The modeling of the time dependent, dynamic behavior of the human musculoskeletal system results in a large scale mechanical multibody system. This consists of submodels for the skeleton, wobbling masses, muscles and tendons as redundant actuators. Optimization models are required for the simulation of the muscle groups involved in a motion. In contrast to the inverse dynamics simulation the fo...

Model based technologies form the core of advanced robotic applications such as model predictive control and feedback linearization. More sophisticated models result in higher quality but the use in embedded real-time control systems imposes strict requirements on timing, memory allocation, and robustness. To satisfy these constraints, the model implementation is often optimized by manual codin...

A new feedback control method is proposed to control the spatio-temporal dynamics in excitable media. Applying suitable external forcing to the system’s slow variable, successful suppression and control of propagating pulses as well as spiral waves can be obtained. The proposed controller is composed by an observer to infer uncertain terms such as diffusive transport and kinetic rates, and an i...

It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data point...

This paper suggests an inverse optimal proportional–integral–derivative (PID) control design method to track trajectories in Lagrangian systems. The inverse optimal PID controller exists if and only if the Lagrangian system is extended disturbance input-to-state stable. First, we find the Lyapunov function and the control law that satisfy the extended disturbance input-to-state stability by usi...

Based on computational principles, the concept of an internal model for adaptive control has been divided into a forward and an inverse model. However, there is as yet little evidence that learning control by the eNS is through adaptation of one or the other. Here we examine two adaptive control architectures, one based only on the inverse model and other based on a combination of forward and i...

Drive cycle simulations of longitudinal vehicle models is an important tool for design and analysis of power trains. On the market today there are several tools for such simulations, and these tools use mainly two different methods of simulation, forward dynamic or quasi-static inverse simulation. Forward dynamic simulation is capable of describing the dynamic behavior of a system to a high lev...

This paper presents a neuro-fuzzy internal model Cartesian controller for robot manipulators. An inductive learning technique is applied to generate the required inverse dynamics and inverse kinematics modelling rules from input/output measurements. A fully differentiable fuzzy neural network is used to construct the adaptive sections of the controller for on-line parameters adaptation. A fuzzy...