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authorYanzhenXiang <54230111+YanzhenXiangRobotics@users.noreply.github.com>2023-11-06 16:49:26 +0100
committerGitHub <noreply@github.com>2023-11-06 16:49:26 +0100
commitd6b96c55ebb89ebd6e2d990ec89122799c68a230 (patch)
tree7a837438da9e07a5e77658c6d882577fff8bcae1 /templates/MPC_TS_SC.m
parentUpdate 23 (diff)
parentupdate the new submission zip generated from npross branch (diff)
downloadmpc_pe-master.tar.gz
mpc_pe-master.zip
Merge pull request #1 from YanzhenXiangRobotics/submissionHEADmaster
Submission
Diffstat (limited to '')
-rw-r--r--templates/MPC_TS_SC.m42
1 files changed, 39 insertions, 3 deletions
diff --git a/templates/MPC_TS_SC.m b/templates/MPC_TS_SC.m
index 0e64767..fb1c720 100644
--- a/templates/MPC_TS_SC.m
+++ b/templates/MPC_TS_SC.m
@@ -14,22 +14,58 @@ classdef MPC_TS_SC
methods
function obj = MPC_TS_SC(Q,R,N,H,h,S,v,params)
% YOUR CODE HERE
+ % initialize parameters
+ nu = params.model.nu;
+ nx = params.model.nx;
+ A=params.model.A;
+ B=params.model.B;
+ H_x = params.constraints.StateMatrix;
+ h_x = params.constraints.StateRHS;
+ H_u = params.constraints.InputMatrix;
+ h_u = params.constraints.InputRHS;
+ [~,P,~] = dlqr(A,B,Q,R);
+
+ U = sdpvar(repmat(nu,1,N),ones(1,N),'full');
+ X = sdpvar(repmat(nx,1,N+1),ones(1,N+1),'full');
+ sv = sdpvar(repmat(length(S(1,:)),1,N+1),ones(1,N+1),'full');
+
+ X0 = sdpvar(nx,1,'full');
+ objective = 0;
+ constraints = X{1} == X0;
+ % items for 1 to N
+ for k=1:N
+ objective = objective + X{k}' * Q * X{k} + U{k}' * R * U{k} + sv{k}' * S * sv{k} + v * max(sv{k});
+ constraints = [constraints, ...
+ X{k+1} == A * X{k} + B * U{k}, ...
+ H_x * X{k} <= h_x+sv{k}, ...
+ H_u * U{k} <= h_u, ...
+ sv{k} >= 0 ...
+ ];
+ end
+ % items for N+1
+ objective = objective + X{N+1}'*P*X{N+1} + sv{N+1}'*S*sv{N+1} + v*max(sv{N+1});
+ constraints = [constraints, ...
+ sv{N+1} >= 0, ...
+ H_x * X{N+1} <= h_x + sv{N+1} ...
+ ];
+ % maximum positively invariant set constraint
+ constraints = [constraints, H * X{N+1} <= h];
opts = sdpsettings('verbose',1,'solver','quadprog','quadprog.TolFun',1e-8);
obj.yalmip_optimizer = optimizer(constraints,objective,opts,X0,{U{1} objective});
end
function [u, ctrl_info] = eval(obj,x)
- %% evaluate control action by solving MPC problem, e.g.
+ % evaluate control action by solving MPC problem
tic;
- [optimizer_out,errorcode] = obj.yalmip_optimizer(x);
+ [optimizer_out,errorcode,~] = obj.yalmip_optimizer{x};
solvetime = toc;
+
[u, objective] = optimizer_out{:};
feasible = true;
if (errorcode ~= 0)
feasible = false;
end
-
ctrl_info = struct('ctrl_feas',feasible,'objective',objective,'solvetime',solvetime);
end
end