Control System Optimization in DC Motor Applications

Control system optimisation for direct-current (DC) motors underpins a vast range of modern technologies, from industrial drives and robotics to electric vehicles and precision servo systems. Central to this endeavour is the tuning of feedback regulators—most commonly proportional–integral (PI) or proportional–integral–derivative (PID) controllers—to achieve rapid settling, minimal overshoot and robust disturbance rejection. Advances in computational power and algorithmic design have enabled the integration of adaptive elements, feedforward compensation and multi-loop architectures that exploit model information and real-time error signals. In brushless DC (BLDC) motors, for instance, separate current and speed loops permit high-bandwidth torque control and precise speed regulation. Metaheuristic methods such as particle swarm, genetic and whale optimisation algorithms are now routinely applied to automate gain selection, balancing exploration of the parameter space with exploitation of local minima to yield faster convergence and improved steady-state accuracy. Concurrently, intelligent control frameworks—including fuzzy logic and neuro-fuzzy inference systems—have been fused with conventional controllers to handle nonlinearity and parameter uncertainty more effectively. These combined strategies have shown tangible reductions in settling time and error indices, underscoring the global significance of optimised DC drives in energy-efficient transportation, manufacturing automation and consumer electronics.

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Control System Optimization in DC Motor Applications publication trend

The graph below shows the total number of articles in control system optimization in dc motor applications across all publications each year (not limited to Nature Index journals).

Technical terms

Proportional–Integral (PI) controller: A feedback regulator combining proportional and integral actions to reduce steady-state error and improve disturbance rejection.

Proportional–Integral–Derivative (PID) controller: A control scheme that adds a derivative term to counteract rapid changes, enhancing transient performance.

Brushless DC (BLDC) motor: A DC motor with electronic commutation, offering high efficiency, precise speed control and low maintenance.

Metaheuristic algorithm: A high-level optimisation technique (e.g. swarm-based or evolutionary) that heuristically explores large parameter spaces.

Adaptive Neuro-Fuzzy Inference System (ANFIS): A hybrid intelligent model combining neural networks and fuzzy logic for nonlinear mapping and adaptive control.

References

1. Optimal PID controller of a brushless dc motor using genetic algorithm. International Journal of Power Electronics and Drive Systems (IJPEDS) (2019).
2. In-wheel motor control system used by four-wheel drive electric vehicle based on whale optimization algorithm-proportional–integral–derivative control. Advances in Mechanical Engineering (2022).
3. Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles. Energies (2023).