Control system encompasses the computational and hardware subsystems that sense environmental state, evaluate performance against desired objectives, and generate actuation commands to regulate robot behaviour toward goals.
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A control system in robotics executes a perception-decision-action loop where sensors provide observations of the robot and its environment, a Control Algorithm processes these measurements relative to desired state or trajectory, and the controller outputs commands to Actuators that produce physical motion. Simple control systems employ fixed control laws like PID Control; advanced systems integrate Machine Learning Discipline, Model Predictive Control, and Reinforcement Learning to adapt control strategy to changing task demands and environmental conditions.
Control systems must operate under hardware constraints—limited computational resources, sensor noise and latency, actuation bandwidth limits, and energy availability—whilst ensuring physical safety, task performance, and robustness to disturbances and model uncertainty. Real-Time Operating Systems provide deterministic scheduling for time-critical control loops. Hierarchical control architectures separate high-level task planning from low-level reflexive control, enabling flexible behaviour expression whilst maintaining stability guarantees.
Contemporary research addresses adaptive control that learns system dynamics online, meta-learning approaches enabling rapid task adaptation, formal verification of safety properties for safety-critical applications, and integration of human input through Teleoperation and Learning from Demonstration. Multi-agent control systems explore decentralised control strategies where each agent computes commands based on local information whilst achieving collective objectives through properly designed Communication Protocols and consensus algorithms.