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DRIVES & CONTROLS Model-based design of advanced motor control systems By Anders Frederiksen Leveraging adva nced procesor functionality to facilitate ease of design has been discussed throughout recent decades. Nowadays even greater design flexibility allows engineers to use standard Model-Based design with MATLAB and Simulink can be used to optimize motor control systems functionality and to minimize overall design time. It also enables design engineers to reuse simulation models to ensure the correct functionality and desired performance of a system in its end market application. Model-Based Design (MBD) has been a discussion topic for decades but has only in recent years evolved into a complete design flow - from model creation to complete implementation. In the 1970s analogue computing platforms were available for simulation but control hardware implementation was done at the transistor level. Simulation tools advances through to the 2000s saw the introduction of graphical control schematic entry tools and control design tools that vastly simplified the task of complex control design and evaluation. However, the control system designer still developed the hardware control algorithm by writing C code to mirror the simulated design. Now at the start of this decade, complete MBD allows a common control design for both simulation and hardware implementation platforms enabling complex control algorithms to be rapidly deployed on hardware platforms. MBD is a process that uses a system model as an executable specification throughout development. This simulation based approach gives you a better understanding of design alternatives and trade-offs than traditional hardware prototype-based design methodologies, enabling you to optimize your design to meet predefined performance criteria. Rather than using complex structures and extensive software code, designers Fig. 1: Model-based design flow. Fig. 2: Concept of a model-based design implementation. Fig. 3: Model-based design setup. can define models with advanced functional characteristics using continuous-time and discrete-time building blocks. Existing C code can be integrated with standard control library blocks to maximize design efficiency. These models, used with simulation tools, can lead to rapid prototyping, software testing and hardware-in-the-loop (HIL) simulation. Simulation enables specification discrepancies and modelling errors to be found immediately, rather than later in the design cycle. Automatic code generation eliminates the manual steps in implementation the same algorithm to run on the hardware platform. This simplifies the design process, minimizes errors in hardware design implementation and reduces the overall time-to-market. There are multiple steps within MBD that allow optimization of individual tasks in the overall design. These tasks can be completed by different design engineers or design teams, and then combined to form the overall design and complete system – see figure 1. With this approach, a higher level of abstraction of the individual tasks can be applied, resulting in an overall design flow optimized for the given end application. All-in-all MBD allows a designer to expand from more classical design schemes and move directly from model creation to simulation, code generation, and HIL test, in a controlled fashion that allows incremental changes in system behaviour without a complete redesign of the system. In figure 2 the different design phases and the scale of individual steps in the flow are visualized. These steps together describe the “standard” flow of MBD. From a motor control design perspective they are: Concept of operations Overall functionality of the motor system Plant modelling / Architecture Development of models of motor, load, power electronics, signal conditioning, etc. Controller modelling and requirements Encoder-based field oriented control of 3-phase PM motor Analysis and synthesis – Detailed design Models created above are used to identify dynamic characteristics of the plant model Tuning and configuration of the system Validation and test Off-line simulation and/or real-time simulation Investigation of time response of the dynamic system Deployment to embedded target – Full operation Automatic code generation Test and verification Updating controller model Anders Norlin Frederiksen is Segment Marketing Manager for Motor and Power Control at Analog Devices – www.analog.com – He can be reached at anders.frederiksen@analog.com 30 Electronic Engineering Times Europe December 2013 www.electronics-eetimes.com


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