Petri Net Controller Research Articles

A Set Covering Approach to Design Maximally Permissive Supervisors for Flexible Manufacturing Systems

The supervisory control of Petri nets aims to enforce the undesired behavior as unreachable by designing a set of control places. This work presents a set cover approach to design maximally permissive supervisors. For each first-met bad marking, an integer linear programming problem is developed to obtain a control place to prohibit it. An objective function is formulated to make the maximal number of first-met bad markings forbidden. Then, we develop a set covering approach to minimize the number of selected control places. The proposed approach can guarantee the maximal permissiveness of the obtained supervisor and provide a trade-off between structural complexity and computational cost. Several examples are considered to validate the proposed method.

Supervisory control of quantitative Petri nets for fixed‐initial‐credit energy problems using a game structure

AbstractThis work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency.

Tailoring the motion planning of humanoids in complex arena: a regression-firefly-based approach

AbstractIn the current investigation, a two-stage hybridization model has been used for the motion planning of humanoids in complex environmental conditions using regression analysis and the firefly algorithm. In the first step, sensory outputs are fed to the regression model, and an initial turning angle (ITA) is obtained. In the second step, the ITA is again fed as input to the firefly model along with other required inputs, and the final turning angle (FTA) is obtained. The FTA serves as the guiding parameter for the humanoids to reach their desired destinations. The developed motion planning scheme has been implemented on NAO humanoid robots in simulation and experimental platforms. A Petri-Net control strategy has been integrated along with the hybrid scheme while negotiating multiple humanoids in a common platform. The results obtained from the motion planning analysis in simulation and experimental arenas are compared against each other in terms of selected navigational parameters and observed satisfactory agreements. Finally, the proposed hybrid controller is also tested against another standard navigational model and substantial enhancement in the performance has been noticed.

Design of Safety Petri Net Controllers for Deadlock Prevention at a Class of Road Intersections

Design of Safety Petri Net Controllers for Deadlock Prevention at a Class of Road Intersections

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

This article introduces a novel and effective diagram based on direct instantaneous power control (DPC) of a PWM-controlled rectifier connected to the grid without a switching table. An optimum control vector of the PWM rectifier's input voltage, which depends on the switching states determined by a Petri nets controller, is adopted. This approach limits the instantaneous detection errors of reactive and active powers, maintains the DC bus voltage at a reference level, and ensures current close to a sinusoidal wave, guaranteeing operation at a unit power factor. The instantaneous tracking errors of active and reactive powers and the angular position of the voltage are used as input variables for the proposed controller, which then selects the best control vector for the converter based on the transition of a Petri net. The significant advantages of DPC based on Petri nets compared to traditional switching tables are that hysteresis comparators are not required, and the classical regulation of active and reactive powers is achieved in all sectors. Simulation and testing findings demonstrated excellent performance, supporting the viability of the suggested control approach using Petri nets.

Smart Fuzzy Petri Net-Based Temperature Control Framework for Reducing Building Energy Consumption.

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems.

Low-energy residential building optimisation for energy and comfort enhancement in semi-arid climate conditions

The application of energy-efficient strategies in buildings, such as the Green Building Concept, can significantly impact human comfort and resource consumption. However, due to the complexity of decision-making factors and the variety of available materials, computational models are necessary to identify the most effective solutions and optimise building energy performance. This study presents an integrated framework that uses machine learning algorithms and a Petri Net control system to optimise the thermal, comfort, and energy efficiency of both vertical and horizontal building envelopes in semi-arid climate zones. The framework incorporates several passive techniques for building energy parameters, including material thickness and melting point, window types, wall insulation thickness and thermal emissivity, wall solar absorbance, window wall ratio, fenestration position, air tightness, roof solar reflectance, roof insulation thickness and conductivity (W/(m·°C)), and floor insulation thickness. An experiment design was developed using Box-Behnken Design-Response Surface Methodology (BBD-RSM) for statistical optimisation, which was coupled with Design Builder simulation model. The methodology was demonstrated by applying it to a residential building in Mexico. Meta Additive Regression was used to analyse the output factors, which showed higher confidence compared to REP Tree and M5P Tree algorithms in green buildings. The results demonstrate that an annual energy reduction of 50 kW/m2 per household can be achieved by using an optimised building envelope.

Robust Petri Net Controllers for Flexible Manufacturing Systems With Multitype and Multiunit Unreliable Resources

It is inevitable that resource failures occur in real production processes, and sometimes many different types of unreliable resource failures may occur. Once there are resource failures, the stagnation states of production caused by these failures, called as failure blockings (FBs), tend to appear. Hence, both deadlocks and FBs can arise to reduce production efficiency sharply. Designing robust control policies for such an automated system is thus very important. Our work focuses on a novel robust Petri net controller of flexible manufacturing systems with multitype and multiunit of unreliable resources. First, under the assumption that all resources are reliable, these systems are modeled by a class of Petri nets called systems of simple sequential processes with resources (S3PRs). For each operation place that is a holder of an unreliable resource, the corresponding repair place and related transitions are added to the net and an S3PR with unreliable resources (S3PRu) is developed. For an S3PRu, the formal definition of FBs is then proposed. Such an FB is characterized by a maximal perfect resource-transition circuit (MPC). Next, the concept of a critical set of MPCs is introduced. For any two adjacent MPCs in such a critical set, one input transition of the former and one output transition of the latter are connected by passing through resource places. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\xi $ </tex-math></inline-formula> -resource is a one-unit resource shared by two or more MPCs that do not contain each other. For an S3PRu without <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\xi $ </tex-math></inline-formula> -resources and critical sets of MPCs, a control place corresponding to an MPC is added to the net with its output arcs to the input transitions of the MPC. However, for an S3PRu with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\xi $ </tex-math></inline-formula> -resources or critical sets of MPCs, the output arcs of each control place are added to the source transitions of the original net. Thus, a novel robust controller of an S3PRu is synthesized. Such a robust controller can guarantee that as long as at least one unit of each type of unreliable resources can work normally, all kinds of parts can be processed to complete smoothly their tasks through any one of their process routes. Finally, three examples are provided to illustrate the efficiency of the proposed robust controller.

Multi-target trajectory planning and control technique for autonomous navigation of multiple robots

The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption.

Towards stabilization and navigational analysis of humanoids in complex arena using a hybridized fuzzy embedded PID controller approach

In this study, path planning and stabilization of humanoids are carried out in an uneven path and dynamic environment. The importance of the work focuses on avoiding local minima and trapping in dead-ends during navigation. The sole purposes of this research are to i) Stabilize the humanoid on an uneven surface. ii) Develop proper path planning for the humanoid so that it can adequately navigate through obstacle-rich terrain. For achieving the above said objectives, the robot's controller is designed by tuning the Proportional-integral-derivative (PID) controller with a fuzzy logic controller (FLC) for better performance. The PID controller does joint angle control and torque control respectively. In contrast, a complicated task like generating optimized turning angle (OTA) and adjusting feet angle during stepping upon an uneven path is done by FLC. Gait generation during stepping on and off an uneven surface for the humanoid robot is discussed and implementation of the inverted pendulum plus flywheel method (LIPPFM) is used for the analysis of the dynamics of motion and removing the height constraint (center of mass) where the upper body is considered as the mass of the pendulum. Petri net controller is used to navigate humanoids in an environment with multiple humanoids. To examine the proposed controller’s performance, the controller undergoes testing in simulation and experimental set up, and the obtained results are compared with recently developed techniques. V-REP software is used for conducting the simulation with an arena-size of 240*160 dimensions to test the effectiveness of the developed controller. A less than 5 % deviation is found because of friction and signal delay is noticed between simulation and the experimental result obtained for navigation while comparing to the previously developed technique such as PEM. There is a significant improvement in path length by 10.49 % is noticed and a decrease of 2.98 % in computational time is noticed. When the navigation parameter of the FUZZY-PID controller is compared with Genetic Potential Field (GPF), Pseudo Bacterial Potential Field (PBPF), and Bacterial Potential Field (BPF), there is an improvement of 15.41 %, 12.51 %, and 8.56 % respectively are noticed. It has been seen that the FUZZY-PID controller minimizes the settling time and lower the peak overshoot. When the humanoid robot crosses the uneven path using the proposed FUZZY-PID controller, the graph obtained is smoother than the previously developed technique. It displays the body attitude angle falling between less than ±1.5 degrees compared to ±2 degrees by the previously developed method which justify the selection of the FUZZY-PID controller.

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

AbstractThe given article emphasizes the development and modeling of a hybrid navigational controller to optimize the path length and time taken. The proposed navigational controller is developed by hybridizing the metaheuristic moth–flame optimization (MFO) approach and the reinforcement learning (RL) approach. Input parameters like obstacle and target locations are fed to the MFO controller that implements a proper navigational direction selection. It forwards to the RL controller, which exercises further refinement of the output turning angle around obstacles. The collaboration of the global MFO approach with the local-based RL approach helps to optimize the path traversed by the humanoid robot in an unknown environment. The major breakthrough in this article is the utilization of humanoid robots for navigation purposes between various checkpoints. The humanoid robots are placed in a cluttered environment and assigned specific target positions to complete the assigned tasks. In the case of a multi-humanoid robot system, to avoid self-collision, it requires a Petri-Net controller to be configured in the navigation system to prevent deadlock situations and enhance the smooth completion of tasks without inter-collision among the humanoid robots. Simulations and real-time experiments are undertaken using different controllers involving single- and multi-humanoid robot systems. The robustness of the proposed controller is also validated in dynamic environment. Comparisons are carried with an established navigational controller in a similar environmental setup, which proves the proposed hybrid controller to be robust and efficient.

Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

AbstractRobotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

On Optimal Supervisor Design for Discrete-Event Systems Modeled With Petri Nets via Constraint Simplification

With integer linear programming problems (ILPPs) being formulated and solved, the existing approaches design optimal Petri-net supervisors via nonpure net structures, including self-loops and data inhibitor arcs. Nonpure net structures are powerful for control of Petri-net-modeled discrete-event systems. However, in the existing work, the formulated ILPPs contain a large number of constraints, which is computationally inefficient. In this article, we propose approaches that formulate ILPPs with fewer constraints such that the computational efficiency is significantly improved. To do so, in formulating ILPPs for optimal Petri-net controllers by using self-loops and data inhibitor arcs, we remove the reachability conditions for legal markings. By doing so, an obtained solution may result in some legal markings unreachable. To solve this problem, a novel technique is developed to design an optimal controller by modifying the initial marking and structure of the obtained supervisor. It is shown that, by the reduced ILPPs, one can find the same feasible solutions as that obtained by the existing work. Finally, the proposed approaches are demonstrated by examples.

Optimal Control of Timed Petri Nets Under Temporal Logic Constraints with Generalized Mutual Exclusion

Optimal Control of Timed Petri Nets Under Temporal Logic Constraints with Generalized Mutual Exclusion

On the Design of Regulation Controllers for Automation Systems with RCPetri

Regulation control for Petri nets is a control framework that allows the design of sequence controllers for automation systems in a systematic and efficient way. In order to implement this control framework, the MATLAB® app RCPetri has been developed. In this work, the RCPetri tool functionalities are described, including model generation, automatic specification generation, automatic control design, model and control simulation, automatic translation to PLC code, and communication by Modbus TCP/IP and OPC UA. Furthermore, three examples are presented to illustrate the application of the tool and the regulation framework: an electro-pneumatic device, a process control system, and a robotic manufacturing cell under a decentralized control scheme.

Towards motion planning of humanoids using a fuzzy embedded neural network approach

This research work focuses on navigational strategy of humanoid robots in complex environments using a fuzzy embedded neural network based controller. The obstacle distances are measured from robot’s current position and referred as front obstacle distance, right obstacle distance and left obstacle distance. These obstacle distances are served as input variables to the neural network model, and target angle is obtained as output parameter. The target angle obtained from neural network is fed to the Mamdani fuzzy system along with the obstacle distances as input variables to obtain the effective target angle for the humanoid robot. A Petri-net controller is embedded with developed neuro-fuzzy controller to perform dynamic path analysis in complex workspaces Single as well as multiple humanoid robots are used to analyze simulation and experimental navigation in different complex environments using developed neuro-fuzzy-petri-net controller. Various simulations are carried out using V-REP simulation software and similar scenario as per simulation is developed under laboratory conditions for various experimental navigation. The results from both the scenarios are related and are found to be in good covenant with each other having permissible range of errors. Simulation and experimental results in relation to navigational parameters shows the robustness of the developed controller. Surface plots and contour plots developed from the designed controller shows the effectiveness and efficacy in achieving global path during motion planning through optimizing target angle. To validate the results and to find out the effectiveness, the developed controller is compared with existing techniques such as IDQ and substantial progress of 16.66% in relation to path length is observed.

An Improved Synthesis Method Based on ILPP and Colored Petri Net for Liveness Enforcing Controller of Flexible Manufacturing Systems

Petri nets are used to design deadlock control strategies for flexible manufacturing systems (FMS), which typically involve the addition of monitors and the associated arcs to the FMS. The addition of several monitors and associated arcs to first constructed Petri net model significantly complicates the Petri net controller. This paper develops a two-step method for preventing deadlocks based on a colored Petri net and a structurally minimal approach that significantly reduces the number of monitors. In the first step, a vector covering technique is applied to generate a minimal covered sets of first-met bad markings (FBMs) and legal markings that are respectively smaller than the sets of FBMs and legal markings. At one iteration, place invariants (PIs) corresponding to monitors are constructed by solving an integer linear programming problem (ILPP) to prohibit the maximum number of FBMs, while allowing all legal markings in the minimal covering set. The purpose of the ILPP is to maximize the number of FBMs forbidden by the PIs. Then, based on a colored Petri net, all generated monitors are combined into a global control place. Therefore, a supervisor with minimal structural complexity can be constructed. The obtained net model is controlled after the addition of the designed supervisor. Two instances from the literature are considered to illustrate the proposed approach.

A General Petri-Net Controller for Avoiding Collisions and Deadlocks in a Partially-Controllable Mixed Traffic System

A General Petri-Net Controller for Avoiding Collisions and Deadlocks in a Partially-Controllable Mixed Traffic System

Switching models and control of Petri Nets with shared resources under marking constraints

ABSTRACT Nowadays multi-option phenomena can generate potential problems related to industrial manufacturing systems. For instance, time and money loss are both expensive costs that should be paid when failing to make the right control choice decision, especially given the existence of various constraints. Therefore, the shared resource allocation between several processes can be transformed into a switching control problem that can be modeled using (Min, +) algebra. Due to the finite number of states and ergo of the application of this type of system, they are defined under the class of Discrete Event Systems. Accordingly, while taking into consideration modes of representation, this paper seeks to discuss a new modelling approach for competitive Timed Events Graphs under capacity constraints. The deduced switching models are then examined to solve the control problem issue of an industrial manufacturing process composed of cells of pick and place robots. To deduce the adequate control laws, an original solution based on the use of linear equations described by (Min, +) linear algebra is provided.

Supervisory Control of Petri Nets in the Presence of Replacement Attacks

This article addresses the robust control problem of discrete event systems assuming that replacement attacks may occur, thus making it appear that an event that has occurred looks like another event. In particular, we assume that this is done by tampering with the sensor-readings in the sensor communication channel. Specifically, we use Petri nets as the reference formalism to model the plant and assume a control specification in terms of a generalized mutual exclusion constraint. We propose three different methods to derive a control policy that is robust to the possible replacement attacks. The first two methods lead to an optimal (i.e., maximally permissive) policy but are computationally inefficient when applied to large-size systems. On the contrary, the third method computes a policy more efficiently and reveals more easily implementable in practice. However, this is done at the expense of optimality.