Pant, Yash Vardhan

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2011 - 201917
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Now showing 1 - 10 of 17
  • Publication
    AUTOPLUG: An Architecture for Remote Electronic Controller Unit Diagnostics in Automotive Systems
    (2012-01-01) Pant, Yash Vardhan; Pajic, Miroslav; Mangharam, Rahul
    In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) architecture between the vehicle and a Remote Diagnostics Center to diagnose, test, update and verify controls software. Within the vehicle, we evaluate observerbased runtime diagnostic schemes and introduce a framework for remote management of vehicle recalls. The diagnostics scheme deals with both real-time and non-real time faults, and we introduce a decision function to detect and isolate faults in a system with modeling uncertainties. We also evaluate the applicability of “Opportunistic Diagnostics”, where the observerbased diagnostics are scheduled in the ECU’s RTOS only when there is slack available in the system. This aperiodic diagnostics scheme performs similar to the standard, periodic diagnostics scheme under reasonable assumptions. This approach works on existing ECUs and does not interfere with current task sets. The overall framework integrates in-vehicle and remote diagnostics and serves to make vehicle recalls management a less reactive and cost-intensive procedure.
  • Publication
    Tech Report: Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints Via Feedback Linearization
    (2016-03-15) Pant, Yash Vardhan; Abbas, Houssam; Mangharam, Rahul
    Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance
  • Publication
    Smooth Operator: Control using the Smooth Robustness of Temporal Logic
    (2017-08-01) Pant, Yash Vardhan; Abbas, Houssam; Mangharam, Rahul
    Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outper- forms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems.
  • Publication
    Fly-by-Logic: Control of Multi-Drone Fleets with Temporal Logic Objectives
    (2018-03-19) Pant, Yash Vardhan; Abbas, Houssam; Quaye, Rhudii A.; Mangharam, Rahul
    The problem of safe planning and control for multi- drone systems across a variety of missions is of critical impor- tance, as the scope of tasks assigned to such systems increases. In this paper, we present an approach to solve this problem for multi-quadrotor missions. Given a mission expressed in Signal Temporal Logic (STL), our controller maximizes robustness to generate trajectories for the quadrotors that satisfy the STL spec- ification in continuous-time. We also show that the constraints on our optimization guarantees that these trajectories can be tracked nearly perfectly by lower level off-the-shelf position and attitude controllers. Our approach avoids the oversimplifying abstractions found in many planning methods, while retaining the expressiveness of missions encoded in STL allowing us to handle complex spatial, temporal and reactive requirements. Through experiments, both in simulation and on actual quadrotors, we show the performance, scalability and real-time applicability of our method.
  • Publication
    Peak Power Reduction in Hybrid Energy Systems with Limited Load Forecasts
    (2014-03-18) Pant, Yash Vardhan; Nghiem, Truong X; Mangharam, Rahul
    Hybrid energy systems, which consist of a load powered by a source and a form of energy storage, find applications in many systems, e.g., the electric grid and electric vehicles. A key problem for hybrid energy systems is the reduction of peak power consumption to ensure cost-efficient operation as peak power draws require additional resources and adversely affect the system reliability and lifetime. Furthermore, in some cases such as electric vehicles, the load dynamics are fast, not perfectly known in advance and the on-board computation power is often limited, making the implementation of traditional optimal control difficult. We aim to develop a control scheme to reduce the peak power drawn from the source for hybrid energy systems with limited computation power and limited load forecasts. We propose a scheme with two control levels and provide a sufficient condition for control of the different energy storage/generation components to meet the instantaneous load while satisfying a peak power threshold. The scheme provides performance comparable to Model Predictive Control, while requiring less computation power and only coarse-grained load predictions. For a case study, we implement the scheme for a battery-supercapacitor-powered electric vehicle with real world drive cycles to demonstrate the low execution time and effective reduction of the battery power (hence temperature), which is crucial to the lifetime of the battery.
  • Publication
    Temporal Logic Robustness for General Signal Classes
    (2019-04-15) Abbas, Houssam; Pant, Yash Vardhan; Mangharam, Rahul
    In multi-agent systems, robots transmit their planned trajectories to each other or to a central controller, and each receiver plans its own actions by maximizing a measure of mission satisfaction. For missions expressed in temporal logic, the robustness function plays the role of satisfaction measure. Currently, a Piece-Wise Linear (PWL) or piece-wise constant reconstruction is used at the receiver. This allows an efficient robustness computation algorithm - a.k.a. monitoring - but is not adaptive to the signal class of interest, and does not leverage the compression properties of more general representations. When communication capacity is at a premium, this is a serious bottleneck. In this paper we first show that the robustness computation is significantly affected by how the continuous-time signal is reconstructed from the received samples, which can mean the difference between a successful control and a crash. We show that monitoring general spline-based reconstructions yields a smaller robustness error, and that it can be done with the same time complexity as monitoring the simpler PWL reconstructions. Thus robustness computation can now be adapted to the signal class of interest. We further show that the monitoring error is tightly upper-bounded by the L ∞ signal reconstruction error. We present a (non-linear) L ∞ -based scheme which yields even lower monitoring error than the spline-based schemes (which have the advantage of being faster to compute), and illustrate all results on two case studies. As an application of these results, we show how time-frequency specifications can be efficiently monitored online.
  • Publication
    Technical Report: Anytime Computation and Control for Autonomous Systems
    (2019-04-15) Pant, Yash Vardhan; Abbas, Houssam; Mohta, Kartik; Quaye, Rhudii A.; Nghiem, Truong X; Devietti, Joseph; Mangharam, Rahul
    The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. A crucial part of the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators, especially those used for localization, often use computationally expensive perception algorithms like visual object tracking. With on-board computers on autonomous robots being computationally limited, the computation time of a perception-based estimation algorithm can at times be high enough to result in poor control performance. In this work, we develop a framework for co-design of anytime estimation and robust control algorithms while taking into account computation delays and estimation inaccuracies. This is achieved by constructing a perception-based anytime estimator from an off-the-shelf perception-based estimation algorithm, and in the process we obtain a trade-off curve for its computation time versus estimation error. This information is used in the design of a robust predictive control algorithm that at run-time decides a contract for the estimator, or the mode of operation of estimator, in addition to trying to achieve its control objectives at a reduced computation energy cost. In cases where the estimation delay can result in possibly degraded control performance, we provide an optimal manner in which the controller can use this trade-off curve to reduce estimation delay at the cost of higher inaccuracy, all the while guaranteeing that control objectives are robustly satisfied. Through experiments on a hexrotor platform running a visual odometry algorithm for state estimation, we show how our method results in upto a 10% improvement in control performance while saving 5-6% in computation energy as compared to a method that does not leverage the co-design.
  • Publication
    Robust Model Predictive Control with Anytime Estimation
    (2014-12-01) Nghiem, Truong X; Pant, Yash Vardhan; Mangharam, Rahul
    With an increasing autonomy in modern control systems comes an increasing amount of sensor data to be processed, leading to overloaded computation and communication in the systems. For example, a vision-based robot controller processes large image data from cameras at high frequency to observe the robot’s state in the surrounding environment, which is used to compute control commands. In real-time control systems where large volume of data is processed for feedback control, the data-dependent state estimation can become a computation and communication bottleneck, resulting in potentially degraded control performance. Anytime algorithms, which offer a trade-off between execution time and accuracy of computation, can be leveraged in such systems. We present a Robust Model Predictive Control approach with an Anytime State Estimation Algorithm, which computes both the optimal control signal for the plant and the (time-varying) deadline/accuracy constraint for the anytime estimator. Our approach improves the system’s performance (concerning both the control performance and the estimation cost) over conventional controllers, which are designed for and operate at a fixed computation time/accuracy setting. We numerically evaluate our approach in an idealized motion model for navigation with both state and control constraints.
  • Publication
    Robustness Evaluation of Computer-aided Clinical Trials for Medical Devices
    (2019-03-14) Jang, Kuk Jin; Pant, Yash Vardhan; Zhang, Bo; Weimer, James; Mangharam, Rahul
    Medical cyber-physical systems, such as the implantable cardioverter defibrillator (ICD), require evaluation of safety and efficacy in the context of a patient population in a clinical trial. Advances in computer modeling and simulation allow for generation of a simulated cohort or virtual cohort which mimics a patient population and can be used as a source of prior information. A major obstacle to acceptance of simulation results as a source of prior information is the lack of a framework for explicitly modeling sources of uncertainty in simulation results and quantifying the effect on trial outcomes. In this work, we formulate the Computer-Aided Clinical Trial (CACT) within a Bayesian statistical framework allowing explicit modeling of assumptions and utilization of simulation results at all stages of a clinical trial. To quantify the robustness of the CACT outcome with respect to a simulation assumption, we define δ-robustness as the minimum perturbation of the base prior distribution resulting in a change of the CACT outcome and provide a method to estimate the δ-robustness. We demonstrate the utility of the framework and how the results of δ-robustness evaluation can be utilized at various stages of a clinical trial through an application to the Rhythm ID Goes Head-to-head Trial (RIGHT), which was a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. Finally, we introduce a hardware interface that allows for direct interaction with the physical device in order to validate and confirm the results of a CACT for implantable cardiac devices.
  • Publication
    Peak Power Control of Battery and Super-capacitor Energy Systems in Electric Vehicles
    (2014-02-01) Pant, Yash Vardhan; Nghiem, Truong X; Mangharam, Rahul
    Hybrid energy systems consist of a load powered by a source and a form of energy storage. Systems with mixed energy supply find applications in the electric grid with renewable and non-renewable sources, in mission critical systems such as Mars rovers with rechargeable and non-rechargeable batteries and low-power monitoring systems with energy harvesting. A general problem for hybrid energy systems is the reduction of peak power consumption to ensure cost-efficient operation as peak power draws require additional resources, adversely affect the system reliability and storage lifetime. Furthermore, in some cases such as electric vehicles, the load dynamics are fast, not perfectly known a priori and the computation power available is often limited, making the implementation of traditional optimal control difficult. This paper aims to develop a control scheme to reduce the peak power drawn from the source for hybrid energy systems with limited computation power and limited load forecasts. We propose a scheme with two control levels and provide a sufficient condition for control of the different energy storage/generation components to meet the instantaneous load while satisfying a peak power threshold. The scheme provides performance comparable to Model Predictive Control, while requiring less computation power and only coarse-grained load predictions. As a case study we implement the scheme for a battery-supercapacitor system in an electric vehicle with real world drive cycles to demonstrate the low execution time and effective reduction of the battery power (hence temperature), which is crucial to the lifetime of the battery.