1,409 research outputs found
Finite-time behavior of inner systems
In this paper, we investigate how nonminimum phase characteristics of a dynamical system affect its controllability and tracking properties. For the class of linear time-invariant dynamical systems, these characteristics are determined by transmission zeros of the inner factor of the system transfer function. The relation between nonminimum phase zeros and Hankel singular values of inner systems is studied and it is shown how the singular value structure of a suitably defined operator provides relevant insight about system invertibility and achievable tracking performance. The results are used to solve various tracking problems both on finite as well as on infinite time horizons. A typical receding horizon control scheme is considered and new conditions are derived to guarantee stabilizability of a receding horizon controller
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
Optimal ripple-free deadbeat controllers
A ripple-free deadbeat controller for a system exists if and only if there are no transmission zeros coinciding with the poles of the reference signal. Approaches to this problem often use the Diophantine equation solution. However, solutions provided by the Diophantine equation often exhibit extremely bad transient responses. This approach gives a new affine parametrization of solutions of the Diophantine equation. Based on this parametrization, LMI conditions are used to provide optimal or constrained controllers for design quantities such as overshoot, undershoot, control amplitude, 'slew rate' as well as for norm bounds such as l1, l2 and l infinity
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
Stability Analysis of Piecewise Affine Systems with Multi-model Model Predictive Control
Constrained model predictive control (MPC) is a widely used control strategy,
which employs moving horizon-based on-line optimisation to compute the optimum
path of the manipulated variables. Nonlinear MPC can utilize detailed models
but it is computationally expensive; on the other hand linear MPC may not be
adequate. Piecewise affine (PWA) models can describe the underlying nonlinear
dynamics more accurately, therefore they can provide a viable trade-off through
their use in multi-model linear MPC configurations, which avoid integer
programming. However, such schemes may introduce uncertainty affecting the
closed loop stability. In this work, we propose an input to output stability
analysis for closed loop systems, consisting of PWA models, where an observer
and multi-model linear MPC are applied together, under unstructured
uncertainty. Integral quadratic constraints (IQCs) are employed to assess the
robustness of MPC under uncertainty. We create a model pool, by performing
linearisation on selected transient points. All the possible uncertainties and
nonlinearities (including the controller) can be introduced in the framework,
assuming that they admit the appropriate IQCs, whilst the dissipation
inequality can provide necessary conditions incorporating IQCs. We demonstrate
the existence of static multipliers, which can reduce the conservatism of the
stability analysis significantly. The proposed methodology is demonstrated
through two engineering case studies.Comment: 28 pages 9 figure
Dynamic operability assessment : a mathematical programming approach based on Q-parametrization
Bibliography: pages 197-208.The ability of a process plant to guarantee high product quality, in terms of low variability, is emerging as a defining feature when distinguishing between alternative suppliers. The extent to which this can be achieved is termed a plant's dynamic operability and is a function of both the plant design and the control system design. In the limit, however, the closedloop performance is determined by the properties inherent in the plant. This realization of the interrelationship between a plant design and its achievable closed-loop performance has motivated research toward systematic techniques for screening inherently inferior designs. Pioneering research in the early 1980's identified right-half-plane transmission zeros, time delays, input constraints and model uncertainty as factors that limit the achievable closedloop performance of a process. Quantifying the performance-limiting effect of combinations of these factors has proven to be a challenging problem, as reflected in the literature. It is the aim of this thesis to develop a systematic procedure for dynamic operability assessment in the presence of combinations of performance-limiting factors. The approach adopted in this thesis is based on the Q-parametrization of stabilizing linear feedback controllers and involves posing dynamic operability assessment as a mathematical programming problet? In the proposed formulation, a convex objective function, reflecting a measure of closed-loop performance, is optimized over all stable Q, subject. to a set of constraints on the closed-loop behavior, which for many specifications of interest is convex. A discrete-time formulation is chosen so as to allow for the convenient hand.ling of time delays and time-domain constraints. An important feature of the approach is that, due to the convexity, global optimality is guaranteed. Furthermore, the fact that Q parametrizes all stabilizing linear feedback controllers implies that the performance at the optimum represents the best possible performance for any such controller. The results are thus not biased by controller type or tuning, apart from the requirement that the controller be linear
Two-channel decentralized integral action controller design
Cataloged from PDF version of article.We propose a systematic controller design method that provides
integral-action in linear time-invariant two-channel decentralized
control systems. Each channel of the plant is single-input–single-output,
with any number of poles at the origin but no other poles in the instability
region. An explicit parametrization of all decentralized stabilizing
controllers incorporating the integral-action requirement is provided
for this special case of plants. The main result is a design methodology
that constructs simple low-order controllers in the cascaded form of
proportional-integral and first-order blocks
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