Tension control algorithms used in electrical wire manufacturing processes: a systematic review

Figures & data

Figure 1. Copper wire drawing process schematic diagram.

Figure 2. Schematic diagram of the wire drawing process with payoff, take-up and dancer mechanism.

Figure 3. Complete system model of wire tension system with dancer dynamics.

Figure 4. Schematic diagram of the control algorithms applied in tension control systems.

Figure 5. Tension control with PI controller.

Figure 6. Model predictive controller basic circuit design.

Figure 7. Schematic diagram of ADRC.

Figure 8. Conceptual diagram of sliding mode control.

Figure 9. H∞ Control schematics.

Figure 10. Illustration of the adaptive control system.

Figure 11. Schematic diagram of fuzzy logic controller.

Figure 12. Artificial neural network architecture. (a) Neuron, (b) Artificial neural network.

Figure 13. Flow chart of genetic algorithm.

Figure 14. ANFIS architecture.

Figure 15. Schematic diagram of the hybrid control algorithm.

Table 1. Advantages and limitation of the various control algorithms.

Control method	Advantages	Limitations
Manual/ mechanical control	It is simple to implement. Low initial cost. Simple structure.	Low control precision. Unable to reach steady state. High maintenance cost. Low speed and low efficiency. Not accurate in setpoint tracking.
Conventional PID control	Dominant control technique in web tension control. Simple in construction and easy to design. Reduced computational load. Cost effective and easy to implement. Steady state error reduction can be attained by increasing parameter gains.	Use of mathematical model of the system for effective control. Produces oscillations and takes a longer time to settle at steady state. Not suitable for nonlinear, complex, uncertain and time varying systems. Instability of the system when PID gains are not tuned properly. Noise has a negative impact on performance.
Active disturbance rejection control	Simple control structure. Robustness against plant uncertainties and external disturbances. Characterised by fast response speed, high accuracy and minimum overshoot. Capability to estimate and compensate the overall effects of uncertainties. Control design does not depend on the model of the plant.	Unsuitable for systems with large time delay. Proper tuning of the ESO is key to its performance. Unproven convergence and stability criterion. Exact correlation between the tuning parameters of the control system and the performance requirements is difficult to ascertain.
Sliding mode control	Rapid convergence. Simple algorithm and strong robustness to parameter uncertainties. Applicable on wide range of nonlinear and higher order systems. External disturbance rejection capability. It can handle sudden and large changes in the system dynamics. Simplification of system dynamics in terms of linearization is not a requirement.	Control algorithm is affected by high switching chattering effects. Characterised by discontinuous control law. Controller design is complex and improper design of the sliding surface can lead to unacceptable performance.
Backstepping control	Able to cope well with model uncertainty. System dimension can be extended using the recursive method. Capable of avoiding the cancellation of useful nonlinearities. Robustness against external disturbances.	Nonlinear observer is necessary when all state variables are unmeasurable. Identification of Lyapunov function is complicated. Sensitive to parameter variations. Item explosion problem is the weakness of this algorithm. Difficult to implement.
H∞ control	Effective control performance under system uncertainties. Stability and performance provide solutions to complex control issues.	Complex mathematical algorithm. Implementation is complicated. A strong system model is necessary.
Model predictive control	Control technique is cost effective. Resilience to changes in disturbances and parameters. Ability to predict future disturbances. Capable of handling slow moving process with time delay. Suitable for large systems composed of several sub-systems.	A good system’s model is necessary. High implementation cost. Application area is limited by its online computing time.
Linear quadratic regulator	Simple and easy to implement. Minimal energy use and strong stability. Mathematically effective. Addition of the Kalman filter to the system boosts performance.	Controller design is limited by the use of linearized system’s model. Full access to system’s states is a requirement. Speed of response is not guaranteed. Unsatisfactory for applications that demand a constant minimal steady state error.
Adaptive control	Appropriate for nonlinear systems with time varying or uncertain parameters. Good stability. Control variables can be modified swiftly in reaction to changes in system dynamics. Ease of use and a fast reaction time. Suitable for systems with unpredictable parameter variation and disturbances.	Requirement of good system’s model. Tedious to implement the controller. Adequate excitation is required. Adaptive loop needs to be slow than the control loop to fulfil requirement. Prove of stability is difficult in situation of changing system dynamics.
Observer-based control	Simple computational method. Simple formulation and ease of implementation. Estimation is fast.	A perfect knowledge of the system is required for its design. For proper state estimation, an exact model of the system is required. Observer coefficients should be chosen critically for effective response. Operating conditions have a big impact on convergence.
Fuzzy logic	Design is intuitive. Control is not dependent on the exact system’s model. Expert experiences are used to formulate the control rules. Capable of managing uncertainty and imprecision. Able to model reasoning mechanisms and human decision-making. The control action greatly depends on the rules given.	Difficulty in implementing operator’s knowledge. Optimisation is based on trial and error. Finding the membership function parameters and developing the optimal number of fuzzy rules are challenging. Limit cycles could occur as a result of nonlinear operation. No precise guidelines for its design. Frequent tuning is required for critical system.
Artificial neural network	Exact mathematical model is not a requirement. Capable of controlling several data and input variables. Accurate and reliable predictions. Capable of resisting unknown disturbances. Offers adaptive control parameters to models with uncertainty. One can train the control system.	Training a huge neural network takes time. Large data set is needed for quality predictions. Problem of overlearning. Absence of a methodical approach to determining the optimal network topology and the number of neurons to be added to the hidden layers. Challenge in selecting the input data of the network weights and choosing the training step, which has a significant impact on the rate of convergence. Consumes more computer power.
Adaptive neuro fuzzy inference system	Combined effect can establish membership function parameters and produce the best fuzzy rules. Swift convergence and effective dynamic reaction. High control precision, robustness and reliability. No need for a precise mathematical model of the controlled object. Control principles and membership functions are independent of expertise.	Can only use a limited number of input variables. Characterised by computational complexity. Efficient training is required. Lack of unified design tools and well-known solutions for step-by-step learning algorithms.
Genetic algorithm	Global non-derivative based optimisation. Good optimisation tool and independent of mathematical model of the system. Fast convergence speed. Algorithm is easy to understand. Parallelism, efficiency, easily modified for different problems. Large and wide solution space searching ability.	Mostly inappropriate for real-time applications. Possibility to fall into premature convergence. Tuning can be challenging. Consumes more computer power. Low accuracy.
Hybrid control	Combine the benefits of multiple control philosophies to address issues that may not be handled by a single controller. Robust, high performance and accuracy.	Intelligent control needs a lot of training data and user experience. The traditional and advanced controllers are challenging to tune. Complicated architecture.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.