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Non‐linear model‐based predictive control for trajectory tracking and control effort minimization in a smartphone‐based quadrotor

100%

García L. , Rosero E.

tom Vol. 16, No. 4

13--18

In this paper, the design and implementation of a nonlinear model‐based predictive controller (NMPC) for predefined trajectory tracking and to minimize the control effort of a smartphone‐based quadrotor are developed. The optimal control actions are calculated in each iteration by means of an optimal control algorithm based on the non‐linear model of the quadrotor, considering some aerodynamic effects. Control algorithm implementation and simulation tests are executed on a smartphone using the CasADi framework. In addition, a technique for estimating the energy consumed based on control signals is presented. NMPC controller performance was compared with other works developed towards the con‐ trol of quadrotors, based on an H∞ controller and an LQI controller, and using three predefined trajectories, where the NMPC average tracking error was around 50% lower, and average estimated power and energy consumption slightly higher, with respect to the H∞ and LQI controllers.

Adaptive Predictive Controller Using Orthonormal Series Functions

86%

Oliveira G. H. C. , Amaral W. C. , Favier G.

tom Vol. 9, no 1

175-191

A constrained adaptive predictive control method that uses uncertain process modelling based on orthonormal series functions is considered. Such unstructured modelling is described as a weighted sum of orthonormal functions using approximate information about the time constant of the process. The orthonormal series functions model can thus be used to derive a j-step-ahead output prediction according to the constrained adaptive predictive control law. In relation to predictive controllers based on structured models, this approach presents the advantage of not requiring prior knowledge of the order or time delay, which decrease prediction errors and lead to a better closed loop performance when these parameters are not well known. Stability issues of the proposed control scheme are discussed and, finally, a simulation example is given to show the performance of the algorithm.

Specialized genetic operators in drinking water distribution systems control

72%

Cimiński A. , Duzinkiewicz K.

tom z. 169

35-42

Model-based predictive control (MPC) is an effective method for control of the large scale systems. The method relying on repeating applying the first element of the calculated control sequence to the system, based on the model of the system and available system output measurements. A time duration of control calculation is a crucial criterion for applying this method. In this paper effective algorithm of control the drinking water distribution system (DWDS) is presented. Algorithm is based on genetic algorithm (GA), specialized genetic operators (SPO) and simulator Epanet. To improve the GA convergence, specialized genetic operators based on system operator knowledge of practical system control are proposed. Effectiveness of proposed specialized genetic operators on the example DWDS of the Chojnice city is presented.