The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, a quadcopter is controlled by a nonlinear model predictive controller, NMPC, for trajectory tracking in presence of an external perturbation. The nonlinear model predictive control was basically confined to slow processes. Applications to fast processes such as robots are rare because the time for the solution may exceed the sampling period. Metaheuristics have been used for solving...
This paper is concerned with a computationally efficient suboptimal nonlinear predictive control algorithm. The nonlinear model of the plant is used to obtain a local linearisation and to calculate, by means of an iterative procedure, the nonlinear response and future control moves. In comparison with fully-fledged nonlinear algorithms, which hinge on non-convex optimisation, the presented approach...
A tracking guidance scheme based on nonlinear model predictive control is proposed for low-thrust transfer trajectory. According to the optimal nominal transfer trajectory, the NMPC is used to design the predictive model of perturbed orbit. Performance index of receding optimization is established, which can indicate the difference between the nominal trajectory and prediction trajectory. Taking the...
A collision-free formation flight controller for unmanned aerial vehicle (UAV) is designed in the framework of nonlinear model predictive control (MPC). It can consider control input saturation and state constraints explicitly. Formation configuration is determined based on virtual reference point method, which has no error propagation in the formation. The formation flight controller is designed...
In this paper a novel method called Sampling-Based Model Predictive Control (SBMPC) is proposed as an efficient MPC algorithm to generate control inputs and system trajectories. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on...
A Model predictive control (MPC) calculates a control input for tracking the system output to a reference trajectory which is an ideal trajectory for the system output to converge on a desired value. In MPC, the parameters of controlled systems are needed for calculating of the control input, and these parameters are treated as well-known and invariable parameters. However, the parameters of system...
This paper presents a novel mobile vehicle navigation algorithm based on the stability analysis of the model predictive control approach. The energy-shaping technique is performed with the navigation function to obtain a new virtual vehicle model that generates candidate feasible trajectories for the motion planner. Stability of the nonlinear model predictive control system is obtained by the passivity...
This paper reports a surge control strategy for centrifugal compressor using nonlinear model predictive control based on Lease-Squared Support Vector Machine(LS-SVM) in order to increase efficiency of centrifugal compressor. The MISO nonlinear predictive models of compressor's discharge pressure and mass flow are developed by LS-SVM. In order to avoid surge, the conditions of anti-surge are chosen...
A practical nonlinear model predictive control (MPC) approach is presented for fast response systems. To this end, a nonlinear model of the system is first developed and linearised along a desired trajectory at each instant to obtain as many linear models as prediction horizon. The state variables are observed using an unscented Kalman filter (UKF). The MPC formulation is thus resulted in a linear...
We propose a model predictive control approach to path-following problems of constrained nonlinear systems. We directly consider input and state constraints. Furthermore, we introduce an extended corridor path-following problem, which allows to add spatial degrees of freedom to the path formulation.We give sufficient stability conditions for predictive solutions to 1d and corridor path-following problems...
Thanks to their cheap startup costs, flexibility, and standard infrastructure re-usability, Networked Control Systems have gained the attention of both the control community and the industry. Unfortunately, the presence of communication networks might introduce nondeterminism due to (random) delays and/or (unpredictable) information losses. In this paper, an event-based model predictive control approach...
Previous efforts to control cellular differentiation have been largely experimental. Although some mathematical models for this process exist, rarely has a quantitative approach been employed to design experiments that predictably direct the cell fate. As an initial step towards this aim, a control strategy for sustaining a desired constant level of differentiated human promyelocytic leukemia (HL60)...
Model predictive control (MPC) an optimization-based approach that decides a control input by the optimal computation as the system output tracks the reference trajectory which is the ideal trajectory while the system output converges on the desired value. In this paper, a tracking controller for the two-link manipulator on the horizontal space via nonlinear model predictive control (NMPC) is proposed...
This paper considers the problem of steering and coordinating a group of omnidirectional mobile robots along given paths. Two subproblems, i.e., a path following subproblem and a motion coordination subproblem are solved by using distributed nonlinear model predictive control (NMPC) whose cost function is coupled with neighbors. The distinct features of NMPC are that constraints can be explicitly...
By substituting reference trajectory for state in prediction horizon, and using sequential one-step predictions with stair-like control strategy instead of a multi-step prediction, an efficient model predictive control algorithm for affine nonlinear systems was proposed. This algorithm resulted in an analytic model predictive control law with slight computational load. Robustness to model mismatch...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.