Robust Real-time Control Systems

1. Robust Real-time Control Systems Reliability through algorithm design, execution and system engineering Raktim Bhattacharya Assistant Professor raktim@aero.tamu.edu Department of Aerospace Engineering H.R. Bright Building, Rm. 701, Ross Street - TAMU 3141 College Station TX 77843-3141

2. Paradigm Shift in Design and Implementation of Control Systems From static offline designs to dynamic online systems that adapt in real time • Role of control algorithms is changing Dynamic Online Static Offline • Change in implementation • What is driving this?Falling cost of hardware, increasing computational power, increasingly complex control, algorithms and development of new, low cost micro sensors and actuators. • Is there a price?Yes! Need sophisticated, reliable software to manage distributed collection of components and tasks.

3. Reliability of Real-Time Control Systems Verification gap expands exponentially with complexity Consequence of the Expanding Verification Gap Less reliable products Increased failure rate in the field High cost implications Resources engaged in fire fighting Not possible to innovate No ability for growth Capability Cannot react to market changes Competition sensitive Market penetration is difficult Verification gap due to rising complexity in embedded systems. (Source:www.verisity.com) • Complexity in Embedded Systems • Cell phones : ~ 10 million lines of code. • Automobiles : ~ 100 million lines of codes. • Aerospace : ~ 1 billion lines of code. Time • Verification is Expensive • 90% time is spent on verification and validation • Cost of Failure • 100 times more in the field than in the development stage • Classification of Uncertainty in Real-time Systems • System (model error, sensor noise, etc) • Communication (delays, packet loss, etc) • Computation ( transient CPU overloads) • Product Development (software V&V) Solution? Guarantee reliability by design, execution and system engineering. How? Next slide ….

4. Uncertainty in System Design application algorithms robust to system uncertainty System Computation Communication System Engineering Uncertainty Description Model uncertainty, sensor noise, wind gust, etc. Complexity Physics. Mitigation Design controller K to guarantee robust performance. Methods Robust Control Design techniques, etc. V&V Bound on input to output norm, etc. • This is a well researched area. • Several techniques exist for robustness analysis of linear and nonlinear systems.

5. Uncertainty in Communication Design application specific transmission controller and routing algorithm to bound communication uncertainty System Computation Communication System Engineering Uncertainty Description Delays, packet loss, channel noise, multiple transmissions, etc. Complexity Information Mitigation Design controller K to mitigate communication uncertainty, robust data transmission. Methods Control with communication constraints, packet based control, filtering, etc. V&V Bound on delays, data rate, etc. Research at aero.tamu.edu • Design of Robust Communication Network • Application defines data traffic, data source & topology. • Synthesize transmission controller and routing algorithm based on communication dynamics. • Guarantee bounds on delay. • Preliminary research is based on the work by F. Kelly and G. Vinnicombe, S.Low, J.C. Doyle and F. Paganini. • Looking at data rate bounds in a dynamic topology as a switched linear system.

6. Design of Robust Communication Network Model data-rate dynamics using fluid based linear models G1(t1) G1(t2) G1(t3) t1 t2 t3 Fig1: Large Scale Network as a Composite of Small Scale Networks Fig2: Dynamic Topology – Effective Data Rate is a Hybrid System System Computation Communication System Engineering Application Design robust communication network for mobile agents engaged in surveillance. Approach 1. Use fluid based linear models to describe the dynamics of data rate for small-scale networks 2. Changing topology results in a switched linear system. 3. Model traffic load as a stochastic process. (Poisson Process, Erlang Formula, etc). 4. Analyse dynamics of node-to-node data rate. 5. Design feedback congestion control algorithm for robustly stable data rate. 6. Work based on research by F. Kelly and G. Vinnicombe, S. Low, J.C. Doyle and F. Paganini. Objective Stabilize node-to-node data rate in the presence of dynamic topology. • Assumptions • Spatial distribution and connectivity of the mobile agents is described via a graph. • The graph is assumed to be dynamic in a sense that it adapts to the movement of the agents. • The agents are constrained to satisfy certain simple dynamics, i.e. they cannot stop on a dime, etc. • The exact trajectories of the agents are governed by a higher-level algorithm that the agents are implementing; e.g. dynamic sensing algorithm, surveillance, etc.

7. Uncertainty in ComputationImplement algorithms as anytime algorithms Source: Zilberstein Research at aero.tamu.edu • Anytime Control Algorithms • In real-time systems, the utility of the decisions degrade with the time spent on computation. • The degradation in utility due to cost of time will render traditional models of computation useless real-time systems in uncertain environments. • Anytime algorithms represent a class of algorithms that can tradeoff quality of solution for computational time. • For controllers, performance is compromised for computational time during transient overloads. Stability is never compromised. • Developed preliminary results for linear time invariant controllers. System Computation Communication System Engineering Uncertainty Description Transient computational overloads, variation in execution characteristics of code, uncertainty in resource availability, etc. Complexity Time Mitigation Scheduling of CPU and other resources to guarantee execution deadline. Methods Dynamics scheduling, imprecise computation, anytime algorithms, etc. V&V Bound on runtime, etc.

8. Anytime Control AlgorithmsModel Reduction Approach Model Reduction Computational time depends on number of states rejected. System Computation Communication System Engineering Consider Linear Controllers Anytime Implementation Switch from higher order to lower order controller during transient CPU overload Results • Algorithm is tested on a linear model for longitudinal motion of a B737-100 TSRV (Transport System Research Vehicle). • Controller objective is to track flight path angle and velocity reference signal. • Able to accommodate drop in CPU resources by 35%. • The closed-loop system is robustly stable, compromised tracking performance to save CPU time.

9. Uncertainty in System Engineering Model and Platform Based Design Methodology System Computation Communication System Engineering Research at aero.tamu.edu • Robust Embedded Software Development Process • Separation of concern between various stages in the design process. • Use formal models to capture functionality and architecture. • Conduct early validation at each stage before proceeding. • Map solutions at one stage to solutions in the following stage Uncertainty Description Mismatch between requirements & implementation, verification gap, sub-component interactions, hardware-software interactions, etc. Complexity Software testing. Mitigation Regression testing, hardware in the loop testing,code coverage analysis, etc. Methods Model and platform based design of embedded software. V&V Validation of requirements with embedded software, high percentage of code coverage, etc.

10. Model and Platform Based Product DevelopmentEnabler for Engineering Effectiveness and Reliability System Computation Communication System Engineering Key Articulation Points Design Space Exploration Specifications Mapping Constraints PlatformA family of alternate solutions a) Design Flow b) Design Flowwith key articulation points c) Exploration of alternate solutions atkey articulation points • Separation of concern between various stages in the design process. • Use formal models to capture functionality and architecture. Key Principles: d) Mapping of solutions in upper layer to solutions in lower layer during integration

11. Model and Platform Based Product DevelopmentKey Benefits System Computation Communication System Engineering Examples: Separation of Architecture from Functionality Key Benefits: Mapping of Functionality to Architecture Early Response Capability

12. New Paradigm in Embedded System Design Process MBPD and the Design “V” System Computation Communication System Engineering

13. Tools for Software and Hardware Modeling Software modeling tools are more matured than hardware modeling tools. System Computation Communication System Engineering

14. Technology Maturity Who is using it? System Computation Communication System Engineering

15. Other Research Activities Guidance Algorithms for Entry Descent Landing • Apply receding horizon control methodology to achieve better guidance performance (70% improvement).

16. Other Research Activities Real-time Trajectory Generation Toolbox in MATLAB Trajectory Space Approximation B-Splines are used to transform infinite dimensional problem to finite dimensional problem. Problem Formulation Trajectory generation problem is cast as an optimal control problem of the following form: Cost: Dynamics: Constraint: Solution Process Transcribe optimal control problem to nonlinear programming problem. Test bed Blimps from Draganfly, vision based positioning, 3 fan actuation, RF controlled.

17. Questions ?