This paper presents an automated knee rehabilitation system designed to assist patients during the gait cycle. The system utilizes a predictive model based on a neural network to generate trajectories of the knee motion from data captured by a motion capture system. Also, some control schemes, a PID, a Second Order Sliding Mode Control, and an Active Disturbance Rejection Control, were implemented to follow these trajectories, considering knee dynamics and external disturbances. Finally, the experimental setup was developed to implement and compare these controllers. This adaptable solution has potential applications for post-surgical patients, athletes, and children with motor difficulties.

This paper proposes a fluid based macroscopical perspective for modeling and simulation of Vehicular Traffic Networks based on the connection of continuous dynamical agents. The agents' flows are designed to be facilitated by a high-level planner that represents conventional traffic signals cycles. The research examines the dynamics of groups using various state-space representations. Some numerical simulations are presented to demonstrate the approach's use and scalability. In order to evaluate the evolution of the system design, several performance criteria were also established and tested.

This paper presents a novel modeling of complex urban traffic networks based on the interconnections of agents which represent the vehicular fluid dynamics in physical spaces like streets and intersections, supervised by a Petri Net. The agent's behavior is ruled by simple nonlinear differential equations based on the vehicular density, average vehicle velocities, congestions and occupation capacities. The Petri Net is designed to manage the traffic light cycles, enabling or disabling the flow of vehicles to and from adjacent agents. The main contribution of the paper is to define the basis of a hybrid and modular system to be addressed by the theory of multi-agent systems and supervisory control of Petri Nets. An example modeling a real intersection in the city of Montevideo is presented. The model's behavior is compared with a common traffic simulator in order to show the potential of the approach and its scalability to more complex traffic networks.

The Gyrover is a single wheel gyroscopically stabilized mobile robot developed at Carnegie Mellon University. An internal pendulum serves as a counter weight for a drive motor that causes fore/aft motion, while a tilt-mechanism on a large gyroscope provides a mechanism for lateral actuation. In this paper we develop a detailed dynamic model for the Gyrover and use this model in an extended Kalman filter to estimate the complete state. A linearized version of the model is used to develop a state feedback controller. The design methodology is based on a semi-definite programming procedure which optimize the stability region subject to a set of linear matrix inequalities that capture stability and pole placement constraints. Finally, the controller design combined with the extended Kalman filter are verified on the prototype.