86,100 research outputs found
Gaussian process model based predictive control
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark
Predictive pole-placement control with linear models
The predictive pole-placement control method introduced in this paper embeds the classical pole-placement state feedback design into a quadratic optimisation-based model-predictive formulation. This provides an alternative to model-predictive controllers which are based on linear–quadratic control. The theoretical properties of the controller in a linear continuous-time setting are presented and a number of illustrative examples are given. These results provide the foundation for novel linear and nonlinear constrained predictive control methods based on continuous-time models
A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of O(1/k^2), where
k is the iteration number. Major challenges in the control of the HPV include a
nonlinear and large-scale model, nonsmoothness in the power-production
functions, and a globally coupled cost function that prevents distributed
schemes to be applied directly. We propose a linearization and approximation
approach that accommodates the proposed the DMPC framework and provides very
similar performance compared to a centralized solution in simulations. The
provided numerical studies also suggest that for the sparsely interconnected
system at hand, the distributed algorithm we propose is faster than a
centralized state-of-the-art solver such as CPLEX
On output feedback nonlinear model predictive control using high gain observers for a class of systems
In recent years, nonlinear model predictive control schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in connection to nonlinear predictive control. Most of the existing approaches for output feedback nonlinear model predictive control do only guarantee local stability. Here we consider the combination of stabilizing instantaneous NMPC schemes with high gain observers. For a special MIMO system class we show that the closed loop is asymptotically stable, and that the output feedback NMPC scheme recovers the performance of the state feedback in the sense that the region of attraction and the trajectories of the state feedback scheme are recovered for a high gain observer with large enough gain and thus leading to semi-global/non-local results
Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation
This paper presents a control architecture in which a direct adaptive control
technique is used within the model predictive control framework, using the
concurrent learning based approach, to compensate for model uncertainties. At
each time step, the control sequences and the parameter estimates are both used
as the optimization arguments, thereby undermining the need for switching
between the learning phase and the control phase, as is the case with
hybrid-direct-indirect control architectures. The state derivatives are
approximated using pseudospectral methods, which are vastly used for numerical
optimal control problems. Theoretical results and numerical simulation examples
are used to establish the effectiveness of the architecture.Comment: 21 pages, 13 figure
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