# Questions tagged [model-predictive-control]

Model Predictive Control (MPC) is a process control method considering the system model and its predicted future optimization while respecting the defined constraints.

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### Solving mpc (model predictive control) on a microprocessor

I have a linear model dx/dt = Ax + Bu And I want to use model predictive control to control it. The sampling time is 100 microseconds and x is a vector with length 5 and u is a vector with length 6. ...
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### Dealing with multicollinear decisions in adaptive control/reinforcement learning that skews the underlying model parameters

Consider the following optimization/control problem: We aim to maximize the cumulative reward $R$ during the horizon $H$ by every day allocating a portion of total budget $B$ to our two different ...
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I have a question regarding the derivation of the adaptive law. Why do we derive the adaptive law-based parameter estimation algorithm in continuous time? Can we derive it in discrete time?
1 vote
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### Algorithms/Solvers for Hard Constrained Non-Linear Optimization Problems - Model Predictive Control Example

I have an autonomous robotic swarm path planning/control problem where a set of "leader" robots have predefined (nontrivial) dynamics in the control set, and "follower" robots are ...
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### Predictive numerical sequence in ADPCM decoding

I was inquiring about ADPCM type audio decoding, decoding where a predictive formula is used that I cannot find, despite having checked several articles and sites. If a prediction phase is added, in ...
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### Model Predictive Control with integral (end-of-horizon) constraints

Let $\mathscr T = \{0,1, \ldots, T\}$ denote the entire time horizon, $x : \mathscr T \to [0,1]$ the state and $u : \mathscr T \to \mathbb [0,1]$ the control. Consider the following problem: \begin{...
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1 vote
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### Model Predictive Control with Linear Programming VS Quadratic Programming

Model Predrictive Control is often used with Quadratic Programming. But I have tried Model Predictive Control with Linear Programming and it works very well. Let's begin with the discrete SISO state ...
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### Best optimization technique for solving overdetermined systems with a constraint

I am trying to make a prediction model based on a system of linear equations: $A\vec{x}=\vec{b}$, where $\vec{x}$ ($m\times1$) is my learning parameters, $A (m\times n)$ and $\vec{b}$ $(m\times1)$ are ...
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1 vote
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### Converting a nonlinear model predictive problem to parametric optimization problem

It is very well known that a linear model predictive control problem \begin{align} \label{eq:linear-original problem} \begin{aligned} &\text{minimize}_{(u_{t})_{t=0}^{N-...
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### Recommended python solver for an online optimization problem

I need to implement a load scheduling algorithm that involves solving an online optimisation problem from a research paper for my Real time systems course. This convex optimisation problem is setup ...
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### How to keep track of variations in states based on controls in optimal control problem?

Suppose that $x(t)$ is the state variable showing the level of water in a tank at time $t$, and water is leaking the tank with rate $\lambda$. Control is denoted by $u(t)$ which is the amount of water ...
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1 vote
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### Linear matrix inequality derivation from Risk-averse MPC problem

TLDR I need to use what looks like the Schur complement to transform a linear matrix inequality but instead of a $2\times 2$ block matrix there are more blocks. Question I'm having trouble with a ...
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### Invariant set for the heat equation

I have problems proving that a set of temperature distributions is invariant. I've been looking a lot for material related to my problem, but I was unable to find the correct keywords or relate the ...
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### Model Predictive Control gives always zero output solution - Why? Do I need soft constraints?

I have a discrete state space model: $$x(k+1) = Ax(k) + Bu(k)$$ $$y(k) = Cx(k)$$ And I'm trying to compute the predicted inputs. The first thing I do is that I fist create the extended observability ...
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