Non-Autonomous Feedback Control for Area Coverage Problems With Time-Varying Risk

2016 ◽  
Author(s):  
Suruz Miah ◽  
Mostafa M. H. Fallah ◽  
Arian Y. Panah ◽  
Davide Spinello
Keyword(s):  
Mobile Agents ◽  
Optimization Problems ◽  
Area Coverage ◽  
Control Law ◽  
Time Varying ◽  
External Threats ◽  
Barbalat’S Lemma ◽  
Diffusion Mechanisms ◽  
Area Surveillance ◽  
Coverage Problems

Motivated by area coverage optimization problems with time varying risk densities, we propose a decentralized control law for a team of autonomous mobile agents in a two dimensional area such that their asymptotic configurations optimize a generalized non-autonomous coverage metric. The generalized non-autonomous coverage metric explicitly depends on a nonuniform time-varying measurable scalar field that is not directly controllable by agents. Several interesting scenarios emerge with time varying risk density. In this work, we consider the case of area surveillance against moving targets or external threats penetrating through the perimeter, and the case of environmental monitoring and intervention with deployment of mobile sensors in areas affected by penetration of substances governed by diffusion mechanisms, as for example oil in a marine environment. In the presence of time-varying risk density the coverage metric is non-autonomous as it includes a time varying component that does not depend on the evolution of the agents. Our non-autonomous feedback law accounts for the time-varying component through a term that vanishes when the risk eventually stops evolving. Optimality with respect to the induced non-autonomous coverage is proven in the framework of Barbalat’s lemma, and the performance is illustrated through simulation of the these two scenarios.

Finite-time control of uncertain time-varying systems in p-normal form

2020 ◽  
Author(s):  
Kanya Rattanamongkhonkun ◽  
Radom Pongvuthithum ◽  
Chulin Likasiri
Keyword(s):  
Normal Form ◽  
Finite Time ◽  
Control Law ◽  
Time Varying ◽  
Time Control ◽  
Special Cases ◽  
Time Varying Systems ◽  
A Chain ◽  
Uncertain Time ◽  
Power Integrator

Abstract This paper addresses a finite-time regulation problem for time-varying nonlinear systems in p-normal form. This class of time-varying systems includes a well-known lower-triangular system and a chain of power integrator systems as special cases. No growth condition on time-varying uncertainties is imposed. The control law can guarantee that all closed-loop trajectories are bounded and well defined. Furthermore, all states converge to zero in finite time.

Robust H∞ control of neutral system for sampled-data dynamic positioning ships

2018 ◽  
Vol 36 (4) ◽  
pp. 1325-1345 ◽  
Author(s):  
Minjie Zheng ◽  
Yujie Zhou ◽  
Shenhua Yang ◽  
Lina Li
Keyword(s):  
Sufficient Conditions ◽  
Control Law ◽  
Dynamic Positioning ◽  
Neutral System ◽  
Time Varying ◽  
Sampled Data ◽  
Stability Theorems ◽  
Data Control ◽  
Conservative Result ◽  
Data Controller

Abstract This study focuses on the robust ${H}_{\infty }$ sampled-data control problem of neutral system for dynamic positioning (DP) ships. Using the input delay approach and a state-derivative control law, the ship DP system is turned into a neutral system with time-varying delays. By incorporating the delay-decomposition technique, Wirtinger-based integral inequality and an augmented Lyapunov–Krasovskii functional, less conservative result is derived for the resulting system. Sufficient conditions are established to determine the system’s asymptotical stability and achieve ${H}_{\infty }$ performance using Lyapunov stability theorems. Then the ${H}_{\infty }$ sampled-data controller is obtained by analyzing the stabilization conditions. Finally, simulation result is shown that the proposed method is effective.

scholarly journals Dynamic Optimization of a Subcritical Steam Power Plant under Time-Varying Power Load

2018 ◽  
Author(s):  
Chen Chen ◽  
George M. Bollas
Keyword(s):  
Power Plant ◽  
Dynamic Optimization ◽  
Electricity Market ◽  
Optimization Problems ◽  
Optimal Time ◽  
Time Varying ◽  
Power Demand ◽  
Test Bed ◽  
Simulation And Optimization ◽  
Power Load

The increasing variability in power plant load, in response to a wildly uncertain electricity market and the need to to mitigate CO2 emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort, and the subject of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model is used as a test-bed for dynamic simulations, in which the coal load is regulated to satisfy a varying power demand. Plant-level control regulates plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant operating at varying load is optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems are formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points is shown feasible with corresponding savings in coal consumption of 184.8 tons/day and carbon footprint decrease of 0.035 kg/kWh.

Discrete-Time Sliding Mode Controller With Time-Varying Band for Unfixed Sampling-Time Systems

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Chidentree Treesatayapun
Keyword(s):  
Discrete Time ◽  
Sliding Mode ◽  
Fuzzy Rule ◽  
Current Control ◽  
Sampling Time ◽  
Control Law ◽  
Time Varying ◽  
Control Effort ◽  
Time Systems ◽  
The Mathematical Model

An adaptive discrete-time controller is developed for a class of practical plants when the mathematical model is unknown and the sampling time is nonconstant or unfixed. The data-driven model is established by the set of plant's input–output data under the pseudo-partial derivative (PPD) which represents the change of output with respect to the change of control effort. The multi-input fuzzy rule emulated network (MiFREN) is utilized to estimate PPD with an online-learning phase to tune all adjustable parameters of MiFREN with the convergence analysis. The proposed control law is developed by the discrete-time sliding mode control (DSMC), and the time-varying band is established according to the unfixed sampling time and unknown boundaries of disturbances and uncertainties. The prototype of direct current-motor current control with uncontrolled-sampling time is constructed to validate the performance of the proposed controller.

scholarly journals Low-Complexity Adaptive Saturated Control of a Class of Nonlinear Systems with Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Gang Zhang ◽  
Deqiang Cheng ◽  
Qiqi Kou
Keyword(s):  
Nonlinear Systems ◽  
Dead Zone ◽  
External Disturbance ◽  
Original System ◽  
Low Complexity ◽  
Control Law ◽  
Zone Model ◽  
Time Varying ◽  
Saturated Control ◽  
Output Constraint

This paper investigates a low-complexity saturated control law for a class of nonlinear systems with consideration of the time-varying output constraint, control constraint, and external disturbance. First, a dead-zone model is employed to transform the control saturation nonlinearity into a linear one with respect to the real input signal. Then, the original system with time-varying output constraint is transformed into a constraint-free one, based on which a novel adaptive saturated control law is devised along the filtered error manifold. By employing minimum learning parameter technique and virtual error concept, only two adaptive parameters are needed to update online, which reduces the computational burdens dramatically. Finally, the applications to Duffing-Holmes chaotic system are organized to validate the effectiveness of the proposed control law.

Decentralized control for mobile robotic sensor network self-deployment: barrier and sweep coverage problems

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 283-294 ◽  
Author(s):  
Teddy M. Cheng ◽  
Andrey V. Savkin
Keyword(s):  
Sensor Network ◽  
Decentralized Control ◽  
Nearest Neighbor ◽  
Barrier Coverage ◽  
Control Law ◽  
Motion Coordination ◽  
Robotic Sensor ◽  
Information Consensus ◽  
Coverage Problems ◽  
Robotic Sensors

SUMMARYThis paper addresses the problems of barrier coverage and sweep coverage in a corridor environment with a network of self-deployed mobile autonomous robotic sensors. Using the ideas of nearest neighbor rules and information consensus, we propose a decentralized control law for the robotic sensors to solve the coverage problems. Numerical simulations illustrate the effectiveness of the proposed algorithm. The results in this paper demonstrate that such simple motion coordination rules can play a significant role in addressing the issue of coverage in a mobile robotic sensor network.

scholarly journals Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-29 ◽  
Author(s):  
Pedro M. Vallejo LLamas ◽  
Pastora Vega
Keyword(s):  
Wastewater Treatment ◽  
Predictive Control ◽  
Treatment Process ◽  
Linear Time ◽  
Real Data ◽  
Control Law ◽  
Time Varying ◽  
Wastewater Treatment Process ◽  
Starting Point ◽  
Fuzzy Predictive Control

A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time-varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow-up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.

Sign in / Sign up

Export Citation Format

Share Document