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In this paper two predictive controllers for non-linear systems are presented. Both methods — ELPC and MELPC — are based on a local linearisation of the process at each sampling instant, thus, a priori, should perform better than the controllers designed in terms of a single linearisation about an operating point. On the other hand, their linear formulation leads to computation times much shorter...
The purpose of this paper is to present a new methodology to control current-fed induction motor. This approach results from a combination of the differential flatness properties and the monovariable Generalized Predictive Control with Multiple Reference Model (GPC/MRM) algorithm. The chosen outputs: the rotor speed and the square of the rotor flux modulus, with respect to the motor modeling, lead...
In this article it is investigated how, alternative to e.g. the standard pulse or blocking mechanisms, other input parametrizations can be used in model predictive control to improve the trade-off between performance and complexity. An efficient parametrization is obtained using the observation that the class of all solutions to a finite or infinite horizon LQ control problem can be parametrized with...
The asymptotic behaviour of the optimal cost for problems with increasing time horizon is studied. The dynamics and costs are general nonlinear, possibly with state and control constraints. Apart from basic consistency assumptions, a uniform detectability hypothesis provides the setup for the analysis, which is based on direct evaluations of bounds for costs and trajectories. This investigation is...
Periodic deformations of organs which are due to respiratory movements may be critical disturbances for surgeons manipulating robotic control systems during laparoscopic interventions or tele-surgery. Indeed, the surgeon has to manually compensate for these motions if accurate gestures are needed, like, e.g., during suturing. This paper proposes a repetitive model predictive control scheme for driving...
In this work a fixed one step ahead predictive control has been applied to control angular position and linear velocity of an electrical wheelchair. The purpose of such as controller is to assist wheelchair users providing an easier and safer navigation. In addition the importance of this type of control is its effect by operation of the feedforward path to follow in the future, on the other hand,...
In this paper the optimal nonlinear predictive control in cascaded structure design of induction motors is presented which provides global asymptotic tracking of smooth speed and flux trajectories. The controller is based on a finite horizon continuous time minimization of nonlinear predicted tracking errors. With full state measurement assumption, the robustness properties with respect to electrical...
In this paper, the problem of overcoming network nondeterminism by means of predictive approaches for nonlinear continuous time systems is addressed. The idea is to use a model of the plant at the controller side to compensate delays and information loss. An event-based predictive control algorithm able to guarantee closed loop nominal stability is presented. Asynchronous controllers can be used to...
This paper presents a computationally attractive nonlinear model predictive control approach for the class of continuous time Lure systems. The control law is obtained via the repeated solution of an efficient to solve convex optimization problem based on linear matrix inequalities (LMIs). Closed-loop stability and satisfaction of input and state constraints are guaranteed by the feasibility of the...
This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and...
Controlled invariant terminal constraints fail to enforce strong feasibility in a rich class of MPC problems, for example when employing move-blocking. In previous work, controlled invariant feasibility was proposed for the purpose of formulating strongly feasible move-blocking MPC problems. In this paper, first, a maximum controlled invariant feasible set condition is derived. Based on this condition...
In this paper a predictive navigation system for mobile robots is presented. The system deals with unexpected moving obstacles which future positions over a prediction horizon are estimated with a Kalman filter approach. Genetic algorithms have been used for real time optimization problem involved in the model based predictive control problem. Experimental results obtained when applying the controller...
A communication-based distributed model predictive control scheme for a set of dynamically decoupled autonomous systems (agents) is proposed. The individual dynamics are described by discrete-time linear systems. Locally, each agent obeys a model predictive controller which minimizes a given local cost-function. Coupling between the agents occurs only through state-constraints. It is assumed that...
A trajectory tracking strategy is presented for processes described by a parabolic nonlinear distributed parameter model. A model-based predictive control approach, combined with an internal model control structure and a state estimation method is extended to this kind of process. On-line requirements such as computational time and constraint satisfaction are outlined and discussed. This method is...
High performance interior permanent magnet synchronous machines show nonlinear magnetics due to saturation and cross-coupling. Nonlinear differential equations describe these phenomena and make feedback control challenging. This paper presents a predictive control method to precisely control the dynamics of these machines. Four real-time capable strategies to online identify transient trajectories...
This work aims to design an optimal dynamic controller to stabilize the walk of a biped robot even in the presence of input and output constraints. In a first time, the robot's trajectory is generated via the Zero Moment Point criterion based on the resolution of a convex optimization problem with Linear Matrix Inequalities. In a second time, the tracking of a reference trajectory is insured by the...
This paper focuses on the design of a Laguerre function based Model Predictive Control (MPC) for simultaneous tracking and vibration control of a flexible joint manipulator. A single link flexible joint manipulator is considered and the dynamic model of the system is derived using Lagrangian approach. The singular perturbation approach is exploited to obtain the two time-scale decomposition of this...
Hydraulic excavators play an important role for various construction tasks. But due to increasing costs of fossil fuels and an increasing environmental awareness, there is high demand for more efficient machinery. During the development of the necessary technologies, simulations based on an excavator model and measured driving cycles allow for an early assessment of the expected machine performance...
The main enthusiasm of the use of MPC in this study relies on its ability in including state and control constraints that naturally comes up in practical problems. At the same time, nonlinearity of constraints compels the numerical solution of optimization problem. Therefore, a number of intelligent algorithms to overcome nonlinear optimization of the cost function is reviewed, however, their time...
This paper presents a framework for dynamic humanoid locomotion on uneven terrain using a novel time-varying extension to the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic CoM height trajectories during stepping. The proposed planning algorithm computes admissible DCM reference trajectories given desired ZMP plans for single and double...
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