1,720,969 research outputs found
Adaptive model predictive control for linear time varying MIMO systems
No full textA robust, adaptive Model Predictive Control (MPC) approach for asymptotically stable, constrained linear time-varying (LTV) systems with multiple inputs and outputs is proposed. The approach consists of two-steps, carried out on-line with a receding horizon strategy. In the first one, a real-time Set Membership identification algorithm exploits the measured input-output data and the available prior knowledge to build and refine a set of admissible models of the plant (Feasible Parameter Set, FPS). This set is guaranteed to contain also the true system dynamics under the considered working assumptions. In the second step, a robust finite-horizon optimal control problem is formulated and solved. The variation of system dynamics is taken into account by inflating the FPS over the prediction horizon, according to worst-case bounds, assumed a priori, on the parameters' rate of change. The resulting optimal control sequence guarantees that the outputs of all possible plants inside the FPS satisfy the operational constraints, also considering all possible future parameter changes. The main theoretical properties of the proposed approach are demonstrated and the method is showcased in numerical simulations, highlighting the fundamental improvement over previous approaches not designed for LTV systems. (C) 2019 Elsevier Ltd. All rights reserved.IPHY
On-line direct control design for nonlinear systems
Full text linkAn approach to design a feedback controller for nonlinear systems directly from experimental data is presented. Improving over a recently proposed technique, which employs exclusively a batch of experimental data collected in a preliminary experiment, here the control law is updated and rened during real-time operation, hence enabling an on-line learning capability. The theoretical properties of the described approach, in particular closed-loop stability and tracking accuracy, are discussed. Finally, the experimental results obtained with a water tank laboratory setup are presented
Adaptive Model Predictive Control for Constrained Time Variying Systems
No full textAn approach to design feedback controllers for discrete-time, uncertain, linear time-varying systems subject to constraints is proposed. Building on previous contributions in the framework of time-invariant systems, in each sampling period a two-step procedure is carried out. In the first step, a set of linear models that are consistent with past input-output data and prior assumptions is built and refined. This set is guaranteed to contain also the true system dynamics if the considered working assumptions are valid. The time-varying nature of the plant is captured by assuming known bounds on the rate of change of the model parameters in time. In the second step, a robust finite-horizon optimal control problem is formulated and solved. The resulting optimal control sequence guarantees that the outputs of all possible plants inside the model set satisfy the operational constraints. The approach is showcased in numerical simulations on a three-tank system
Data-driven control of nonlinear systems: An on-line direct approach
Full text linkA data-driven method to design reference tracking controllers for nonlinear systems is presented. The technique does not derive explicitly a model of the system, rather it delivers directly a time-varying state-feedback controller by combining an on-line and an off-line scheme. Like in other on-line algorithms, the measurements collected in closed-loop operation are exploited to modify the controller in order to improve the tracking performance over time. At the same time, a predictable closed-loop behavior is guaranteed by making use of a batch of available data, which is a feature of off-line algorithms. The feedback controller is parameterized with kernel functions and the design approach exploits results in set membership identification and learning by projections. Under the assumptions of Lipschitz continuity and stabilizability of the system's dynamics, it is shown that if the initial batch of data is informative enough, then the resulting closed-loop system is guaranteed to be finite gain stable. In addition to the main theoretical properties of the approach, the design algorithm is demonstrated experimentally on a water tank system
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