Time-Varying System Identification Using Modulating Functions and Spline Models With Application to Bio-Processes

Time dependent parameters are frequently encountered in many real processes which need to be monitored for process modeling, control and supervision purposes. Modulating functions methods are especially suitable for this task because they use the original continuous-time differential equations and avoid differentiation of noisy signals. Among the many versions of the method available, Pearson–Lee method offers a computationally efficient alternative. In this paper, Pearson–Lee method is generalized for non-stationary continuous-time systems and the on-line version is developed. The time dependent parameters are modeled as polynomial splines inside a moving data window and recursion formulae using shifting properties of sinusoids are formulated. The simple matrix update relations considerably reduce the number of computations required when compared with repeatedly using FFT. The method is illustrated for estimating the kinetic rates and yield factors as time-varying parameters in a fermentation process. The Monod law along with temperature dependency models were used to simulate the data. The simulation study shows that it is not necessary to assume a growth model in order to estimate the kinetic rate parameters

LONG-TERM MULTI-DIMENSIONAL INTERACTIVE TIME-LAPSE PHOTOGRAPHY USING MICROSOFT KINECT

In this thesis, a method is presented for the capture and interactive presentation of long-term multi-dimensional time-lapse photography. Time-lapse capture is commonly used for the observation based study of relatively long term phenomena such as plant growth and weather patterns. In terms of filmic devices, the visual time compression effect is complementary to slow motion and is nearly as prevalent. In this project, commonly available camera and computer equipment is used to capture images autonomously with minimal system supervision. A set of images is established, using long term, short interval continuous capture at a fixed position. Results are presented demonstrating dynamic movement within this set using the Microsoft Kinect sensor for Xbox 360 to evaluate participant gestures in real-time. Viewers\u27 tracked movements and positions motivate specific frame selection and playback order, allowing independent navigation through the time-lapse, independently by time of day, time of season, and in any order participants can obtain with their own movement and performance

Deep Reinforcement Learning for Tensegrity Robot Locomotion

Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and other supplementary materials are available from http://rll.berkeley.edu/drl_tensegrityComment: International Conference on Robotics and Automation (ICRA), 2017. Project website link is http://rll.berkeley.edu/drl_tensegrit