From 532c9636258f779955ec5ff7aba7c45ac64146e4 Mon Sep 17 00:00:00 2001 From: Nao Pross Date: Wed, 24 May 2023 16:39:28 +0200 Subject: Take deliverables for soft contraints from yuanxu Acording to table 9 - MPC_TS_SC - MPC_TS_SC/eval - MPC_TS_SC_script (.m and MPC_TS_SC_params.mat output file) --- templates/MPC_TS_SC.m | 42 +++++++++++++++++++++++++++++++++++++++--- 1 file changed, 39 insertions(+), 3 deletions(-) (limited to 'templates/MPC_TS_SC.m') 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 -- cgit v1.2.1