Control systems

Sensors: receive signals (microphones, cameras…)

Actuators: actually do stuff in the ’real world’ (LEDs, motors, speakers, displays, lamps…)

Controller: “the brain”, the intelligent unit. provide hardware & software that makes system autonomous by using sensor input etc.

any pervasive computing system executes sense-control-act sequence in a loop

Control:

• deliberative: think hard, act later
  • planning — look ahead at outcomes of possible actions
  • searching — looking for sequence of actions leading to desired goal
  • use internal representation of the environment — a map, for example
  • for decisions, use for example shortest path from one node to another in a map
    • uses Dijkstra’s or A* algorithm
    • GPS nav systems use this
• reactive: don’t think, react!
  • e.g. smart curtains, thermostat, obstacle handling, landmark navigation
  • don’t use internal representation, just direct mapping between sensors and effectors
  • rules:
    • if dark outside, then close the curtains
  • control type
    • open-loop control — input signal to controller, actuator, output controlled variable
      • examples: microwave, automatic lights, automatic water faucets
    • closed-loop control — get feedback, check if everything was executed right
      • uses a comparator that gets feedback from output of actuator
      • comparator outputs error to controller, which then tries to minimise error
      • example: heater
  • obstacle handling
    • simple — contact (touch sensor)
    • better — proximity, but don’t know distance (whiskers)
    • best — ranging
      • sonar, with reflected sound waves. echolocation (distance = speed × time)
      • LIDAR, using a laser swept across FOV
  • landmark navigation
    • follow a line, a wall
    • feedback control — turn always same angle, turn proportionally (P), proportional derivative (PD, rate of change), or proportional derivative and integral (PID, rate of change and time)
• others: hybrid, behaviour-based