Pushpak Jagtap

Pushpak Jagtap
Indian Institute of Science | IISC · Robert Bosch Centre for Cyber Physical Systems (RBCCPS)

PhD

About

40
Publications
3,369
Reads
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316
Citations
Additional affiliations
September 2021 - present
Indian Institute of Science
Position
  • Professor (Assistant)
July 2020 - August 2021
KTH Royal Institute of Technology
Position
  • PostDoc Position
July 2019 - June 2020
Ludwig-Maximilians-University of Munich
Position
  • Research Assistant
Education
January 2016 - December 2020
Technische Universität München
Field of study
  • Electrical and Computer Engineering
May 2012 - June 2014
Indian Institute of Technology Roorkee
Field of study
  • Systems and Control

Publications

Publications (40)
Preprint
In this paper, we focus on the problem of compositional synthesis of controllers enforcing signal temporal logic (STL) tasks over a class of continuous-time nonlinear interconnected systems. By leveraging the idea of funnel-based control, we show that a fragment of STL specifications can be formulated as assume-guarantee contracts. A new concept of...
Preprint
Full-text available
The paper presents a methodology for temporal logic verification of continuous-time switched stochastic systems. Our goal is to find the lower bound on the probability that a complex temporal property is satisfied over a finite time horizon. The required temporal properties of the system are expressed using a fragment of linear temporal logic, call...
Preprint
Full-text available
In this paper, we study formal synthesis of control policies for partially observed jump-diffusion systems against complex logic specifications. Given a state estimator, we utilize a discretization-free approach for formal synthesis of control policies by using a notation of control barrier functions without requiring any knowledge of the estimatio...
Thesis
Full-text available
This dissertation is motivated by the challenges arising in the synthesis of controllers for complex systems enforcing complex specifications (usually expressed as temporal logic formulae or (in)finite strings on automata). This thesis develops several controller synthesis approaches for various complex systems without discretizing state-sets that...
Article
In this paper, we focus on mitigating the computational complexity in abstraction-based controller synthesis for interconnected control systems. To do so, we provide a compositional framework for the construction of abstractions for interconnected systems and a bottom-up controller synthesis scheme. In particular, we propose a notion of approximate...
Preprint
Full-text available
This paper focuses on the controller synthesis for unknown, nonlinear systems while ensuring safety constraints. Our approach consists of two steps, a learning step that uses Gaussian processes and a controller synthesis step that is based on control barrier functions. In the learning step, we use a data-driven approach utilizing Gaussian processes...
Article
This article focuses on synthesizing control policies for discrete-time stochastic control systems together with a lower bound on the probability that the systems satisfy the complex temporal properties. The desired properties of the system are expressed as linear temporal logic specifications over finite traces. In particular, our approach decompo...
Article
The article addresses the issue of reliability of complex embedded control systems in the safety-critical environment. In this article, we propose a novel approach to design controller that (i) guarantees the safety of nonlinear physical systems, (ii) enables safe system restart during runtime, and (iii) allows the use of complex, unverified contro...
Article
Full-text available
In this paper, we study formal synthesis of control policies for partially observed jump-diffusion systems against complex logic specifications. Given a state estimator, we utilize a discretization-free approach for formal synthesis of control policies by using a notation of control barrier functions without requiring any knowledge of the estimatio...
Preprint
Full-text available
For a closed-loop control system with a digital channel between the sensor and the controller, the notion of invariance entropy quantifies the smallest average rate of information transmission above which a given compact subset of the state space can be made invariant. In this work, we present for the first time an algorithm to numerically compute...
Preprint
Full-text available
Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision trees are smaller and more explainable. We...
Preprint
Full-text available
In this paper, we consider the problem of abstraction-based controller synthesis for interconnected control systems. In general, the conventional methods for the construction of discrete abstractions and synthesis become computationally expensive due to the state and input spaces dimensions while dealing with large interconnected systems. The resul...
Preprint
Full-text available
In this paper, we provide a compositional framework for synthesizing hybrid controllers for interconnected discrete-time control systems enforcing specifications expressed by co-Buchi automata. In particular, we first decompose the given specification to simpler reachability tasks based on automata representing the complements of original co-Buchi...
Article
Synthesis of controllers for stochastic control systems ensuring safety constraints has gained considerable attention in the last few years. In this paper, we consider the problem of synthesizing controllers for partially observed stochastic control systems to ensure finite-time safety. Given an estimator with a probabilistic guarantee on the accur...
Article
In this paper, we provide for the first time an automated, correct-by-construction, controller synthesis scheme for a class of infinite dimensional stochastic systems, namely, retarded jump–diffusion systems. First, we construct finite abstractions approximately bisimilar to non-probabilistic retarded systems corresponding to the original systems h...
Preprint
Full-text available
We study formal synthesis of control policies for discrete-time stochastic control systems against complex temporal properties. Our goal is to synthesize a control policy for the system together with a lower bound on the probability that the system satisfies a complex temporal property. The desired properties of the system are expressed as a fragme...
Preprint
The paper addresses the issue of reliability of complex embedded control systems in the safety-critical environment. In this paper, we propose a novel approach to design controller that (i) guarantees the safety of nonlinear physical systems, (ii) enables safe system restart during runtime, and (iii) allows the use of complex, unverified controller...
Chapter
This paper presents a methodology for temporal logic verification of discrete-time stochastic systems. Our goal is to find a lower bound on the probability that a complex temporal property is satisfied by finite traces of the system. Desired temporal properties of the system are expressed using a fragment of linear temporal logic, called safe LTL o...
Article
Full-text available
In this paper, we consider a problem of formation control of large vehicle network and propose a systematic way to establish robust and ?efficient interaction between agents, referred as cascade formulation. The proposed formulation divides the network into smaller clusters and meta-cluster ensuring 2-rooted communication graph. We use complex Lapl...
Preprint
Full-text available
This paper presents a methodology for temporal logic verification of discrete-time stochastic systems. Our goal is to find a lower bound on the probability that a complex temporal property is satisfied by finite traces of the system. Desired temporal properties of the system are expressed using a fragment of linear temporal logic, called safe LTL o...
Article
Full-text available
ion-based synthesis techniques are limited to systems with moderate size. Thus to contribute towards scalability of these techniques, in this paper we propose a compositional abstraction-based synthesis for cascade interconnected discrete-time control systems. Given a cascade interconnection of several components, we provide results on the composit...
Conference Paper
Full-text available
In this chapter, we introduce QUEST, a new tool for automated controller synthesis of incrementally input-to-state stable nonlinear control systems. This tool accepts ordinary differential equations as the descriptions of the nonlinear control systems and constructs their symbolic models using state-space quantization-free approach which can potent...
Article
Incremental stability is a property of dynamical systems ensuring the uniform asymptotic stability of each trajectory rather than a fixed equilibrium point or trajectory. Here, we introduce a notion of incremental stability for stochastic control systems and provide its description in terms of existence of a notion of so-called incremental Lyapunov...
Article
Full-text available
In this paper, we provide for the first time an automated, correct-by-construction, controller synthesis scheme for a class of infinite dimensional stochastic hybrid systems, namely, hybrid stochastic retarded systems. First, we construct finite dimensional abstractions approximately bisimilar to original infinite dimensional stochastic systems hav...
Conference Paper
Full-text available
Incremental stability is a property that ensures the uniform asymptotic stability of each trajectory rather than a fixed equilibrium point or trajectory. This makes it a stronger stability notion for dynamical systems. Here, we introduce a notion of incremental stability for stochastic control systems and provide its description in terms of a notio...
Conference Paper
Full-text available
Incremental stability is a strong property of dynamical systems ensuring the uniform asymptotic stability of each trajectory rather than a fixed equilibrium point or fixed trajectory. Here, we introduce a notion of incremental stability for time-delayed stochastic control systems and provide a sufficient condition under which the time-delayed stoch...
Article
The paper investigates the motion control problem of Autonomous Underwater Vehicle (AUV) in three-dimensional space. Here, we consider a non-linear, nonholonomic and highly under-actuated dynamical model of AUV with six degrees of freedom. Because of its higher-dimensional complex model, the traditional model predictive control technique leads to c...
Article
In this paper, a novel neuro-fuzzy learning machine called randomized adaptive neuro-fuzzy inference system (RANFIS) is proposed for predicting the parameters of ground motion associated with seismic signals. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as i...
Conference Paper
Fuel cell is a clean energy source alternative for electric vehicle and certain microgrid operations. However to compensate its slow dynamics and inability for tracking fast load transients it needs some auxiliary storage system like battery, supercapacitor (SC) or ultracapacitor (UC). This paper considers hybrid combination of FC-UC, as it has add...
Conference Paper
The paper considers the problem of motion synchronization in multi-agent systems while maintaining a specific inter-agent formation pattern. The agents are modelled as double integrator dynamical systems with motion reference generated by a virtual leader referred to as exosystem. The main essence of this paper lies in integration of output regulat...
Article
The paper proposes a novel, simple and faster learning approach named 'Extreme-ANFIS' to tune premise and consequent parameters of Takagi-Sugeno Fuzzy Inference System (TS-FIS). Further the Extreme-ANFIS is used to design inverse model of nonlinear dynamical system. In this paper, the product concentration of non-isothermal Continuous Stirred Tank...
Conference Paper
This paper proposes a new neuro-fuzzy learning machine called extreme learning adaptive neuro-fuzzy inference system (ELANFIS) which can be applied to control of nonlinear systems. The new learning machine combines the learning capabilities of neural networks and the explicit knowledge of the fuzzy systems as in the case of conventional adaptive ne...
Thesis
Full-text available
The work entails the brief overview of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Architecture and its conventional Hybrid Learning Algorithm (HLA). Hybrid Learning Algorithm uses two passes (forward and backward pass), which is a combination of Least Square Estimate (LSE) and back propagation based on gradient descent. As it uses gradient base...
Conference Paper
Full-text available
This paper compares the performance of conventional adaptive network based fuzzy inference system (ANFIS) network and extreme-ANFIS on regression problems. ANFIS networks incorporate the explicit knowledge of the fuzzy systems and learning capabilities of neural networks. The proposed new learning technique overcomes the slow learning speed of the...

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