14,146 research outputs found
Performance-oriented model learning for data-driven MPC design
Model Predictive Control (MPC) is an enabling technology in applications
requiring controlling physical processes in an optimized way under constraints
on inputs and outputs. However, in MPC closed-loop performance is pushed to the
limits only if the plant under control is accurately modeled; otherwise, robust
architectures need to be employed, at the price of reduced performance due to
worst-case conservative assumptions. In this paper, instead of adapting the
controller to handle uncertainty, we adapt the learning procedure so that the
prediction model is selected to provide the best closed-loop performance. More
specifically, we apply for the first time the above "identification for
control" rationale to hierarchical MPC using data-driven methods and Bayesian
optimization.Comment: Accepted for publication in the IEEE Control Systems Letters (L-CSS
Performance and safety of Bayesian model predictive control: Scalable model-based RL with guarantees
Despite the success of reinforcement learning (RL) in various research
fields, relatively few algorithms have been applied to industrial control
applications. The reason for this unexplored potential is partly related to the
significant required tuning effort, large numbers of required learning
episodes, i.e. experiments, and the limited availability of RL methods that can
address high dimensional and safety-critical dynamical systems with continuous
state and action spaces. By building on model predictive control (MPC)
concepts, we propose a cautious model-based reinforcement learning algorithm to
mitigate these limitations. While the underlying policy of the approach can be
efficiently implemented in the form of a standard MPC controller,
data-efficient learning is achieved through posterior sampling techniques. We
provide a rigorous performance analysis of the resulting `Bayesian MPC'
algorithm by establishing Lipschitz continuity of the corresponding future
reward function and bound the expected number of unsafe learning episodes using
an exact penalty soft-constrained MPC formulation. The efficiency and
scalability of the method are illustrated using a 100-dimensional server
cooling example and a nonlinear 10-dimensional drone example by comparing the
performance against nominal posterior MPC, which is commonly used for
data-driven control of constrained dynamical systems
Adaptive-smith predictor for controlling an automotive electronic throttle over network
The paper presents a control strategy for an automotive electronic throttle,
a device used to regulate the power produced by spark-ignition engines. Controlling
the electronic throttle body is a difficult task because the throttle accounts strong
nonlinearities. The difficulty increases when the control works through communication
networks subject to random delay. In this paper, we revisit the Smith-predictor
control, and show how to adapt it for controlling the electronic throttle body over a
delay-driven network. Experiments were carried out in a laboratory, and the corresponding
data indicate the benefits of our approach for applications.Peer ReviewedPostprint (published version
Data-driven adaptive model-based predictive control with application in wastewater systems
This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms
Robust predictive feedback control for constrained systems
A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed-loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model
- âŠ