1,704 research outputs found
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
Robust Multi-Criteria Optimal Fuzzy Control of Continuous-Time Nonlinear Systems
This paper presents a novel fuzzy control design of continuous-time nonlinear systems with multiple performance criteria. The purpose behind this work is to improve the traditional fuzzy controller performance to satisfy several performance criteria simultaneously to secure quadratic optimality with inherent stability property together with dissipativity type of disturbance reduction. The Takagi– Sugeno fuzzy model is used in our control system design. By solving the linear matrix inequality at each time step, the control solution can be found to satisfy the mixed performance criteria. The effectiveness of the proposed technique is demonstrated by simulation of the control of the inverted pendulum system
Dynamically Stable 3D Quadrupedal Walking with Multi-Domain Hybrid System Models and Virtual Constraint Controllers
Hybrid systems theory has become a powerful approach for designing feedback
controllers that achieve dynamically stable bipedal locomotion, both formally
and in practice. This paper presents an analytical framework 1) to address
multi-domain hybrid models of quadruped robots with high degrees of freedom,
and 2) to systematically design nonlinear controllers that asymptotically
stabilize periodic orbits of these sophisticated models. A family of
parameterized virtual constraint controllers is proposed for continuous-time
domains of quadruped locomotion to regulate holonomic and nonholonomic outputs.
The properties of the Poincare return map for the full-order and closed-loop
hybrid system are studied to investigate the asymptotic stabilization problem
of dynamic gaits. An iterative optimization algorithm involving linear and
bilinear matrix inequalities is then employed to choose stabilizing virtual
constraint parameters. The paper numerically evaluates the analytical results
on a simulation model of an advanced 3D quadruped robot, called GR Vision 60,
with 36 state variables and 12 control inputs. An optimal amble gait of the
robot is designed utilizing the FROST toolkit. The power of the analytical
framework is finally illustrated through designing a set of stabilizing virtual
constraint controllers with 180 controller parameters.Comment: American Control Conference 201
Stabilization of cascaded nonlinear systems under sampling and delays
Over the last decades, the methodologies of dynamical systems and control theory have been playing an increasingly relevant role in a lot of situations of practical interest. Though, a lot of theoretical problem still remain unsolved. Among all, the ones concerning stability and stabilization are of paramount importance. In order to stabilize a physical (or not) system, it is necessary to acquire and interpret heterogeneous information on its behavior in order to correctly intervene on it. In general, those information are not available through a continuous flow but are provided in a synchronous or asynchronous way. This issue has to be unavoidably taken into account for the design of the control action. In a very natural way, all those heterogeneities define an hybrid system characterized by both continuous and discrete dynamics. This thesis is contextualized in this framework and aimed at proposing new methodologies for the stabilization of sampled-data nonlinear systems with focus toward the stabilization of cascade dynamics. In doing so, we shall propose a small number of tools for constructing sampled-data feedback laws stabilizing the origin of sampled-data nonlinear systems admitting cascade interconnection representations. To this end, we shall investigate on the effect of sampling on the properties of the continuous-time system while enhancing design procedures requiring no extra assumptions over the sampled-data equivalent model. Finally, we shall show the way sampling positively affects nonlinear retarded dynamics affected by a fixed and known time-delay over the input signal by enforcing on the implicit cascade representation the sampling process induces onto the retarded system
Nonlinear and sampled data control with application to power systems
Sampled data systems have come into practical importance for a variety of reasons.
The earliest of these had primarily to do with economy of design. A more recent surge of interest
was due to increase utilization of digital computers as controllers in feedback systems. This thesis
contributes some control design for a class of nonlinear system exhibition linear output. The
solution of several nonlinear control problems required the cancellation of some intrinsic dynamics
(so-called zero dynamics) of the plant under feedback. It results that the so-dened control will
ensure stability in closed-loop if and only if the dynamics to cancel are stable. What if those
dynamics are unstable? Classical control strategies through inversion might solve the problem while
making the closed loop system unstable. This thesis aims to introduce a solution for such a problem.
The main idea behind our work is to stabilize the nonminimum phase system in continuous- time
and undersampling using zero dynamics concept. The overall work in this thesis is divided into
two parts. In Part I, we introduce a feedback control designs for the input-output stabilization
and the Disturbance Decoupling problems of Single Input Single Output nonlinear systems. A
case study is presented, to illustrate an engineering application of results. Part II illustrates the
results obtained based on the Articial Intelligent Systems in power system machines. We note
that even though the use of some of the AI techniques such as Fuzzy Logic and Neural Network
does not require the computation of the model of the application, but it will still suer from some
drawbacks especially regarding the implementation in practical applications. An alternative used
approach is to use control techniques such as PID in the approximated linear model. This design
is very well known to be used, but it does not take into account the non-linearity of the model. In
fact, it seems that control design that is based on nonlinear control provide better performances
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