8/7/2023 0 Comments Fmincon%This is an alternative to the inline definition of y_mod() above. = fmincon(objective,p_initial,options) įprintf('RMS Error=%.3f, intercept=%.3f, slope=%.4f\n'. %If you use y_mod(), then you must define it somewhere X = Y = Global x %need this if define y_mod() separately, and in that case y_mod() must declare x global %Reply to stack exchange question on parameter fitting This version uses the multi-line declaration of y_mod(). It has many commented lines because it includes alternate ways to fit the data: an inline declaration of y_mod(), or a multi-line declaration of y_mod(), or no y_mod() at all. RMS Error=0.374, intercept=4.208, slope=0.0388Īnd here is code for the above. Here is the output from a script that has the function declaration: > modelFitExample2a How can I multiply p(2) with x? Where x is not optimized, because the values are given. However, it does not work if I use the following code. If I specify my function as follows then it works. The model is: y = alpha beta'*x.įor minimization, I am using Matlab's fmincon function and am struggling with multiplying my parameter p(2) by x. I test the option that you suggest, the simulation is the same (same messages, same situation) I think that the problem is related with the number of "call of the objective function" for every sample time, not the number of the use of fmincon at all the simulation.I have a simple model where I want to minimize the RMSE between my dependent variable y and my model values. You're right about "call of the objective function", it's not "run". About the controller, I use a real time simulation software with Matlab.Ībout the MPC, I use a non linear model, you can find the description here: The task selects the solver fmincon - Constrained nonlinear minimization. In the Specify problem type section of the task, select Objective > Nonlinear and Constraints > Nonlinear. Thank you very much for your help and all this suggestion and information. Click the Insert tab and then, in the Code section, select Task > Optimize. The number of function-calls when it raisis this warning depends on the number of parameters length(x0) as the algorithm first needs wiggle a little at all parameters in all directions. fmincon doesn't find a direction in your parameter space, in which it can step for decreasing the value of your objective-function. Without code, it is hard to follow your specific problem, but the message (and probalb the exitflag) tells you that either your initial value or your objective-function sucks. = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon,opts) opts = optimoptions('fmincon','MaxFunctionEvaluations',100) But you wanted to say " call of the objective function" as I guess) as one would to over and over again in MPC. For real implementations, I suggest to use the open-source library NLopt, which provides a nice Matlab interface and typically runs faster.įmincon Now to your actual question: can one limit the calls of the objective function in fmincon? (Note that your term " run" is confusing as this refers to the starting of the optimization. Have a look here at Matlab doc on how to choose the ideal algorithm. If you go for runtime (so if it is a real-controller and not just a simulation) this is a bad choice since you have just a (potentially constrained but) linear optimization problem (state-space representation as you noted). MPC That stands for model predictive control, which is a control technique that solves an optimization problem at every sample point for a defined future horizon (by this it determines the control law implicitly, so you don't need to do the nasty pole-placement in Laplacian-space).Īnyway, you use fmincon to solve this optimization problem.
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