Electromobility
Drives & electronics
more even under difficult conditions.
precise, reliable & series-tested
Intelligent control algorithms
Know-how and many years of experience
Field-tested control algorithms
Control algorithms are often the heart of a technical application.
adcos has the right solution, especially for demanding control tasks.
Field-oriented controllers for EC-motors
Field-oriented controllers for EC-motors are widespread and have been state-of-the-art for many years.
Train-like driving of multi-articulated buses
Multi-articulated buses, in which several or even all axles can be steered individually, are being used more and more, especially in large cities in China.
Optimal state space control
Optimal state space controllers are particularly suitable for complex systems where classic PID controllers are not sufficient.
Field-oriented controllers for EC-motors are widespread and have been state-of-the-art for many years.
Nevertheless, there are special features in many applications that require detailed specialist knowledge and corresponding experience in order to efficiently get problems under control. We have bundled our knowledge in this area, built up over many years and in a wide variety of projects, in control libraries with associated system models as well as technical documentation and requirements.
Our validated and series-tested algorithms are extremely robust and offer high reliability over the entire operating and temperature range.
Key features
- Control library with associated system models as well as technical documentation and requirements
- Validated and series-tested algorithms with high reliability
- Reference designs for different applications
- Use for sensorless and sensor-based systems
- Supporting tools for motor analysis and parameter identification
- Know-how transfer
Multi-articulated buses, in which several or even all axles can be steered individually, are being used more and more, especially in large cities in China.
There is a requirement for the vehicle to be guided as accurately as possible on the track and with centimeter precision, analogous to the classic tram. At the same time, the vehicle must not be tense and the resulting articulation and tire forces must not become too great. This can be achieved by an intelligent multi-axle steering control that calculates a trajectory (virtual rail) based on the first axle and steers the following axles in such a way that the best compromise between directional stability and the articulation and tire lateral forces that occur is achieved.
Through several successfully completed projects, we have developed corresponding multi-axis steering controllers and associated vehicle models for different vehicle configurations and tested them in real operation. We have summarized this know-how in control libraries with associated system models as well as technical documentation and requirements.
Key features
- Control library with associated system models as well as technical documentation and requirements
- Validated and series-tested algorithms with high reliability
- Reference designs for different applications
- Adaptation to specific bus configurations (number and arrangement of articulations and axes)
- Know-how transfer
Optimal state space controllers are particularly suitable for complex systems where classic PID controllers are not sufficient. This is the case, for example, in systems with dead time, higher order (large number of states), unstable systems or multivariable systems (several manipulated variables, measured variables or objective variables). The design of one or more PID controllers is usually not effective or provides unsatisfactory results.
Optimum state space controllers such as LQG (Linear Quadratic Gaussian) or MPC (Model Predictive Control) are particularly well suited for such controlled systems. By formally describing the controller and plant in the state space, any number of states, manipulated variables, measured variables or objective variables can be taken into account. The controller can be designed under certain conditions such as controllability and observability and provides an optimal result. Corresponding extensions also exist for non-linear controlled systems.
We have been dealing with optimal state space controllers for many years, have a corresponding amount of practical experience and bundle our know-how in user-friendly tools and documentations.