Robotic and mechatronic systems, autonomous vehicles, electric power systems and smart grids, as well as manufacturing and industrial production systems can exhibit complex nonlinear dynamics or spatio-temporal dynamics which need to be controlled to ensure good functioning and performance.

Demand response (DR) describes controlled changes in the power consumption of an electric load to better match the power demand with the supply. This helps with increasing the share of intermittent renewables like solar and wind, thus ensuring use of the generated clean power and reducing the need for storage capacity.

Robotics, mechatronics and autonomous systems can exhibit complex nonlinear dynamics which can lead to unsatisfactory transients and deviation from setpoints or even to instability. A standard approach in the control of these systems had been the concept of diffeomorphisms to bring a system into a linear form. However, these methods are not straightforward and result in complicated state-space model transformations. In this monograph, new methods have been investigated which are not constrained by the shortcomings of global linearization-based control schemes. They can be implemented in a computationally simple manner, are followed by global stability proofs, and perform better than previous optimal control approaches for a wider class of nonlinear dynamical systems and applications.