Fractional order filters and compensators are natural solutions to modeling complex physical phenomena or to improved design of closed loop performance compared to traditional integer order transfer functions. Despite manifold advantages, a caveat in their breakthrough in the systems and control engineering community is their unlimited memory, which makes them difficult to implement in practice. To preserve the benefits of fractional order filters, the implementation must meet both accuracy and simplicity from user expectations point of view. The traditional implementation is based on integer order approximations in a bounded frequency range. The continuous-time approximations are either simple or accurate, but rarely both simultaneously and focus on simple fractional order differentiator and integrator. In this paper, a weighted least-squares method is proposed based on a simple algorithm, making it an efficient and elegant solution. The method is suitable for all structures of fractional order systems and general non-rational transfer functions, being more versatile compared to existing approaches. Our solution also produces low-order approximations without compromising accuracy. Guidelines for a proper use of the method are provided. Several numerical examples, including fractional order controllers, general fractional order transfer functions and dead-time systems are illustrated. A comparison with the classical Oustaloup Recursive Approximation method demonstrates its added value with respect to accuracy and efficiency.

Industrial applications are often downgraded to 60% of their original performance in the first 6 months of their execution. In most processes, these are still robust to process specs and continue to be used as such. However, significant performance improvement up to 40-50% is achievable by retuning the controllers. For this, it is very relevant to have methods applicable in practice to deliver better process models to achieve better closed loop performance. In this paper, we give such a solution that is based on closed loop data, i.e. with the existing controller in operation. The measurement interval is short and the test delivers data that is highly robust to loop disturbances. The obtained process model is accurate and can be further used to design a new controller or to retune the existing one. To validate the proposed approach, two case studies are considered: a non-minimum phase integrating system and a poorly damped integrating one. The experimental results demonstrate the robustness and efficiency of this novel approach.

Stimulated by Pharma 4.0, pharmaceutical industry is undergoing a transition from batch to continuous manufacturing (CM). There are manifold of benefits of continuous tablets production such as: increased product quality; improved efficiency; reduced production costs. Even more, CM could accelerate the product development resulting in faster delivery of medicine to patients, and this without affecting product quality. To exploit the advantages of CM, the mainly separated process units need to be integrated to form end-to-end systems from raw material to final product. Such integration requires a deep understanding and a holistic approach toward process development and optimization. In this paper, a feasibility study of using model predictive control for the multi-variable process of tablet production is presented. The simulations have been performed on a novel and unique simulation environment where the main unit processes involved in the manufacturing of tablets are implemented. Preliminary results indicate that the proposed methodology is feasible and implementable on the benchmark simulation.

General anesthesia is used to induce a reversible loss of consciousness in a patient to ensure they are completely unaware and pain-free during a surgical procedure. This is achieved through the administration of a combination of drugs that depress the central nervous system and can include intravenous injections, inhaled gases, or a combination of both. During the anesthesia maintenance phase, the events in surgery are not continuous, but obey a profile similar to that endorsed by event-based control. The present study considers event-based control as a natural solution to the control challenge of general anesthesia. Two fractional order Proportional Integral (PI) controllers are tuned and implemented using a decentralized approach to control a part of general anesthesia based on hemodynamic variables. The results show that this approach can be successfully used to keep Cardiac Output and Mean Arterial Pressure stable, with a special focus on patient safety, through fast detection of surgical stimulus and robustness to inter/intra patient variability.

In this study, a revisited improved approach of an initial frequency response based autotuner is proposed to enable PID controller design based on S-shaped step response data. In prior autotuner, the critical frequency value is found using relay test whereas process frequency response and its derivative at this frequency are calculated via the sine test. With the proposed approach, these values are estimated using the first order plus time delay models, which are employed to characterize S-shaped step response. Firstly, an identification method is used to find the model parameters, i.e. time constant$T$and delay time L. Secondly, the required values are estimated using the first order plus time delay model. The remaining tuner design steps are the same as in the prior autotuner. The simulations are performed on four different types of dynamical systems to show effectiveness of the proposed approach. The simulation results suggest that the performance of the control system using the proposed approach improves in terms of achievable performance indicators such as overshoot and settling time.

In this paper a Position Based Visual Servoing (PBVS) algorithm using deep neural network is applied to a UR10 cobot. A pre-trained Convolutional Neural Network (CNN) will be re-purposed and fine-tuned in an offline stage. In order implement and validate the CNN for a visual servoing application, a dataset was created in a simulated environment by moving a simulated UR10 robot to various positions and capturing an image with the corresponding relative pose to the target object. The obtained dataset was validated with ground truth dataset collected using the real robot. To control the motion of the cobot (simulated/real) a meta-operating system and a vision based control law was designed in ROS. The visual servoing task is defined as a repositioning task whereby the performance of the visual servoing is evaluated by the convergence to one desired pose from various, arbitrarily selected starting poses. Three different network architectures were implemented and tested. The obtained results reveal that all network architecture can be successfully applied to visual servoing systems.

This paper studies the design and implementation of a robust non linear fractional-order PI controller for a coupled tank system. The proposed control scheme is formed of two nested loops. The inner-loop consists in a feedback linearization control law closed to the water level of the upstream tank which reduces the effect of the uncertainty of the parameters of the actuator. The outer-loop is a fractional-order PI controller closed to the water level of the downstream tank which is robust to flow changes caused by variations of the cross sections of the outputs of the two tanks. The control signal generated by the outer-loop controller is the reference of the inner-loop. A new frequency-response technique is proposed to tune the robust outer-loop controller in such a way that the closed-loop system is not only robustly stable but also yields a nearly constant damping (overshoot) when the output cross sections change.

In this paper we analyze the opportunity to provide a methodology and signal processing algorithm to model and quantify drug concentrations in blood. The properties of blood as a non-Newtonian fluid are used to develop a physiologically based model using fractional calculus tools, which provide natural solutions to derivatives which no longer limit their order to integer numbers. In vitro tests support our mathematical model development. A prospective use for continuous monitoring of drug concentration profiles during anesthesia or other interventions is proposed. The proposed solution is based on fractional order impedance models.

This paper provides an overview of the distributed parameter properties of non-Newtonian fluids and a proposal for identifying them. A low cost setup is described along with a proposed methodology protocol. The paper introduces the problem of moving from a linear framework of fluid properties towards a nonlinear one and motivates the choice for lumped nonlinear parameter model structures. It follows identification using nonlinear least squares in various liquids. The results obtained suggest that parameters of the proposed fractional order impedance model are susceptible to changes in density as being one important feature of non-Newtonian fluids. Further use of these findings across disciplines is given in the conclusion section of the paper.

This article studies a leader-follower formation algorithm based on fractional order proportional-derivative (FOPD) controller for multiple unmanned aerial vehicles (UAVs) when tackling an emergency health case. The controller parameters are tuned by using a particle swarm optimization (PSO) algorithm. Its performance is compared with an integer order proportional-derivative (IOPD) controller. Finally, the global path planning for the UAVs swarm is found by using a Dijkstra's algorithm with quintic polynomial trajectory. This provides an optimal global path by considering the physical system dimension and constraints of acceleration and velocity of the UAV. The simulation tests using the virtual environment demonstrate that the proposed approach outperforms the IOPD controller.

As part of the new directions for vision and mission of Europe, patient well-being and healthcare become core features of a modern and prosperous society. That is, healthcare costs are optimized towards patient benefit and sideways effects such as cost-related reduction in medication, in frequency of post-operatory interventions, in recovery times and in comorbidity risk. In this paper, we address the incidence of events related to stroke, epileptic seizures and tools to possibly predict their presence from Electroencephalography (EEG) signal acquired in post-surgery patients. Wavelet analysis and spectrogram indicate graphically changes in the energy content of the EEG signal. Physiologically based neuronal dynamic pathway is used to derive fractional order impedance models. Nonlinear least squares identification technique is used to identify model parameters, with results suggesting parameter redundancy. There is a significant difference in model parameter values between EEG signal with/-out events.

In this paper the nonlinear effects in the respiratory systems at low frequencies are measured and evaluated in healthy children and healthy adults. To this aim forced oscillations technique (FOT) has been used to non-invasively measure the lung tissue mechanics. FOT does not require any special effort from the patient in contrast with standardized tests where maneuvers are necessary. Hence, FOT is an ideal lung function test for extreme ages, more specifically children and elderly, given the simpleness of measurement technique. Hitherto, measurements at low frequencies (i.e. close to the breathing frequency $\approx$0.3 Hz) have been invasively performed in sacrificed animals and on anesthetized humans. Here we measure in the frequency interval 0.1-2 Hz a total number of 94 volunteers (37 adults with ages between 25-35 years and 57 children with ages between 8-11 years). To evaluate the non-linear contributions of the respiratory tissue, a novel T-index has been introduced. We have tested the hypothesis whether the nonlinear distortions are changing with growth/development of the respiratory tree and aim to quantity its dependence to biometric values. The results obtained indicate that the proposed index can differentiate between the two analyzed groups and that there is a dependence to age, height and weight. A medical information system may use this information to update predictions of respiratory function and provide aid in decision-making process of drug therapy.

The first-order-plus-dead-time model (FOPDT) is a popular simplified representation of higher order dynamics. However, a well known drawback is the rapid decrease of the frequency response accuracy with increasing process order. This especially applies to the higher frequency range. Literature offers solutions by extending this three parameter model with more parameters. Here, a fractional dead time is proposed. As such, a Frequency-Dependent Delay (FDD) is introduced, which offers a better approximation. As the fractional-order term introduces nonlinear coupling between the phase and the magnitude of the process, the fitting of the function becomes an iterative process, so a constrained multi-objective optimization is needed. This novel model, first-order-plus-frequency-dependent-delay or FOPFDD is fitted on a real electrical ladder network of resistors and capacitors of four and eight parts. The classic model, which is clearly a special case of the new model, is outperformed in the entire bandwidth.

Given the input/output constraints and cross couplings of supply chain (SC) nodes, model predictive control (MPC) is efficient to seek the optimal solutions to the problems posed by interacting nodes to satisfy customer demands. In supply chain applications, due to the growing spatial distribution and interactions between the supply network elements, the information flow management becomes a challenging yet significant task. To reduce numerical complexity while maintaining implementability, a distributed MPC strategy is proposed. The scheme aims at finding the Nash equilibrium where the controller of each subsystem communicates with other ones in the presence of noncooperative interaction and strong coupled inputs due to the ordering decisions. Extensive numerical simulations verify that the strategy outperforms conventional policies in terms of substantially reduced SC operating cost.

The present paper presentes the tuning of a Fractional Order Proportional Integral (FOPI) controller for second-order-plus-time-delay (SOPDT) plants. The tuning procedure is based on imposing frequency domain constraints for the open loop system with the FOPI controller and the SOPDT plant. The gain crossover frequency, phase margin and the iso-damping property that guarantees a certain degree of robustness to gain variations are imposed in order to obtain the parameters of the fractional order controller. The proposed method is validated by real life implementation on a process whose dynamics are approximated to a SOPDT model. The settling time, steady state error, robustness and disturbance rejection capabilities are analyzed using experimental test cases.

This paper proposes a rationale and methodology for control structure adaptation in presence of varying, unknown sub-system interaction degree. Two elements are introduced: 1) the detection, i.e., the cycle of detecting changing circumstances, planning and deploying responsive modifications and 2) the adaptation of the control architecture to maintain specified performance, fulfilled in absence of model information. The first hand results presented in this work indicate the method works well. Simulation studies support the application potential of the proposed methodology.

This paper presents the deployment and validation of a control structure for a dynamic knee rig used to gain insight into the kinematics and kinetics of the human knee joint. The dynamic knee rig is able to implement a bicycle motion onto the knee joint using five different actuators. Two actuators control the position of the ankle, one actuator mimics the quadriceps force and two more actuators impose the hamstring forces. Identification of the subsystems of the rig is performed using the ARX method. The obtained mathematical models are used to tune the controller parameters of the proportional-integral-derivative (PID) controllers which control each subsystem. Apart from PID controllers, the control structure also contains feedforward action, decouplers and gain adaptation mechanism. The controllers are tested using a mechanical hinge as knee joint The results indicate a good trajectory tracking performance of the controllers.

This paper presents the application of fractional calculus tools, i.e. fractional order impedance and Cole-Cole elements to detect, measure and estimate glucose concentrations by means of electrochemical impedance spectroscopy. In this paper, the fractional order impedance model is presented and compared with the measured impedance. The model parameters are related to various physical conditions of the test-cells and a baseline measurements along with blind evaluation are presented. The results obtained indicate that the fractional order impedance model can capture the dynamics of the measured impedance.

This paper presents a systematic overview of models describing viscoelastic properties in respiratory tissue during tidal breathing and afferent lung function testing devices. A technological development timeline is given and current state of art is evaluated with clinical data in healthy subjects and chronic obstructive pulmonary disease diagnosed patients. Further technological and methodological improvements are suggested.

This paper presents a mathematical framework for over-simplification of pharmacodynamic models to capture drug effects in humans. A large representative class of drugs are classically modelled by Hill equations, and a specific case is discussed in this paper. The proposed model is validated in simulation against a classical model of drug effect for a specific case of hypnotic drug used in general anaesthesia: Propofol. The results support the validity of the proposed model and allow further improvements in the current use of such models. An important property of the model is that it allows prediction of the patient's response to drug infusion dynamic profiles and allows a smoother control sequence of drug profiles, i.e. a more suitable approach for model based predictive control strategies. A manifold of 1000 Monte Carlo simulations from generated data in closed loop control indicate the suitability of the model for continuous infusion drug management during hypnosis.

In this paper we introduce a methodology for adapting a population model to the actual patient dynamics, i.e. An individualization of target controlled infusion (TCI) based infusion. The solution proposed in this paper is to use the effect site concentration (Ce) of the drug into the patient as a feedback signal and to adapt the parameters of the Hill curve (relating the BIS and Ce) during the induction phase, resulting in a patient-individualized closed loop control of anesthesia. This allows moving from conventional generic patient models for drug infusion regulatory loops to personalized medicine.

This paper discusses the derivation of a theoretical model of the movement of the shank, i.e. the lower part of the leg, around the knee joint. The objective is to control the angle of the shank with respect to the position of the upper leg. Subsequently, two types of design techniques are used to design a PID controller for the derived system: a model-based design method and an autotuner technique. The resulting PID controllers are compared by simulating a squat movement and a normal gait.We conclude that both PID controllers perform similar but the control effort for the auto-tuner technique presents a smoother course. However, the main advantage of the PID auto-tuner technique is that no mathematical transfer function model is necessary in order to tune the controller.

This paper provides an up-to-date review from the intersecting point of both clinical and engineering frameworks. The text interwoves available measures, models and control algorithms for general anesthesia. The three main parts of general anesthesia: neuromuscular blockade (NMB), hypnosis and analgesia are critically observed and perused from a global objective perspective. The outcome of this review is that quantifying and controlling depth of anesthesia is a challenging process and that current bottlenecks are singularly due to lack of direct measurement of analgesia during general anesthesia. Some ongoing efforts are recognized towards the development of a pain transmission model, possibly leading to the breakthrough required for analgesia sensor availability.

This paper introduces an extension of the Internal Model Control algorithm for efficient disturbance rejection. The approach is based on ideas from model based predictive control and diophantine equation derivation. As an illustration of the power of the extension, an example from the process industry is borrowed, namely a drum boiler. The process is challenging for control since it has an integrator and non-minimum phase dynamics. The performance of the proposed extension is compared against nominal IMC design and PID. The simulation results suggest that the proposed algorithm outperforms the other implementations in terms of effective disturbance rejections.

This paper presents a multivariable relay-based PID autotuning strategy, which ensures a specified modulus margin (i.e. robustness). The algorithm is applied on the coupled quadruple tanks from Quanser. The system is challenging for control since it presents non-minimum phase transmission zeros. The performance of the autotuner is validated against a computer-aided design tool based on the frequency response, i.e. FRTool. The experimental results suggest that the proposed autotuning procedure has similar performance as the control design based on full knowledge of the system. This is a remarkable conclusion and provides a good motivation to claim that our algorithm may be useful in chemical process applications where full knowledge of the systems model is still a burden for the control engineer.

This paper reports on the analysis of tidal breathing patterns measured during noninvasive forced oscillation lung function tests in six individual groups. The three adult groups were healthy, with prediagnosed chronic obstructive pulmonary disease, and with prediagnosed kyphoscoliosis, respectively. The three children groups were healthy, with prediagnosed asthma, and with prediagnosed cystic fibrosis, respectively. The analysis is applied to the pressure-volume curves and the pseudophase-plane loop by means of the box-counting method, which gives a measure of the area within each loop. The objective was to verify if there exists a link between the area of the loops, power-law patterns, and alterations in the respiratory structure with disease. We obtained statistically significant variations between the data sets corresponding to the six groups of patients, showing also the existence of power-law patterns. Our findings support the idea that the respiratory system changes with disease in terms of airway geometry and tissue parameters, leading, in turn, to variations in the fractal dimension of the respiratory tree and its dynamics.

This paper presents a novel concept of modeling biological systems by means of preserving the natural rules governing the system's dynamics, i.e., their intrinsic fractal (recurrent) structure. The purpose of this paper is to illustrate the capability of recurrent ladder networks to capture the intrinsic recurrent anatomy of neural networks and to provide a dynamic model which shows typical neuronal phenomena, such as the phase constancy. As an illustrating example, the simplified model for a neural network consisting of motor neurons is used in simulation of a recurrent ladder network. Starting from a generalized approach, it is shown that, in the steady state, the result converges to a constant-phase behavior. The outcome of this paper indicates that the proposed model is a suitable tool for specific neural models in various neuroscience applications, being able to capture their fractal structure and the corresponding fractal dynamic behavior. A link to the dynamics of EEG activity is suggested. By studying specific neural populations by means of the ladder network model presented in this paper, one might be able to understand the changes observed in the EEG with normal aging or with neurodegenerative disorders.