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

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
Verification gap due to rising complexity in embedded systems. (Source:
Time
Capability
Not possible to innovate
No ability for growth
Less reliable products
Increased failure rate in the field
High cost implications
Resources engaged in fire fighting
Cannot react to market changes
Competition sensitive
Market penetration is difficult
Consequence of the Expanding Verification Gap
Complexity in Embedded Systems
Cell phones : ~ 10 million lines of code.
Automobiles : ~ 100 million lines of codes.
Aerospace : ~ 1 billion lines of code.
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
Communication
Computation
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
Communication
Computation
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
System
Communication
Computation
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.
Fig2: Dynamic Topology – Effective Data Rate is a Hybrid System t1 t2 t3
G1(t1)
G1(t2)
G1(t3)
Fig1: Large Scale Network as a Composite of Small Scale Networks

7. Uncertainty in Computation Implement algorithms as anytime algorithms
System
Communication
Computation
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.
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.

8. Anytime Control Algorithms Model Reduction Approach
System
Communication
Computation
System Engineering
Consider Linear Controllers
Model Reduction
Computational time depends on number of states rejected.
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 B 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
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.
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
Research at aero.tamu.edu

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

11. Early Response Capability
Model and Platform Based Product Development Key Benefits
System
Computation
Communication
System Engineering
Examples:
Separation of Architecture from Functionality
Key Benefits:
Mapping of Functionality to Architecture
Capability
Benefits
Early Validation
Reduced turn backs, higher reliability
Platform Flexibility
Lower cost & obsolescence insensitivity
Reuse
Faster development time
Analysis
Quantification of quality & efficiency
Early
Response
Capability

12. New Paradigm in Embedded System Design Process MBPD and the Design “V”
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 Communication Computation
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
Problem Formulation
Trajectory generation problem is cast as an optimal control problem of the following form:
Trajectory Space Approximation
B-Splines are used to transform infinite dimensional problem to finite dimensional problem.
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 ?