Algorand is a public blockchain platform designed for digital transactions and smart contracts, utilizing an energy-efficient pure proof-of-stake consensus mechanism based on a Byzantine agreement protocol. Despite its potential, its performance under high traffic conditions remains largely unexplored. This study aims to evaluate key performance metrics of Algorand, including transaction throughput, block utilization, transaction confirmation time, block time, and block size, under high load stress to assess the impact of heavy traffic. The network was stressed through two experimental setups: In the first, the nodes sent between 5,000 and 45,000 payment transactions to the Algorand network; in the second, the transaction rate was fixed at 5,000 transactions per second, with stress durations ranging from 2 to 40 seconds. The results demonstrate that Algorand distributes transactions across multiple blocks before the first block reaches full capacity. Transaction confirmation times increased with higher traffic and longer stress durations, occasionally exceeding one minute. Block size and throughput initially increased with higher transaction rates and extended stress periods, but eventually stabilized. This research represents the first comprehensive stress test of the Algorand network under high transaction loads. Future research could focus on identifying and mitigating bottlenecks in the transaction confirmation process to enhance Algorand’s performance further.
This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and asymptotically steers the tracking error to zero. The brief also presents a closed-loop stability proof of the method, ensuring perfect asymptotic estimation and tracking by the controlled vehicle. The proposed solution advantageously needs no a-priori information about the total disturbance boundedness, additional variables to model uncertainty, or observer parameters to be tuned. Its effectiveness and superiority to existing methods are studied in theory and shown in simulations where a full racecar model, based on the vehicle dynamics blockset, is required to track aggressive maneuvers. Through a faster and more accurate disturbance estimation, the solution robustly ensures better dynamic responses even with measurement noise.
Accurate and robust control methodologies are critical to the reliable and safe operation of engineering systems. Sliding mode control (SMC) is a form of variable structure control and is regarded as one of the most effective nonlinear robust control approaches. The control law is designed so that the system state trajectories are forced towards the sliding surface and stays within a region of it. The switching gain in the control signal brings an inherent amount of stability to the control process. However, the controller is only as effective as the knowledge of critical system states and parameters. Estimation strategies, such as the Kalman filter or the smooth variable structure filter (SVSF), may be employed to improve the quality of the state estimates used by control methods. A recently developed SVSF formulation, referred to as the second-order SVSF, offers robustness and chattering suppression properties of second-order sliding mode systems. It produces robust state estimation by preserving the first- and second-order sliding conditions such that the measurement error and its first difference are pushed towards zero. This paper aims to combine the SMC with the second-order SVSF in an effort to develop and offer an improved control strategy. It is proposed that this controller will offer an improvement in terms of controller accuracy without affecting its inherent stability and robustness. An electro hydrostatic actuator will be used for proof of concept, and future work will extend the application to automotive power trains.
New offshore oil recourses that are developed are more difficult to drill and increase the requirements to the technology in the offshore industry. A relatively new technology is Managed Pressure Drilling where a choke topside is used to control the bottom hole pressure. The bottom hole pressure measurement is unreliable, which motivates the need for an observer. Different methods for estimation will be presented and compared in this thesis. Proofs of convergence are outlined for the Stamnes observer, derived for the Grip observer and some stepping stones for further work are presented for the Optimal Polynomial Filter. Each observer is simulated with a simple step in the mud pump to verify the estimation laws. The results show that all observes estimated the bottom hole pressure correctly for this simple case. A more realistic case, a pipe connection, is also simulated for each observer. The case includes zero flow from the mud pump, which reveals that all observers miss the estimation convergence in this case, but that the estimate converges when there is flow from the mud pump. One of the states that affects the bottom hole pressure is the pressure loss due to friction in drill string and annulus. Earlier work modeled these losses as quadratic with respect to the flow through the bit, which are simplifications. To improve the estimate of the bottom hole pressure, new and better friction models are needed. Measurement data from Gullfaks C are analyzed to get new knowledge of friction loss in the drilling string and annulus. For the drill string the quadratic friction model is found to be good enough, catching the main behavior. On the other hand, the friction loss in the annulus is a more complicated function of flow. The annulus friction is approximated with sets of basis functions and the weighted sum of these functions gives an approximation to the friction curve. Each weight is estimated to get the friction loss estimate. The use of the weighted sum of four 1st-order b-spline functions give a good approximation to the real friction curve, and the weight for each basis function is estimated. This is tested in simulations both with a simple case and the pipe connection case. The simulations show that the annulus friction loss and the bit pressure are estimated correctly.