In this study, backstepping control integrated with lyapunovbased model predictive control bsmpc is proposed for nonlinear systems in a strictfeedback form. Lyapunovbased model predictive control of nonlinear. Lyapunovbased model predictive control of a puc7 grid. Christofidesmultirate lyapunovbased distributed model predictive control of. The proposed economic mpc is designed via lyapunov based techniques and has two different operation modes. More importantly, the lmpc controller inherits the stability property of the. Backstepping control integrated with lyapunovbased model. Distributed lyapunovbased model predictive control for. For stability, the terminalconstraintbased mpc schemes are required to bring.
Mpc can exploit efficient algorithms such as linear program ming tools to solve. Distributed lyapunovbased model predictive control for collision avoidance of multiagent formation abstract. Lyapunovbased hybrid model predictive control for energy. A cost function of the proposed model predictive controller is designed from the system stability point of view, inspired by the lyapunov control theory.
A lyapunov function based model predictive control algorithm is used to track the maximum power point of the pv system. Lyapunovbased model predictive control for dynamic. In our algorithm, we learn the parameters of the network, v netx, and a safe level, l s. Lyapunov based design and model predictive control for the simulation of free and controlled. Among different mpc formulations, a lyapunovbased model predictive control mhaskar et al. In this study, model predictive control mpc and inverse optimal control ioc. Alternative strategies for designing stabilizing model predictive. The proposed controller eliminates the need for gains that are associated with cost function coefficients, as seen in classical model predictive control based controllers. Distributed lyapunovbased mpc eindhoven university of. Model predictive control with control lyapunov function. Lyapunovbased model predictive control of stochastic.
In this paper, we propose a control lyapunovbarrier functionbased model predictive control clbfmpc method for solving the problem of stabilization of nonlinear systems with input constraint satisfaction and guaranteed safety for all times. In this paper, an approach to low complexity distributed mpc of linear. Trajectory tracking control of an autonomous underwater. Trajectory tracking control of an autonomous underwater vehicle using lyapunov based model predictive control article in ieee transactions on industrial electronics pp99.
Ulices santa cruz leal university of california, irvine. This has motivated significant research work on applications of this control design to nonlinear processes. Equation 7 is solved using the ipopt software package 19 with. Low complexity distributed model predictive control by using. Neural lyapunov model predictive control where v netx is a lipschitz feedforward network that produces a n v n xmatrix.
A lyapunov function based model predictive control for. Control lyapunovbarrier functionbased model predictive control of nonlinear systems abstract. Lyapunovbased stochastic nonlinear model predictive control. A new idea to construct stabilizing model predictive control is studied for a constrained system based on the adaptation of an existing stabilizing controller with a control lyapunov function. A stochastic lyapunovbased controller is first utilized to characterize a region of the. The main contribution of the proposed technique is the assurance of the closedloop stability and recursive feasibility, by a novel approach focused on mld models, using ellipsoidal terminal constraints and the lyapunov decreasing condition. View ulices santa cruz leal s profile on linkedin, the worlds largest professional community. The lyapunovbased controllers define a general class of feedback control laws which result in the closedloop system achieving negative definiteness of the drift of the lyapunov function derivative over the set. This study addresses the problem of distributed formation control for a multiagent system with collision avoidance between agents and with obstacles, in the presence of various constraints. Active and reactive power flow control of gridtied threephase inverter is achieved for pv generations system.
Lyapunovbased stochastic nonlinear model predictive. Abstractstochastic uncertainties in complex dynamical systems lead to variability of system states, which can in turn degrade the closedloop performance. Control solutions based on mpc are particularly powerful due to their. The proposed controller consists of a lyapunovbased hybrid model predictive control based on mixed logical dynamical mld framework. To mitigate the impact of cyberattacks in chemical processes, this work integrates a neural network nn based detection method and a lyapunov based model predictive controller for a class of. Safeness indexbased economic model predictive control of. Mpc algorithm can be implemented by solving a single linear program in each. Control lyapunovbarrier functionbased model predictive. The controller synthesis method used in this work is lyapunovbased model.
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