Gaussian process (GP) regression has been widely used in supervised machine learning for its flexibility and inherent ability to describe uncertainty in the function estimation. In the context of control, it is seeing increasing use for modeling of nonlinear dynamical systems from data, as it allows the direct assessment of residual model uncertainty. We present a model predictive control (MPC)...

A model predictive control (MPC) scheme for a permanentmagnet synchronous motor (PMSM) is presented. The torque controller optimizes a quadratic cost consisting of control error and machine losses repeatedly, accounting the voltage and current limitations. The scheme extensively relies on optimization, to meet the runtime limitation, a suboptimal algorithm based on differential flatness, contin...

This paper presents a method to compute an approximate explicit model predictive control (MPC) law using neural networks. The optimal MPC control law for constrained linear quadratic regulator (LQR) systems is piecewise affine on polytopes. However, computing this optimal control law becomes computationally intractable for large problems, and motivates the application of reinforcement learning ...

Networked control systems (NCSs) present many advantages such as easy installation and maintenance, flexible layouts and structures of components, and efficient allocation and distribution of resources. Consequently, they find potential applications in a variety of emerging industrial systems including multi-agent systems, power grids, tele-operations and cyberphysical systems. The study of NCS...

This paper aims to discuss the approach of constrained modified feedback linearization model predictive control for spacecraft simulator. By utilizing high accuracy and properties (MPC), an optimum MPC is designed linearized system. The composite controller has ability both attitude angular velocity reaction wheels (i.e. steering momentum zero at end maneuver). simulation experimental results d...

I re-examine mass estimates of the Coma cluster from pre-ASCA X-ray spectral observations. A large range of model dark matter distributions are examined, under the assumptions of hydrostatic equilibrium and spherical symmetry, to determine the widest possible allowed range on the total mass of the cluster. Within a radius of 1 Mpc, the total cluster mass is tightly constrained to be (6.2 ± 0.9)...

Abstract: Type 1 diabetes is characterized by a lack of insulin production from the pancreas, causing high blood glucose concentrations and requiring external insulin infusion to regulate blood glucose. A novel procedure of “human-friendly” identification testing using multisine inputs is developed to estimate suitable models for use in an artificial pancreas. A human-friendlymultisine input si...

Discrete-event systems with synchronization but no concurrency can be described by models that are “linear” in the max-plus algebra, and they are called max-plus-linear (MPL) systems. Examples of MPL systems often arise in the context of manufacturing systems, telecommunication networks, railway networks, parallel computing, etc. In this paper we provide a solution to a finite-horizon model pre...

Explicit piecewise linear (PWL) state feedback laws solving constrained linear model predictive control (MPC) problems can be obtained by solving multi-parametric quadratic programs (mp-QP) where the parameters are the elements of the state vector. This allows MPC to be implemented via a PWL function evaluation without real-time optimization. The main drawback of this approach is dramatic incre...

This paper shows new convergence properties of constrained linear discrete time system with bounded disturbances under Model Predictive Control (MPC) law. The MPC control law is obtained using an affine disturbance feedback parametrization with an additional linear state feedback term. This parametrization has the same representative ability as some recent disturbance feedback parametrization, ...