5,532 research outputs found
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International
Conference on Robotics and Automation (ICRA
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
On-line multiobjective automatic control system generation by evolutionary algorithms
Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics
Multi-Parametric Extremum Seeking-based Auto-Tuning for Robust Input-Output Linearization Control
We study in this paper the problem of iterative feedback gains tuning for a
class of nonlinear systems. We consider Input-Output linearizable nonlinear
systems with additive uncertainties. We first design a nominal Input-Output
linearization-based controller that ensures global uniform boundedness of the
output tracking error dynamics. Then, we complement the robust controller with
a model-free multi-parametric extremum seeking (MES) control to iteratively
auto-tune the feedback gains. We analyze the stability of the whole controller,
i.e. robust nonlinear controller plus model-free learning algorithm. We use
numerical tests to demonstrate the performance of this method on a mechatronics
example.Comment: To appear at the IEEE CDC 201
CAutoCSD-evolutionary search and optimisation enabled computer automated control system design
This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytically and practically, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process
Learning for Advanced Motion Control
Iterative Learning Control (ILC) can achieve perfect tracking performance for
mechatronic systems. The aim of this paper is to present an ILC design tutorial
for industrial mechatronic systems. First, a preliminary analysis reveals the
potential performance improvement of ILC prior to its actual implementation.
Second, a frequency domain approach is presented, where fast learning is
achieved through noncausal model inversion, and safe and robust learning is
achieved by employing a contraction mapping theorem in conjunction with
nonparametric frequency response functions. The approach is demonstrated on a
desktop printer. Finally, a detailed analysis of industrial motion systems
leads to several shortcomings that obstruct the widespread implementation of
ILC algorithms. An overview of recently developed algorithms, including
extensions using machine learning algorithms, is outlined that are aimed to
facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced
Motion Control, 202
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