Modelling and simulation for mechatronic design in automotive systems

This paper gives an overview of current industry based projects in the field of vehicle modelling and simulation for the mechatronic design of automotive systems. It shows the wide range of applications for analysis and synthesis during the development process, including vehicle systems, vehicle dynamics, occupant safety, adaptive cruise control, hardware-in-the-loop and fault tolerant real-time systems.     

Multidisciplinary interface model for design of mechatronic systems

The design of mechatronic systems is based on the integration of several disciplines, such as mechanical, electrical and software engineering. How to achieve an integrated multidisciplinary design during the development process of mechatronic systems has attracted the attention of both academia and industry. However, solutions which can fully solve this problem have not been proposed by now. The concept of multidisciplinary interface represents the logical or physical relationship integrating the components of the mechatronic system or the components with their environment. As the design of mechatronic systems is a multidisciplinary work, the multidisciplinary interface model can be considered as one of the most effective supports to aid designers for achieving the integrated multidisciplinary design during the development process. The paper presents a multidisciplinary interface model for design of mechatronic systems in order to enable the multidisciplinary integration among design team members from different disciplines. On the one hand, the proposed model ensures the consistency of interface defined by the designers. On the other hand, it helps the designers to guarantee the different components integrate correctly. The interface model including three concepts: classification, data model and compatibility rules. The multidisciplinary interface model is implemented by a case study based on a 3D measurement system.     

Computer support for mechatronic control system design

This paper discusses the demands for proper tools for computer aided control system design of mechatronic systems and identifies a number of tasks in this design process. Real mechatronic design, involving input from specialists from varying disciplines, requires that the system can be represented in multiple views. Several tools are already available but there are still substantial shortcomings. The paper gives indications about the developments needed to come to better design tools in the future. A specific example is worked out in more detail, i.e., automated performance assessment of mechatronic motion systems during the conceptual design stage.     

Category theory-based collaborative design methodology for mechatronic systems

Recent advances in companies are characterized by highly dynamic, knowledge-intensive and collaborative process. This has become primary concern for mechatronic systems since they involve multiple disciplines and knowledge. This requires a close exchange in order to share knowledge between the different design teams. The first step in knowledge sharing is to identify the most important knowledge that need to be capitalized, which we call “crucial knowledge”. During this exchange, heterogeneous knowledge and modelling languages are involved in the design process, which can lead to conflicts. Hence, the challenge is to continuously capture and handle such conflicts between expert models. Thus, the focus of this paper is to propose a new collaborative design model suitable for mechatronic concurrent design. Our contribution lies in identifying crucial knowledge and resolving conflicts in a formal way in order to ensure efficient collaboration. Our methodology called Category Theory-based Collaborative Design (CaTCoD) is described with its associated meta-model. A demonstrator is also used to validate the proposed methodology using an example from the aeronautic field.     

A SysML-based methodology for mechatronic systems architectural design

Mechatronic systems are characterized by the synergic interaction between their components from different technological domains. These interactions enable the system to achieve more functionalities than the sum of the functionalities of its components considered independently. Traditional design approaches are no longer adequate and there is a need for new synergic and multidisciplinary design approaches with close cooperation between specialists from different disciplines.SysML is a general purpose multi-view language for systems modeling and is identified as a support to this work.In this paper, a SysML-based methodology is proposed. This methodology consists of two phases: a black box analysis with an external point of view that provides a comprehensive and consistent set requirements, and a white box analysis that progressively leads to the internal architecture and behavior of the system.     

Tool support for the design of self-optimizing mechatronic multi-agent systems

Complex technical systems, such as mechatronic systems, can exploit networking as well as the computational power available today to achieve an automatic improvement of the technical system performance at run-time through self-optimization. To realize this vision, appropriate means for the design of such self-optimizing mechatronic systems are required. Well-established techniques and tools for the modeling of cognitive behavior, reflective behavior, and control behavior exist. However, to really enable self-optimization and its full potential, these different aspects have to be safely integrated in a manner that remains comprehensible to the designer. In this article, we present how this required integration has been realized at the semantic level by extending the unified modeling language (UML), and at the tool level by integrating the CAE tool CAMeL and the CASE tool Fujaba real-time tool suite. The presented Mechatronic UML approach supports the design of verifiable, complex, reconfigurable mechatronic systems using the multi-agent system metaphor. This work was developed in the course of the Special Research Initiative 614—self-optimizing concepts and structures in mechanical engineering—University of Paderborn, and was published on its behalf and funded by the Deutsche Forschungsgemeinschaft. Sven Burmester, Oliver Oberschelp, Florian Klein and Peter Scheideler are members of the respective research group which left after the paper was submitted.     

Evolutionary design of discrete controllers for hybrid mechatronic systems

This paper investigates the issue of evolutionary design of controllers for hybrid mechatronic systems. Finite State Automaton (FSA) is selected as the representation for a discrete controller due to its interpretability, fast execution speed and natural extension to a statechart, which is very popular in industrial applications. A case study of a two-tank system is used to demonstrate that the proposed evolutionary approach can lead to a successful design of an FSA controller for the hybrid mechatronic system, represented by a hybrid bond graph. Generalisation of the evolved FSA controller to unknown control targets is also tested. Further, a comparison with another type of controller, a lookahead controller, is conducted, with advantages and disadvantages of each discussed. The comparison sheds light on which type of controller representation is a better choice to use in various stages of the evolutionary design of controllers for hybrid mechatronic systems. Finally, some important future research directions are pointed out, leading to the major work of the succeeding part of the research.     

Advances in the control of mechatronic suspension systems

The suspension system is a key element in motor vehicles. Advancements in electronics and micropro- cessor technology have led to the realization of mechatronic suspensions. Since its introduction in some production motorcars in the 1980s, it has remained an area which sees active research and development, and this will likely continue for many years to come. With the aim of identifying current trends and future focus areas, this paper presents a review on the state-of-the-art of mechatronic suspensions. First, some commonly used classifications of mechatronic suspensions are presented. This is followed by a discussion on some of the actuating mechanisms used to provide control action. A survey is then reported on the many types of control approaches, including look-ahead preview, predictive, fuzzy logic, proportional-integral-derivative （PID）, optimal, robust, adaptive, robust adaptive, and switching control. In conclusion, hydraulic actuators are most commonly used, but they impose high power requirements, limiting practical realizations of active suspensions. Electromagnetic actuators are seen to hold the promise of lower power requirements, and rigorous research and development should be conducted to make them commercially usable. Current focus on control methods that are robust to suspension parameter variations also seems to produce limited performance improvements, and future control approaches should be adaptive to the changeable driving conditions.     

Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity

The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened.     

Estimation and control of mechatronic systems using sensitivity bond graphs

A new bond graph framework for sensitivity theory is applied to model-based predictive control, state estimation, and parameter estimation in the context of physical systems. The approach is illustrated using a nonlinear mechatronic system.     

Review:Advances in the control of mechatronic suspension systems

The suspension system is a key element in motor vehicles. Advancements in electronics and microprocessor technology have led to the realization of mechatronic suspensions. Since its introduction in some production motorcars in the 1980 s, it has remained an area which sees active research and development, and this will likely continue for many years to come. With the aim of identifying current trends and future focus areas, this paper presents a review on the state-of-the-art of mechatronic suspensions. First, some commonly used classifications of mechatronic suspensions are presented. This is followed by a discussion on some of the actuating mechanisms used to provide control action. A survey is then reported on the many types of control approaches, including look-ahead preview, predictive, fuzzy logic, proportional–integral–derivative(PID), optimal, robust, adaptive, robust adaptive,and switching control. In conclusion, hydraulic actuators are most commonly used, but they impose high power requirements, limiting practical realizations of active suspensions. Electromagnetic actuators are seen to hold the promise of lower power requirements, and rigorous research and development should be conducted to make them commercially usable. Current focus on control methods that are robust to suspension parameter variations also seems to produce limited performance improvements, and future control approaches should be adaptive to the changeable driving conditions.     

Modelling and motion control of a mechatronic system using BGM with intelligent controllers

In this research paper, a mechatronics system such as a pan tilt platform (PTP) has been considered for motion control under intelligent controllers. A proportional-derivative (PD) controller is considered for comparison of results obtained from fuzzy and hybrid controllers. The trajectory following performance of the mechatronics system is found against these controllers. The results of simulations show that hybrid fuzzy controller reduce the tracking error effectively in lesser settling time. The intelligent controllers require knowledge base of error and derivative of error to compensate the PTP dynamics. The intelligent controllers have similar trends as the PD controllers and compensated both electrical and mechanical dynamics. The PD controller requires position measurement. The intelligent controllers have knowledge base consisting of position and velocity data. Thus intelligent controllers have position measurement along with knowledge base for position control system. The best results were achieved with hybrid fuzzy controllers. They meet the desired specifications.     

Modelling and optimal controller design of networked control systems with multiple delays

In this paper we discuss the modelling and control of networked control systems (NCS) where sensors, actuators and controllers are distributed and interconnected by a common communication network. Multiple distributed communication delays as well as multiple inputs and multiple outputs (MIMO) are considered in the modelling algorithm. In addition, the asynchronous sampling mechanisms of distributed sensors are characterized to obtain the actual time delays between sensors and the controller. Due to the characteristics of a network architecture, piecewise constant plant inputs are assumed and discrete-time models of plant and controller dynamics are adopted to analyse the stability and performance of a closed-loop NCS. The analysis result is used to verify the stability and performance of an NCS without considering the impact of multiple time delays in the controller design. In addition, the proposed NCS model is used as a foundation for optimal controller design. The proposed control algorithm utilizes the information of delayed signals and improves the control performance of a control system encountering distributed communication delays. Several simulation studies are provided to verify the control performance of the proposed controller design.     

Modelling and control design for a magnetic levitation system

We study the problem of controlling the position of a platen levitated using linear motors in three-dimensional space. We develop a non-linear six-state model of the system and provide two non-linear controllers solving the set point stabilization problem. The first controller is derived by decomposing the model in two subsystems, applying feedback linearization to one of them, and using the invariance principle to prove attractiveness of the origin of the second subsystem. The second controller is found by dynamic feedback linearizing the entire system dynamics. In both cases we provide a rigorous procedure to determine the operating range of the device.     

Targeted processing for real-time embedded mechatronic systems

This paper describes the architecture and design framework for a multiprocessor system on chip (SoC) solution that is being developed for adaptive, high-performance, embedded real-time control applications. Most of the design-to-implementation stages are automated by software tools avoiding most of the error-prone programming tasks and hardware-related issues. Therefore, the work presented here minimises the interdisciplinary design efforts typical to mechatronic systems design, allowing control engineers to focus mainly on the control laws development. The performance achieved by the proposed architecture allows for a straightforward addressing of implementation requirements for a variety of embedded applications, including micro-electromechanical systems.     

Virtual prototyping of mechatronic systems

Mechatronic systems abound in technological fields such as robotics and machine tools industry. Significant advances in dynamic performance of these systems can be achieved provided that mutual interactions of different domains (such as mechanics, electronics, hydraulics and control) are thoroughly understood. Virtual prototyping entails integration of multi-domain dynamic simulation in the design process, in order to reproduce and analyze the effects of design choices on the overall performance. This paper states the requirements of the modeling language and software tool for simulation of mechatronic systems and describes an experience of use of DYMOLA with Modelica language in the simulation of a complete machining center. The model has been successfully validated against experimental results collected on the real machining center.     

Rapid prototyping of real-time control laws for complex mechatronic systems: a case study

Rapid prototyping of complex systems embedded in even more complex environments raises the need for a layered design approach. Our example is a mechatronic design taken from the automotive industry and illustrates the rapid-prototyping procedure of real-time-critical control laws. The approach is based on an object-oriented structuring allowing not only central control units but also distributed control units as needed by today’s designs. The implementation of control laws is a hardware-in-the-loop simulation, refined in steps and reducing the simulation part at every one of these. On the lower level, common platforms, such as FPGAs, microcontrollers or specialized platforms, can be instantiated.