Pushpak Jagtap

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

Autonomous exploration in an unknown environment is widely studied, and many exploration strategies exist. However, in most of the works, safety is not usually given top priority. The reason behind the violation of safety by most of the exploration algorithms in real-world applications is the ignorance of some or all of the following factors (i) th...

This paper is concerned with a formal verification scheme for both discrete- and continuous-time deterministic systems with unknown mathematical models. The main target is to verify the safety of unknown systems based on the construction of barrier certificates via a set of data collected from trajectories of systems while providing an a-priori gua...

Motivated by the concept of Quasi-Sliding Mode (QSM) in discrete-time systems, this paper presents a novel approach that relaxes the requirement of ubiquitous exact sliding motion in continuous-time systems, aiming to achieve an approximation- and chattering-free quasi-sliding mode controller (QSMC). The proposed QSMC provides robust and chattering...

This paper addresses the problem of tracking control for an unknown nonlinear system with time-varying bounded disturbance subjected to prescribed Performance and Input Constraints (PIC). Since simultaneous prescription of PIC involves a trade-off, we propose an analytical feasibility condition to prescribe feasible PIC which also yields feasible i...

The paper focuses on the problem of formal synthesis of controllers for control-affine nonlinear systems against complex properties. Our goal is to design a closed-form control policy that guarantees the satisfaction of complex properties that are expressed using ( ω )-regular languages and equivalently recognized by Non-deterministic Büchi Automat...

In this paper, we propose a computationally efficient symbolic controller synthesis technique for multi-agent systems. The paper focuses on synthesizing distributed controllers enforcing local temporal logic specifications along with global safety specifications for multi-agent systems. To solve the problem in a computationally efficient way we lev...

This paper formulates and studies the concepts of approximate (alternating) bisimulation relations characterizing equivalence relations between interconnected systems and their abstractions. These equivalence relations guarantee that the symbolic model conserves the original model's dynamics. We develop a compositional approach for abstraction-base...

This paper aims to synthesize a reachability controller for an unknown dynamical system. We first learn the unknown system using Gaussian processes and the (probabilistic) guarantee on the learned model. Then we use the funnel-based controller synthesis approach using this approximated dynamical system to design the controller for a reachability sp...

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...

This paper focuses on the problem of distributed consensus control of multi-agent systems while considering two main practical concerns (i) stochastic noise in the agent dynamics and (ii) predefined performance constraints over evolutions of multi-agent systems. In particular, we consider that each agent is driven by a stochastic differential equat...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...