Reconfigurable Fuzzy Automaton for Software Agents

1. Reconfigurable Fuzzy Automaton for Software Agents Janos L. Grantner, Paolo A. Tamayo, Ramakrishna Gottipati, George A. Fodor Presentation By Dr. Janos L. Grantner WCCI/FUZZ-IEEE 06

2. Introduction HFB-FSM Model Intelligent Software Agents Reconfigurable Architecture Design Simulation Results Conclusion Presentation Outline

3. The problems that characterize industrial process control innovation are: Introducing new knowledge into a system Activating stored domain knowledge in an autonomous way Validating the knowledge Recovering the system if the new, activated knowledge is not suitable to handle the situation Introduction

4. These problems can be addressed using intelligent software agents with fuzzy automata New knowledge can be implemented by adding agents New knowledge is introduced by means of states in the goal path of an event driven, sequential control algorithm Fuzzy automata is an effective approximation method to model continuous and discrete signals in a single theoretical framework Knowledge validation is achieved By quantifying the degree of deviation from the nominal operating conditions due to unexpected events Execution monitoring is also performed with fuzzy automata Introduction (contn’d)

5. Intelligent Software Agent ARCHITECTURE To FSA-BROKER: architecture, Supervision, real-time All ports are bi-directional To ALARM SERVER To HMI Server Commissioning Panel Object State Application All ports have a named type Fuzzy Automaton Connection to other objects APPLICATION

6. IP (Intellectual Property) modules are designed as generic fuzzy automaton agents Agents communicate via NoC (Network on Chip) to decrease the real estate needed for pathways on the chip Agent broker can be implemented on an FPGA A set of specialized architecture operations are needed to implement an agent broker on an FPGA Example of such implementation: NoC Hardware Implementation of Agents

7. Synthesis of Network on Chip (NoC) • Input: IP components with cost figures: U1, U2, U3, V1, V2, V3 • Clustered constraints (clustering is NP-complete): U1,2,3 is one cluster, V1,2,3 is another cluster • Communication Constrained Graph: Ui communicates with Vi, i=1,2,3 • Optimal synthesis – quadratic programming approach: have only one communication channel • Method: CDCS (constrained driven communication synthesis) • At present, software implementation takes minutes v1 v1 u1 v2 v2 u1 u2 u2 v3 v3 u3 u3

8. HFB-FSM Model

9. It is based upon the computational model of HFB-FSM Assumes a multi-fuzzy input and one fuzzy output (MISO) configuration Digital inputs and analog inputs with threshold are omitted at this point Each fuzzy input is mapped to a set of Boolean variables using the B-Algorithm(Fuzzy-to-Boolean mapping) Example for Designing a Reconfigurable Fuzzy Automaton

10. k overlapping linguistic sub – intervals are mapped to n (n = 2k-1) non overlapping Boolean sub – intervals, and Xbi = 1 if the xc position of the fuzzy input maximum falls into Boolean sub – interval i(i = 1,…,n) and XBj = 0 for all j = i(j = 1,……,n) Example (Contn’d)

11. Example (Contn’d)

12. Reconfigurable Architecture Design (Contn’d)

13. Example (Contn’d)

14. Example (Contn’d)

15. Example (Contn’d)

16. Example (Contn’d)

17. Example (Contn’d)-Parameters

18. Validation • Container Crane Problem • The Container-Crane problem simulates the operation of transferring a container van from a ship into a railcar platform. The Container-Crane problem is developed using the fuzzyTech software

19. Validation (Contn’d) • Container Crane Problem • The two fuzzy inputs are • Angle of displacement of the suspended load (X) • Left swing results in a negative angle • Right swing results in a positive angle • Distance of the load from the rail car (Y) • Far , Near , Close (also the states of the system) • Output is the power applied to the crane (Z) • Positive power, negative power and zero power • A simplified HFB-FSM will have 3 states, each of which will be made of just one crisp state

20. Validation (Contn’d)

21. Validation (Contn’d) • Normalized universal set

22. Validation (Contn’d)

23. Validation (Contn’d) • Simulation

24. Validation (Contn’d) Inference and Model Building Operation Performance Summary Where : K is the constant overhead cycles when performing the operation, currently 4 clock cycles. S is the number of States

25. For the example: Inference will be N+K = 7 + 4 = 11 clock cycles. Model Building will be (SxNxNxR) + 1+K = (3x7x7x3) + 1+4 = 446 clock cycles At 100MHZ clock rate we can run approximately 220,000 Model Building Operations and 10 Million Inferences per second Validation (Contn’d)

26. An intelligent software agent architecture with fuzzy automaton was introduced Online reconfiguration of this architecture is needed to introduce new knowledge and for fault detection and identification and recovery IP (Intellectual property) modules are implemented on hardware in contemporary control systems Hardware implementation of a reconfigurable fuzzy automaton was presented. Conclusion