB PHI;
* 0 0 0 CB PHI] from left to right
*/
for(int i = 0; i < horizon; i++) {
for(int j = 0; j < rdim; j++) {
memcpy(GAMMA + horizon*ydim*(i*rdim+j) + ydim*i, CB + ydim*j, ydim*sizeof(float)); // Add CB
memcpy(GAMMA + horizon*ydim*(i*rdim+j) + ydim*i + ydim, PHIB + horizon*ydim*j, (horizon-i-1)*ydim*sizeof(float)); // Add PHI*B
}
}
/*
* Transpose of gamma
*/
tran(GAMMA, horizon*rdim, horizon*ydim);
//print(CB, rdim, ydim);
//print(PHIB, rdim, horizon*ydim);
//print(GAMMA, horizon*ydim, horizon*rdim);
}
/*
* Transpose
*/
static void tran(float* A, int row, int column) {
float B[row*column];
float* transpose;
float* ptr_A = A;
for (int i = 0; i < row; i++) {
transpose = &B[i];
for (int j = 0; j < column; j++) {
*transpose = *ptr_A;
ptr_A++;
transpose += row;
}
}
// Copy!
memcpy(A, B, row*column*sizeof(float));
}
/*
* [C*A^1; C*A^2; C*A^3; ... ; C*A^horizon] % Extended observability matrix
*/
static void obsv(float* PHI, const float* A, const float* C){
/*
* This matrix will A^(i+1) all the time
*/
float A_pow[adim*adim];
memset(A_pow, 0, adim * adim * sizeof(float));
float A_copy[adim*adim];
memcpy(A_copy, (float*) A, adim * adim * sizeof(float));
/*
* Temporary matrix
*/
float T[ydim*adim];
memset(T, 0, ydim * adim * sizeof(float));
/*
* Regular T = C*A^(1+i)
*/
mul((float*) C, (float*) A, T, ydim, adim, adim);
/*
* Insert temporary T into PHI
*/
memcpy(PHI, T, ydim*adim*sizeof(float));
/*
* Do the rest C*A^(i+1) because we have already done i = 0
*/
for(int i = 1; i < horizon; i++){
mul((float*) A, A_copy, A_pow, adim, adim, adim); // Matrix power A_pow = A*A_copy
mul((float*) C, A_pow, T, ydim, adim, adim); // T = C*A^(1+i)
memcpy(PHI + i*ydim*adim, T, ydim*adim*sizeof(float)); // Insert temporary T into PHI
memcpy(A_copy, A_pow, adim * adim * sizeof(float)); // A_copy <- A_pow
}
}
/*
* x = Ax - KCx + Bu + Ky % Kalman filter
*/
static void kalman(float* x, const float* A, const float* B, float* u, const float* K, float* y, const float* C) {
/*
* Compute the vector A_vec = A*x
*/
float A_vec[adim*1];
memset(A_vec, 0, adim*sizeof(float));
mul((float*) A, x, A_vec, adim, adim, 1);
/*
* Compute the vector B_vec = B*u
*/
float B_vec[adim*1];
memset(B_vec, 0, adim*sizeof(float));
mul((float*) B, u, B_vec, adim, rdim, 1);
/*
* Compute the vector C_vec = C*x
*/
float C_vec[ydim*1];
memset(C_vec, 0, ydim*sizeof(float));
mul((float*) C, x, C_vec, ydim, adim, 1);
/*
* Compute the vector KC_vec = K*C_vec
*/
float KC_vec[adim*1];
memset(KC_vec, 0, adim*sizeof(float));
mul((float*) K, C_vec, KC_vec, adim, ydim, 1);
/*
* Compute the vector Ky_vec = K*y
*/
float Ky_vec[adim*1];
memset(Ky_vec, 0, adim*sizeof(float));
mul((float*) K, y, Ky_vec, adim, ydim, 1);
/*
* Now add x = A_vec - KC_vec + B_vec + Ky_vec
*/
for(int i = 0; i < adim; i++){
*(x + i) = *(A_vec + i) - *(KC_vec + i) + *(B_vec + i) + *(Ky_vec + i);
}
}
/*
* C = A*B
*/
static void mul(float* A, float* B, float* C, int row_a, int column_a, int column_b) {
// Data matrix
float* data_a = A;
float* data_b = B;
for (int i = 0; i < row_a; i++) {
// Then we go through every column of b
for (int j = 0; j < column_b; j++) {
data_a = &A[i * column_a];
data_b = &B[j];
*C = 0; // Reset
// And we multiply rows from a with columns of b
for (int k = 0; k < column_a; k++) {
*C += *data_a * *data_b;
data_a++;
data_b += column_b;
}
C++; // ;)
}
}
}
/*
* Print matrix or vector - Just for error check
*/
static void print(float* A, int row, int column) {
for (int i = 0; i < row; i++) {
for (int j = 0; j < column; j++) {
printf("%0.18f ", *(A++));
}
printf("\n");
}
printf("\n");
}
免责声明：我从事 MATLAB Coder
有一个 configuration setting 告诉 MATLAB Coder 在不使用动态分配的内存的情况下生成代码，或者发出错误告诉您为什么它不能这样做。
cfg = coder.config('lib'); cfg.DynamicMemoryAllocation = 'Off'; codegen -config cfg ...MATLAB Coder 支持使用固定大小数组、可变大小数组和动态分配数组生成代码。 the documentation中显示了各种生成的签名格式。对于非动态分配的可变大小数组，常见的签名类似于：
foo(x_data[100], x_size[2])是的，对于您在生成代码时指定的硬件，生成的代码通常是可移植的并且独立于 MATLAB。代码生成支持的可用函数和 类 的完整列表是 listed here。在极少数情况下，生成的代码需要依赖 MATLAB 中的库。这些案例将在文档中列出。
horzcat和vertcat等基本操作生成独立于 MATLAB 的可移植代码。是的。对于数组输出和具有多个输出的 MATLAB 函数，生成的代码将 return 通过引用输出。它还支持在某些情况下通过引用传递参数，当相应的 MATLAB 函数具有 same variable as an input and output 时：
function A = foo(A,B)调用如：y = foo(y,z);可以产生类似void foo(double A[100], const double B[20]);的内容，其中A是一个输入输出。