Load frequency control strategy via fractional-order controller and reduced-order modeling

Several papers reviewing fractional order calculus in control applications have been published recently. These papers focus on general tuning procedures, especially for the fractional order proportional integral derivative controller. However, not all these tuning procedures are applicable to all kinds of processes, such as the delicate time delay systems. This motivates the need for synthesizing fractional order control applications, problems, and advances completely dedicated to time delay processes. The purpose of this paper is to provide a state of the art that can be easily used as a basis to familiarize oneself with fractional order tuning strategies targeted for time delayed processes. Solely, the most recent advances, dating from the last decade, are included in this review.

https://biblio.ugent.be/publication/8612329/file/8612330.pdf

A Survey of Recent Advances in Fractional Order Control for Time Delay Systems

Fractional calculus has been a topic of great interest for the last few decades The applications of fractional calculus can be found in the area of viscoelastic and chaotic systems, whose dynamics is expressed in the form of fractional differential equations The ongoing research work is based on the design of 1-Degree of Freedom (1-DOF) and 2-Degrees of Freedom (2-DOF) Fractional Order PID (FOPID) controllers for a Magnetic levitation (Maglev) plant and the performance has been compared with that of 1-DOF and 2-DOF Integer Order PID (IOPID) controllers in both simulation and real time The Degree of Freedom (DOF) represents the number of feed-forward control loops in a closed loop system A 2-DOF controller configuration comprises of a serial compensator and a feed-forward compensator in a closed loop structure An FOPID controller has a structure similar to that of a conventional IOPID controller, except that its derivative and integral orders are fractional numbers The design of such a controller requires the determination of five parameters: Kp, Ki, Kd, α and β, where α and β are the derivative and integral orders of the FOPID controller The controller design problem has been framed as an optimization problem, in which the cost function is formulated from the characteristic equation of the closed loop system at dominant poles that are identified from the given performance specifications The closed loop response shows that the proposed2-DOF FOPID controller exhibits superior response and robustness with respect to its integer order counterpart

Real time implementation of fractional order PID controllers for a magnetic levitation plant

Design of sliding mode control for quadruple-tank MIMO process with time delay compensation

The beauty of the proportional-integral-derivative (PID) algorithm for feedback control is its simplicity and efficiency. Those are the main reasons why PID controller is the most common form of feedback. PID combines the three natural ways of taking into account the error: the actual (proportional), the accumulated (integral), and the predicted (derivative) values; the three gains depend on the magnitude of the error, the time required to eliminate the accumulated error, and the prediction horizon of the error. This paper explores the new meaning of integral and derivative actions, and gains, derived by the consideration of non-integer integration and differentiation orders, i.e., for fractional order PID controllers. The integral term responds with selective memory to the error because of its non-integer order λ , and corresponds to the area of the projection of the error curve onto a plane (it is not the classical area under the error curve). Moreover, for a fractional proportional-integral (PI) controller scheme with automatic reset, both the velocity and the shape of reset can be modified with λ . For its part, the derivative action refers to the predicted future values of the error, but based on different prediction horizons (actually, linear and non-linear extrapolations) depending on the value of the differentiation order, μ . Likewise, in case of a proportional-derivative (PD) structure with a noise filter, the value of μ allows different filtering effects on the error signal to be attained. Similarities and differences between classical and fractional PIDs, as well as illustrative control examples, are given for a best understanding of new possibilities of control with the latter. Examples are given for illustration purposes.

https://www.mdpi.com/2227-7390/7/6/530/pdf

Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC...

Tuning algorithms for fractional order internal model controllers for time delay processes

The advent of Industry 4.0 (I4.0) has pushed technology beyond its physical limits, making the process prone to errors and poorer performance. Whether it is about smart manufacturing where mass customization is envisaged, or collaborative human–robot engineering systems, the pyramid of process operation has changed to a matrix form and control is the backbone of all process elements. The paper gives a concise guideline as to how, when, where, and what to apply when it comes to choosing the most suitable control strategy as a function of multi-parameter objective optimization. Both proportional-integral-derivative (PID) and model predictive control (MPC) control are addressed in this context.

https://www.mdpi.com/2411-5134/4/1/10/pdf

The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective

An industrially relevant formulation of a distributed model predictive control algorithm based on minimal process information

This paper presents three model-based control strategies applied to a multivariable process. First, a simple and rather naive approach is employed, i.e. treating the process as two SISO (Single Input Single Output) loops and design PID controllers. Obviously, this approach is effective, but does not take into account the interaction between the loops. Next, interaction is compensated by using dynamic decouplers and control performance is improved. Finally, a multivariable IMC (Internal Model Control) method is applied. All the results were validated on the laboratory setup with coupled quadruple tanks from Quanser. This is an interesting and challenging testbed for control, i.e. it poses non-minimum phase transmission zeros. Our experimental results show that the IMC outperforms the PID control at the cost of additional design complexity. All controllers were successfully tested for setpoint trajectory and disturbance rejection and tackled well the noise in the system.

Multivariable model-based control strategies for level control in a quadruple tank process

This paper presents an extensive analysis of the properties of different control horizon sets in an Extended Prediction Self-Adaptive Control (EPSAC) model predictive control framework. Analysis is performed on the linear multivariable model of the steam/water loop in large-scale watercraft/ships. The results indicate that larger control horizon values lead to better loop performance, at the cost of computational complexity. Hence, it is necessary to find a good trade-off between the performance of the system and allocated or available computational complexity. In this original work, this problem is explicitly treated as an optimization task, leading to the optimal control horizon sets for the steam/water loop example. Based on simulation results, it is concluded that specific tuning of control horizons outperforms the case when only a single valued control horizon is used for all the loops.

https://biblio.ugent.be/publication/8599211/file/8599214.pdf

Anca Maxim

Papers

Tuning algorithms for fractional order internal model controllers for time delay processes

The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective

An industrially relevant formulation of a distributed model predictive control algorithm based on minimal process information

Multivariable model-based control strategies for level control in a quadruple tank process

Effect of Control Horizon in Model Predictive Control for Steam/Water Loop in Large-Scale Ships