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We consider robust predictive control of continuous-time, constrained, nonlinear systems by means of a discrete-time control scheme. The key idea is to discretize the system first and to explicitly bound the arising discretization error. Taking this error into account a robustly stabilizing predictive controller is proposed that guarantees constraint satisfaction at the sampling instances and via...
In this paper, a method of robust model predictive control for constrained linear discrete-time systems with bounded disturbances is presented. The approach is based on modifying the concept of discrete-time integral sliding mode control into an optimal constrained control problem. By introducing an additional sliding mode control term into the state feedback law, the closed-loop system can be maintained...
In this paper, a novel model predictive control (MPC) scheme is presented for linear stochastic systems with probabilistic constraints. Instead of the prediction of the behavior of the original linear stochastic system, the behavior of a corresponding nominal linear system is predicted. Thus, the optimization problem that is solved online has the same computational burden as the ones of standard deterministic...
In this paper, a novel controller based on the combination of differential flatness property and model predictive technology for the generator excitation system is proposed. Such controller will maintain the terminal voltage of the generator within physical limits by better utilizing the existing excitation control. This eliminates the need for adding new fast storage. One preliminary real-world system...
The paper proposes a predictive control law for orbital rendezvous hovering phases. The proposed algorithm provides an impulsive control that steer the vehicle to a set of the periodic relative orbits enclosed in a hovering zone. The control law is computed by determining a point lying in the intersection between a semi-algebraic set and a hyperplane using an alternating projections algorithm. The...
Feasibility of the optimization problem in a predictive controller may be compromised in the event of a fault. One alternative to recover feasibility is to relax the constraints. The terminal constraints seem like suitable candidates for relaxation, as they are often artificially introduced to ensure recursive feasibility. As an advantage, the physical constraints over the states and controls can...
In this paper, formation of a group of multiple cooperative unmanned aerial vehicles (UAVs) in a desired geometrical pattern while tracking an aerial target is implemented using decentralized Learning Based Model Predictive Control (LBMPC). The LBMPC is a new control technique that combines statistical learning along with control engineering providing guarantees on safety, robustness and convergence...
This paper presents a method to generate and follow a smallest weighted path in 3D by quadcopter. A smallest weighted path is obtained using Dijkstra's algorithm based on terrain information. To follow the generated path we use a Model predictive control that takes constraints on jerks, position and angular acceleration on the quadcopter. Simulation results are presented to validate our approach.
In this work, we propose a novel robust dynamic controller in order to stabilize a walking biped robot with input and output constraints. Firstly, the trajectory of the robot is generated via the Zero Moment Point method based on the resolution of a convex optimization problem with Linear Matrix Inequalities. Then, the tracking of a referential trajectory is insured by the design of an optimal dynamic...
The objective of this paper is to control the angular speed in a model of a DC motor using different control strategies like Model Predictive Control and Linear Quadratic Regulator for comparison purpose. Model Predictive Control approach provides online & offline computation of the optimization law by Multi Parametric Quadratic Programming. The controllers are designed based on the optimal control...
This paper focus on resolving the trajectory tracking problem of two wheeled mobile robot. We begin by presenting the kinematic model of the robot which is the base of the control law than we present a PI controller and a model predictive controller to solve the problem of trajectory tracking. We performed a comparison between the performances of the classical PI controller and the predictive controller...
This paper considers pick-and-place tasks using aerial vehicles equipped with manipulators. The main focus is on the development and experimental validation of a nonlinear model-predictive control methodology to exploit the multi-body system dynamics and achieve optimized performance. At the core of the approach lies a sequential Newton method for unconstrained optimal control and a high-frequency...
Walking capability composed of stability and efficiency is one of the most important issues in the field of humanoid robots. An effective swing of the arms is expected to enhance the walking capability under the constraints from the limited body. We propose an arm-swing method to enhance the stability and efficiency by selecting optimal arm-swing strategy depending on the walking conditions. In this...
A data-driven predictive control methodology based on reduced Hankel matrix is proposed in this paper. Undersome assumptions, the properties of a system can be simply and visually behaved by the construction of input-output Hankel matrix. The row size of the Hankel matrix depends on the request of system excitation, which also determines the prediction and control horizons. In order to show the required...
In this paper we have applied two types of control on two coupled water tanks, where the dynamic model is nonlinear system. The first control is the sliding mode with linear surface, the advantages of this type of control is the discontinuity behavior which can be applied to variable structure systems (VSS) and its good robustness against the injected disturbances, and/or the noise, and finally unaffected...
This paper presents the design of predictive control with state constraints for the swing-up and stabilizing control of a cart with an inverted pendulum system where the state constraints are proposed on angle and velocity of pendulum, position and velocity of cart, Since the system has strong nonlinearity and inherent instability, a step of linearization is necessary to extract linear state space...
This paper presents a model predictive approach to achieve power demand reduction in dynamic positioning operations. The MPC supervision, together with a relaxed operational constraint, is able to reduce the overall power demand in terms of mean, peak and frequency components.
Model predictive control is a well established strategy for dealing with saturating actuator problems. The essential feature of the method is a receding horizon quadratic optimal control problem which is solved subject to input constraints. On the other hand, anti-windup methods are ad-hoc procedures which achieve input saturation in an instantaneous fashion. Both methods are known to perform well...
A novel formulation of linear model-based predictive control is presented. Predicted flat output trajectories and stabilization around these trajectories constitute the main components of our predictive control approach. A simple dc motor and a cement mill model illustrate the principal features of the method.
The joint presence of constraints and disturbances can drive a predictive controller to infeasibility and instability. Here it is shown how robustness against persistent bounded disturbances can be provided by adding to the nominal predictive controller a suitable invariance constraint. The proposed technique allows to significantly enlarge the feasibility region with respect to techniques based on...
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