53,398 research outputs found
Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants
Modern control is implemented with digital microcontrollers, embedded within
a dynamical plant that represents physical components. We present a new
algorithm based on counter-example guided inductive synthesis that automates
the design of digital controllers that are correct by construction. The
synthesis result is sound with respect to the complete range of approximations,
including time discretization, quantization effects, and finite-precision
arithmetic and its rounding errors. We have implemented our new algorithm in a
tool called DSSynth, and are able to automatically generate stable controllers
for a set of intricate plant models taken from the literature within minutes.Comment: 10 page
Crone control of a nonlinear hydraulic actuator
The CRONE control (fractional robust control) of a hydraulic actuator whose dynamic model is nonlinear is presented. An input-output linearization under diffeomorphism and feedback is first achieved for the nominal plant. The relevance of this linearization when the parameters of the plant vary is then analyzed using the Volterra input-output representation in the frequency domain. CRONE control based on complex fractional differentiation is finally applied to control the velocity of the input-output linearized model when parametric variations occur
'Constant in gain Lead in phase' element - Application in precision motion control
This work presents a novel 'Constant in gain Lead in phase' (CgLp) element
using nonlinear reset technique. PID is the industrial workhorse even to this
day in high-tech precision positioning applications. However, Bode's gain phase
relationship and waterbed effect fundamentally limit performance of PID and
other linear controllers. This paper presents CgLp as a controlled nonlinear
element which can be introduced within the framework of PID allowing for wide
applicability and overcoming linear control limitations. Design of CgLp with
generalized first order reset element (GFORE) and generalized second order
reset element (GSORE) (introduced in this work) is presented using describing
function analysis. A more detailed analysis of reset elements in frequency
domain compared to existing literature is first carried out for this purpose.
Finally, CgLp is integrated with PID and tested on one of the DOFs of a planar
precision positioning stage. Performance improvement is shown in terms of
tracking, steady-state precision and bandwidth
Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models
Matlab/Simulink is a development and simulation language that is widely used
by the Cyber-Physical System (CPS) industry to model dynamical systems. There
are two mainstream approaches to verify CPS Simulink models: model testing that
attempts to identify failures in models by executing them for a number of
sampled test inputs, and model checking that attempts to exhaustively check the
correctness of models against some given formal properties. In this paper, we
present an industrial Simulink model benchmark, provide a categorization of
different model types in the benchmark, describe the recurring logical patterns
in the model requirements, and discuss the results of applying model checking
and model testing approaches to identify requirements violations in the
benchmarked models. Based on the results, we discuss the strengths and
weaknesses of model testing and model checking. Our results further suggest
that model checking and model testing are complementary and by combining them,
we can significantly enhance the capabilities of each of these approaches
individually. We conclude by providing guidelines as to how the two approaches
can be best applied together.Comment: 10 pages + 2 page reference
Beyond the Waterbed Effect: Development of Fractional Order CRONE Control with Non-Linear Reset
In this paper a novel reset control synthesis method is proposed: CRONE reset
control, combining a robust fractional CRONE controller with non-linear reset
control to overcome waterbed effect. In CRONE control, robustness is achieved
by creation of constant phase behaviour around bandwidth with the use of
fractional operators, also allowing more freedom in shaping the open-loop
frequency response. However, being a linear controller it suffers from the
inevitable trade-off between robustness and performance as a result of the
waterbed effect. Here reset control is introduced in the CRONE design to
overcome the fundamental limitations. In the new controller design, reset phase
advantage is approximated using describing function analysis and used to
achieve better open-loop shape. Sufficient quadratic stability conditions are
shown for the designed CRONE reset controllers and the control design is
validated on a Lorentz-actuated nanometre precision stage. It is shown that for
similar phase margin, better performance in terms of reference-tracking and
noise attenuation can be achieved.Comment: American Control Conference 201
Performance-based control system design automation via evolutionary computing
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD)
automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient
evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional
designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’
controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of
a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on
a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing,
with good closed-loop performance and robustness in the presence of practical constraints and perturbations
System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation
It is known that the set of internally stabilizing controller
is non-convex, but it admits convex
characterizations using certain closed-loop maps: a classical result is the
{Youla parameterization}, and two recent notions are the {system-level
parameterization} (SLP) and the {input-output parameterization} (IOP). In this
paper, we address the existence of new convex parameterizations and discuss
potential tradeoffs of each parametrization in different scenarios. Our main
contributions are: 1) We first reveal that only four groups of stable
closed-loop transfer matrices are equivalent to internal stability: one of them
is used in the SLP, another one is used in the IOP, and the other two are new,
leading to two new convex parameterizations of . 2)
We then investigate the properties of these parameterizations after imposing
the finite impulse response (FIR) approximation, revealing that the IOP has the
best ability of approximating given FIR
constraints. 3) These four parameterizations require no \emph{a priori}
doubly-coprime factorization of the plant, but impose a set of equality
constraints. However, these equality constraints will never be satisfied
exactly in numerical computation. We prove that the IOP is numerically robust
for open-loop stable plants, in the sense that small mismatches in the equality
constraints do not compromise the closed-loop stability. The SLP is known to
enjoy numerical robustness in the state feedback case; here, we show that
numerical robustness of the four-block SLP controller requires case-by-case
analysis in the general output feedback case.Comment: 20 pages; 5 figures. Added extensions on numerial computation and
robustness of closed-loop parameterization
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
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