913 research outputs found
Switched predictive control design for optimal wet-clutch engagement
Modeling of hydraulic clutch transmissions is far from straightforward due to their nonlinear hybrid dynamics, i.e. switching between three dynamic phases. In this paper we identify a local linear model only for the constrained first phase, based on which a predictive controller is used to track a suitable engagement signal. The robustness of this controller in the latter two phases is guaranteed by making the constraints inactive and pre-tuning the control parameters based on its closed loop formulation and applying robust stability theorem. This controller is then implemented in real-time on a wet-clutch test setup and is shown to achieve optimal engagement
Model Predictive Control for Offset-Free Reference Tracking
The paper deals with the offset-free reference tracking problem of the Model Predictive Control (MPC). That problem is considered for a class of the constant or occasionally changed constant reference signals. Proposed solution arises from a simple subtraction of the ARX model of two consecutive time steps. The solution is adapted to a state-space form and it corresponds to usual predictive control design without increase of the design complexity. The construction of the prediction equations and predictive controller structure is explained in the paper
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Improvement of powertrain mechatronic systems for lean automotive manufacturing
In recent years, the increasing severity of emission standards forced car manufacturers to integrate vehicle powertrains with additional mechatronic elements, consisting in sensors, executors and controlling elements interacting with each other. However, the introduction of the best available ecological devices goes hand in hand with the legislation and/or limitations in different regional markets. Thus, the designers adapt the mechatronic system to the target emission standards of the produced powertrain. The software embedded into the Engine Control Unit (ECU) is highly customized for the specific configurations: variability in mechatronic systems leads to the development of several software versions, lowering the efficiency of the design phase.
Therefore the employment of a standard for the communication among sensors, actuators and the ECU would allow the development of a unique software for different configurations; this would be beneficial from a manufacturing point of view, enabling the simplification of the design process. Obviously, the new software must still guarantee the proper level of feedbacks to the ECU to ensure the compliance with different emission standards and the proper engine behavior. The general software is adapted to the powertrain: according to the specific target emission standard, some control elements may not be necessary, and a part of the software may be easily removed.
In this paper, starting from a real case-study, a more general methodology is proposed for configurations characterized by different powertrain sets and manufacturing line constraints. The proposed technique allows to maintain the accuracy of the control system and improve process efficiency at the same time, ensuring lean production and lowering manufacturing costs. A set of mathematical techniques to improve software efficacy is also presented: the resulting benefits are enhanced by software standardization, because the design effort may be shared by the largest possible number of applications
Nonlinear model predictive low-level control
This dissertation focuses on the development, formalization, and systematic evaluation of a
novel nonlinear model predictive control (MPC) concept with derivative-free optimization.
Motivated by a real industrial application, namely the position control of a directional control
valve, this control concept enables straightforward implementation from scratch, robust
numerical optimization with a deterministic upper computation time bound, intuitive controller
design, and offers extensions to ensure recursive feasibility and asymptotic stability by
design. These beneficial controller properties result from combining adaptive input domain
discretization, extreme input move-blocking, and the integration with common stabilizing
terminal ingredients. The adaptive discretization of the input domain is translated into
time-varying finite control sets and ensures smooth and stabilizing closed-loop control. By
severely reducing the degrees of freedom in control to a single degree of freedom, the exhaustive
search algorithm qualifies as an ideal optimizer. Because of the exponentially increasing
combinatorial complexity, the novel control concept is suitable for systems with small input
dimensions, especially single-input systems, small- to mid-sized state dimensions, and simple
box-constraints. Mechatronic subsystems such as electromagnetic actuators represent this
special group of nonlinear systems and contribute significantly to the overall performance of
complex machinery.
A major part of this dissertation addresses the step-by-step implementation and realization
of the new control concept for numerical benchmark and real mechatronic systems. This dissertation
investigates and elaborates on the beneficial properties of the derivative-free MPC
approach and then narrows the scope of application. Since combinatorial optimization enables
the straightforward inclusion of a non-smooth exact penalty function, the new control
approach features a numerically robust real-time operation even when state constraint violations
occur. The real-time closed-loop control performance is evaluated using the example
of a directional control valve and a servomotor and shows promising results after manual
controller design.
Since the common theoretical closed-loop properties of MPC do not hold with input moveblocking,
this dissertation provides a new approach for general input move-blocked MPC
with arbitrary blocking patterns. The main idea is to integrate input move-blocking with
the framework of suboptimal MPC by defining the restrictive input parameterization as a
source of suboptimality. Finally, this dissertation extends the proposed derivative-free MPC
approach by stabilizing warm-starts according to the suboptimal MPC formulation. The
extended horizon scheme divides the receding horizon into two parts, where only the first
part of variable length is subject to extreme move-blocking. A stabilizing local controller
then completes the second part of the prediction. The approach involves a tailored and
straightforward combinatorial optimization algorithm that searches efficiently for suboptimal
horizon partitions while always reproducing the stabilizing warm-start control sequences in
the nominal setup
Modified Predictive Control for a Class of Electro-Hydraulic Actuator
Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the tuning control parameters. A modified predictive control method is proposed in this paper. The modified predictive method is based on the control matrix formulation combined with optimized move suppression coefficient. Poor dynamics and high nonlinearities are parts of the difficulties in the control of the Electro-Hydraulic Actuator (EHA) functions, which make the proposed matrix an attractive solution. The developed controller is designed based on simulation model of a position control EHA to reduce the overshoot of the system and to achieve better and smoother tracking. The performance of the designed controller achieved quick response and accurate behavior of the tracking compared to the previous study
Predictive pole-placement control with linear models
The predictive pole-placement control method introduced in this paper embeds the classical pole-placement state feedback design into a quadratic optimisation-based model-predictive formulation. This provides an alternative to model-predictive controllers which are based on linear–quadratic control. The theoretical properties of the controller in a linear continuous-time setting are presented and a number of illustrative examples are given. These results provide the foundation for novel linear and nonlinear constrained predictive control methods based on continuous-time models
REALISATION OF MPC ALGORITHM FOR QUANSER QUBE-SERVO
This paper offers an in-depth look into the design and implementation of a Model Predictive Control (MPC) algorithm for the QUANSER QUBE-SERVO system. The QUBE-SERVO is a sophisticated laboratory experimental setup consisting of a servo motor, an encoder, and a rotary module. This combination provides a robust platform for investigating and testing various control strategies. In particular, the central focus of this study is the usage of the MPC algorithm for controlling the position of the QUBE-SERVO’s rotary disc load module.
The MPC algorithm plays a pivotal role in this application by predicting the future behaviors of the system, and controlling the system by minimizing an objective function over a defined finite horizon. This makes it a versatile and effective tool for controlling complex systems.
One of the key challenges in practical control applications is maintaining system stability in the presence of disturbances and uncertainties. To this end, we propose a MPC algorithm designed specifically to stabilize the QUBE-SERVO under such conditions. The functionality of this algorithm is not limited to the QUBE-SERVO system alone, and can be extended to other control systems exhibiting similar characteristics.
The effectiveness of the proposed MPC algorithm is rigorously tested through simulation studies. These studies involve subjecting the QUBE-SERVO to various reference signals and disturbances. The results of the simulations provide strong evidence of the algorithm’s capability to effectively track reference signals, while also rejecting disturbances and uncertainties, thereby corroborating its efficacy for the QUBE-SERVO application.
Moreover, the original MPC algorithm was enhanced to improve its performance for trajectory tracking tasks. We also discuss the integration of the MPC algorithm within the MatLAB and LabVIEW programming environments, which served as the base platforms for designing and running the simulations in this project.
This paper, therefore, presents a comprehensive and practical approach for the successful implementation of the MPC algorithm in the QUANSER QUBE-SERVO system, and demonstrates its potential for wider application in similar control systems
New Trends in the Control of Robots and Mechatronic Systems
In recent years, research into the control of robotic and mechatronic systems has led to a wide variety of advanced paradigms and techniques, which have been extensively analysed and discussed in the scientific literature [...
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