The design of effective controllers for complex Two-Input-Two-Output (TITO) systems presents a significant challenge, particularly in the context of implementing a design methodology suited to this specific type of process. In the majority of cases, if the system in question exhibits highly complex dynamics or a high order, the procedure to be performed is an order reduction. This is done in order to describe the essential dynamic characteristics of the original system in a way that allows for the reliable design of controllers. This work presents a practical approach to the design of decentralized Fractional-Order Proportional-Integral-Derivative (FOPID) controllers for TITO systems. The methodology comprises a reduced-order representation of the original system and an optimization-guided design. The intention is to quantify the performance loss in the decentralized FOPID controller design procedure when reduced-order models are employed. This will facilitate a validity analysis of the fractional controller selection. To enable a fair comparison in the validation example, an equivalent procedure for a decentralized PID controller will be presented in parallel.

In the design of Multiple-Input-Multiple-Output (MIMO) control systems, one of the most critical considerations is system stability. This is typically addressed in the controller design process through the incorporation of a stability index, often based on frequency domain analysis, to quantify system robustness after controller implementation. This work focuses on analyzing the design of Decentralized Proportional-Integral-Derivative (PID) controllers for Two-Input-Two-Output (TITO) processes, which are among the most common multivariable systems. The proposed approach evaluates stability margins using the maximum of the sensitivity function, despite robustness also being measurable through the maximum of the singular values. By utilizing the equivalent sensitivity function for multivariable systems, the method provides a direct means of assessing robustness once decentralized controllers are implemented. The feasibility of this approach is examined for a specific level of robustness and is validated employing two benchmark processes.

In this paper we deal with the practical implementation of Proportional-integral-Derivative-Acceleration (PIDA) controllers. In particular, we discuss and analyze the digital realization of such a kind of controllers by taking into account the typical additional functionalities that are essential in practical applications. The specific issues related the use of the second derivative of the control error are addressed. Simulation results show the effectiveness of the different approaches. It is believed that having such a reference implementation will foster the applicability of PIDA controllers in industry.

Thermal Energy Storage (TES) systems represent a cornerstone in the development of sustainable energy solutions, enhancing the flexibility in energy management. Heat exchangers are pivotal components of TES systems and they require precise control to optimize efficiency. Proportional-Integral-Derivative (PID) controllers are commonly employed for heat exchangers control but, since these systems are subject to time-delays and nonlinearities, their performance leaves room for enhancement. This paper explores the potential of Proportional-Integral-Derivative-Acceleration (PIDA) control, also known as PIDD or PIDD2, that introduces a double derivative (acceleration) component to the traditional PID structure. The capability of PIDA control in improving system performance compared to conventional PID controllers is evaluated through a simulation study performed on a realistic heat exchanger simulator. The obtained results show the efficacy of PIDA control in enhancing control performance without adding additional complexity or tuning efforts.

Autotuning and system parameters monitoring are crucial aspects of modern motion control algorithms. At-the-edge controllers need to detect system changes and perform autotuning autonomously without requiring excessive computational burdens. Two algorithms to estimate the main dynamics of mechanical systems using integral figures of merit are proposed. The algorithms implementations are analyzed in offline and online modes. They have been tested in simulation, using an elastic model as a testbed, and in a hardware-in-the-loop PLC-controlled system. The results show the effectiveness of the methods compared to a recursive least square method.

In this paper we propose a methodology for the design of Proportional-Integral-Derivative-Acceleration (PIDA) controllers for integral processes. In particular, starting from a high-order process model, which can be obtained by means of a relay-feedback experiment, the controller parameters are determined by applying a general Internal Model Control Strategy and reducing the controller transfer function to the PIDA form by approximating it with a Maclaurin truncated series. An additional low-pass filter is added to compensate for the measurement noise effect. Both cases with the presence and absence of the integral action in the controller are analyzed. Illustrative examples are given to demonstrate the effectiveness of the methodology and its suitability to be applied in a practical context.

The goal of the control system in lift application in high-speed/high-rise applications is to maximize speed without sacrificing passengers comfort. Lifts are affected by position-based disturbances, which could be compensated by employing a repetitive control strategy. Lifts also have nonlinearities due to the rope mass and stiffness variation with respect to the position. This paper analyzes the performance of LTI position-based repetitive control strategies in lift applications. Simulation with a nonlinear model of the lift shows that repetitive control strategies could improve control performance. In particular, Gaussian Process Repetitive Control is less sensitive to the nonlinearities and provides fewer oscillations.

In this paper we analyze the performance that can be achieved by means of Proportional-Integral-Derivative-Acceleration (PIDA) controllers and we compare it with that achievable by means of PID controllers in order to evaluate the cost/benefit ratio when an action proportional to the second derivative of the control error is added to the control law. In particular, different process transfer functions are considered and the tuning of the controllers is determined with genetic algorithms by minimizing the integrated absolute error. Both the set-point following and load disturbance rejection tasks are evaluated. In order to provide a fair comparison, a constraint on the maximum sensitivity is also posed so that the achieved robustness is taken into account.

In this paper we propose a tuning rule for PI controllers that is based on the same rationale of lambda-tuning but aims to optimize the performance in the load disturbance rejection task. In particular, the maximum sensitivity obtained by lambda-tuning is used as a constraint in minimizing the integral of the absolute error, in such away to achieve the desired tradeoff between performance and robustness. The tuning rule has been obtained by finding the optimal parameters for different processes by means of genetic algorithms and then by fitting the results with appropriate functions.

In this paper an optimized robust feedforward/feedback control strategy for anesthesia induction is proposed. Propofol is used to induce hypnosis and the bispectral index scale (BIS) is employed as an indicator of depth of hypnosis. The control action is divided in two parts, a feedforward action, which acts alone in the first phase of anesthesia induction, and a feedback PID action that concludes the induction by smoothly driving the BIS to the target value. The feedforward action is given by an optimized bolus of propofol that is determined by taking into account the uncertainty of the nonlinear pharmacodynamic model for the effect of propofol on BIS. In particular, the bolus is obtained as the solution of a constrained minimum-time control problem in order to minimize the induction time of anesthesia while preventing the undershoot of the BIS level. Thus the patient’s safety is ensured by avoiding side-effects due to drug overdosing. The PID controller is then used in order to compensate for the deviation of the BIS from the theoretical behaviour caused by the effect of inter-patient and intra-patient variability.

In this paper we present a simple technique to improve the set-point following performance of the basic Predictive Functional Control algorithm. The technique is based on a first-order-plus-dead-time model of the process and on a suitable selection of the set-point signal, so that a process variable transition from a steady-state value to another one is achieved in a predefined time interval. The additional design effort, which is practically negligible, is discussed. Illustrative examples are given to demonstrate the effectiveness of the proposed solution.

In this paper an automatic tuning procedure for a cascade position control architecture of general mechatronic systems is presented. The proposed procedure firstly estimates the transfer function of the system by analysing its open-loop response to a signal specifically designed in order to satisfy the system position, velocity and torque constraints. Then, if present, oscillatory behaviours are detected by finding the (anti)resonances of the frequency response. Parametric tuning rules are eventually defined for the PID controllers and for the biquadratic filters. Results obtained by implementing the procedure on a Hardware In the Loop setup demonstrate the effectiveness of the method.

In this paper a closed loop automatic tuning procedure for the velocity control of oscillatory mechatronic systems is proposed. The transfer function of the system is estimated relying only on the measurements on the motor side, resonances are identified and biquadratic filters and the PID controller are tuned in order to improve the pre-existing control system by reducing the oscillations on the load side. Experimental results obtained with a Hardware-In-the-Loop setup show the effectiveness of the method.

The paper presents a fast online predictive method to solve the task-priority differential inverse kinematics of redundant manipulators under kinematic constraints. It implements a task-scaling technique to preserve the desired geometrical task, when the trajectory is infeasible for the robot capabilities. Simulation results demonstrate the effectiveness of the methodology.

Reduction of the energy consumption in robotized processes is a key issue in nowadays manufacturing. In this paper, we propose a simple approach to energy minimization of robotic tasks with assigned cycle time based on sequential quadratic programming. The method aims at re-shaping a given timing law in the sense of energy saving, without modifying the desired path and the given cycle time. Thanks to the iterative linearization of the nonlinear time-constraint, the resulting minimization problem is solved by only using common quadratic programming solvers, making the method suitable for a direct implementation in robot industrial controllers. At first, the method is devised by only considering the kinematics of the manipulator. The dynamic model is then straightforwardly included, without significantly increasing the complexity of the method. Validation in simulation environment is provided in order to show the effectiveness of the methodology.

In this paper, a Model Predictive Control approach for the velocity control of operator-in-the loop overhead cranes is proposed. The operator can select the maximum position overshoot as a tuning parameter for the method. Simulations provide a comparison between the proposed method and the well known Zero Vibration input shaping technique, showing its effectiveness in controlling the payload oscillations.

In this paper we present a new method to reduce the computational complexity of model predictive control algorithms with online optimization. The formulation of the predictive equations is performed in the continuous-time domain, while the control inputs are parametrized as piecewise constant functions, with less steps than the control horizon. The continuous-time formulation permits the arbitrary choice of the prediction and control time-instants, disregarding the sampling period of the system, and this improves the goodness of the approximation. Moreover, since the inputs are forced to be piecewise constant, the resulting controller can be directly implemented in discretetime. A tuning method for the choice of the prediction and control instants is proposed, minimizing the deviation with respect to the non-approximated controller. Numerical results show the effectiveness of this strategy against other methods with same reduction of computational complexity.

In this paper we analyze the tuning of a PIDPlus event-based control scheme with a noise-filtering event generator. In particular, different well-known PID tuning rules have been considered with different processes. A comparison with a standard PID control scheme has been also considered to evaluate the event generator effectiveness. It is shown that, independently from the employed tuning rule, the application of the devised event generator provides the advantage of reducing the variability of the manipulated variable in the presence of high-frequency measurement noise.

In this paper we propose the use of a dynamic model in which the effects of temperature on friction are considered to develop a virtual force sensor for industrial robot manipulators. The estimation of the inertial parameters and of the friction model are explained. The effectiveness of the virtual force sensor has been proven in a polishing task. In fact, the interaction forces between the robot and the environment has been measured both with the virtual force sensor and a common load cell. Moreover, the advantages provided by considering the temperature dependency are highlighted.

Disturbance attenuation is often recognized as the primary concern of a control system. Regulation of the operating conditions is the usual task work to be pursued by a feedback controller. However, much of the academic works almost concentrate on set-point experiments for controller evaluation. Even those that explicitly concentrate on the disturbance attenuation problem, usually concentrate on input load disturbances. They exclude the specific, but usually not considered in the literature, case when the path from the disturbance to the controlled variable is different to the path from the controller output to the controlled variable. There are some process units such as heat exchangers or distillation columns where this situation may be encountered. However actual feedback controllers, usually of PI/PID type, do not take into account this situation. The point raised in this paper is that an increment of performance can be obtained if the information regarding the disturbance dynamics is included in the PI/PID design stage.

In this paper, we present a new approach to manipulability maximisation for a dual-arm manipulator, which takes into account the manipulability of the overall task. This method tries to overcome the drawbacks given by traditional approaches, which optimise the manipulability of the local configuration of the manipulator, but do not take into account the rest of the task, even though it is known a priori. In this way, it is possible to improve the average manipulability index over the task. The method is applied to a dual-arm system, wherein the task is expressed in terms of relative poses between the end-effectors. For this reason, the kinematic of the system is solved by means of the relative Jacobian.

In this paper we present a methodology, based on genetic algorithms, for the optimization of the set-point signals for the heating system of a residential building. In particular, the set-point is planned for each room based on a simplified temperature model of the system and by taking into account the constraints on the comfort given by the user. The genetic algorithm determines the time instants when step or ramp signals have to be applied in order to minimize the energy consumption in the case of a radiators heating system. Simulation results obtained by using TRNSYS software tool demonstrate the effectiveness of the method.

In this paper we present a strategy developed to compensate for multiple sinusoidal disturbances affecting the pressure output of an industrial revamped plastic injection molding machine. The method is based on the suitable implementation of an adaptive feed-forward control technique and has the characteristic that it can be added to the already existing standard control structures such as the normal feedback structure or the cascade one without modifying any part of them. Experimental results show that the technique allows the significant reduction of multiple sinusoidal disturbances affecting the same part of the plant.

This paper deals with modeling the temperature of a room in a smart building. Two different approaches are analyzed and compared. The first one is based on a black-box identification procedure, which yields a fairly simple dynamical system that is suitable for real-time control. The second approach is based on first principles, it requires a complex and time consuming calibration procedure but it is capable of describing accurately the physical behavior of the system. The trade-off between the accuracy of the model and its computational complexity is evaluated by using experimental data collected from a smart pilot building located in Almería, Spain.

This paper deals with the identification of fractional models with one fractional parameter. This kind of models are capable to represent an extensive range of dynamics, including overdamped and oscillatory behaviors. The identification algorithm consists in applying an optimization function, starting from an initial point, that allows the program to calculate a very representative model of the process. The results demonstrate the usefulness and robustness of the tool, which can be employed to identify integer and fractional systems in an easy way and this can be later exploited for further studies, for example the development of tuning rules.

In this paper an approach for controlling overhead cranes by avoiding oscillations is proposed and experimentally tested. In particular, a dynamic inversion technique is applied to the overhead crane model to design suitable (position or velocity) set-point and torque feedforward signals. The inversion approach is further simplified in order to make the proposed methodology more industrial-oriented. Experimental results, obtained by using a scaled overhead crane built by using only off-the-shelf components, show the effectiveness of the proposed methodology.

In this paper we present a Hardware-In-the-Loop setup for the simulation of complex mechatronic systems. The setup consists of two coupled brushless motors. One of them is the motor under test, which is used to design the control algorithm and to test the control software, while the other one simulates the device to be controlled. Libraries of mechanical and hydraulic components have been implemented in an IEC61131-3 language so that a complex system can be simulated in a relatively easy way and this allows for a rapid control prototyping. Practical issues are discussed and an illustrative example is shown to confirm the effectiveness of the setup.

In this paper we present a new friction model for industrial robot manipulators that takes into account temperature effects. In particular, after having shown that friction might change very significantly during robot operations, two solutions based on a polynomial description of the joint friction are proposed and compared. In both cases the models proposed do not need a measurement of the joint temperature, but just of the environmental temperature, so as to be easily applied in industry. Experimental results demonstrate the effectiveness of the applied methodology.

In this work a virtual and remote lab developed to explore the properties of event-based PID controllers in a flexible link system is presented. The architecture is based on the decomposition of the system in three tiers or layers: the server, the client, and a middleware. The server layer is directly connected to the plant, the client side implements the controller and the graphical interface, and the middle-tier acts as interface between client and server. The implementation of the remote lab allows the control of a flexible link plant through a generic network. The platform is used to evaluate, with a particular case, the effectiveness of an algorithm that allows the user to find numerically the limit cycles in control schemes where the feedback is done through a level crossing sampling, and which can be applied to LTI systems with delay.

A two-degree-of-freedom constrained control strategy for a solar furnace is proposed in this paper with the goals of attaining a minimum-time transition between two values of the temperature subject to constraints on both the saturation and slew rate level of the process input. Disturbance compensation and the model mismatches are handled by using a feedback linearization and a PID controller. Simulation results demonstrate the effectiveness of the methodology using data from the solar furnace of the Plataforma Solar de Almería.

The design of anti-windup schemes for fractional-order PID controllers is addressed in this paper. The use of techniques typically employed for integer-order PID controllers is discussed and compared for fractional-order ones, showing that the back-calculation method is the one that provides in general the best performance. Further, a new scheme that involves the use of the (fractional) derivative action is proposed. In particular, the derivative action switches between two different values when the control variable saturates in order to reduce the saturation effects as much as possible. Simulation results are presented in order to demonstrate the effectiveness of the approach.

In this paper, we propose a new method, which is based on an iterative-learning-control (ILC) algorithm, for the contour tracking of an object of unknown shape performed by an industrial robot manipulator. In particular, we consider (both implicit and explicit) hybrid force/velocity control whose performance is improved by repeating the task. Here, a time-based reference signal is not present, and therefore, a new approach has been developed, which is different from the typical applications of ILC. Experimental results show the effectiveness of the technique.

This paper analyzes optimal controller settings for controllers with One-Degree-of-Freedom (1-DoF) Proportional-Integral-Derivative (PID) structure. The analysis is conducted from the point of view of the operating mode (either servo or regulation mode) of the control-loop and tuning mode of the controller. Performance of the optimal tuning settings can be degraded when the operating mode is different from that selected for tuning and obviously both situations can be present in any control system. In this context, a Performance Degradation index is minimized and based on this minimization, an autotuning procedure as a function of the normalized process dead-time is proposed.

A Chebyshev optimization approach can be employed effectively for the determination of the (reference) command input to be applied to a feedback PID control system in order to achieve a minimum-time output transition subject to constraints on both the control variable and the system output. The resulting command input function is given in polynomial form, for which a practical implementation can be difficult. In this paper we propose a method for the determination of a stable low-order filter whose step response approximates the open-loop command signal so that a standard two degree-of-freedom controller results. Simulation examples show that the technique is effective and yields a stable transfer function which can be easily implemented into a DCS control system.

In this paper we present a control technique for a domestic tumble dryer. The control algorithm, that aims at decreasing the energy consumption by satisfying at the same time the required constraints, basically consists in a selective control between a PI controller (which controls the clothes temperature) and a three-state controller (which controls the heater temperature). Experimental results show that the overall control algorithm is effective and provide a better performance with respect to the standard PI controller.

The paper investigates the decoupling of an explicit hybrid force/velocity control of robot manipulators employed in the contour tracking task of objects of unknown shape taking into account the elastic model of the contact and of the robot. The proposed controller allows the achievement of the decoupling of the normal force and tangential velocity control loops and can be expressed as a multi-input multi-output (MIMO) time-varying proportional-integral-derivative (PID) controller. Experimental results demonstrate the effectiveness of the method.

In this paper we propose a new method, based on an iterative learning control (ILC) algorithm, for the contour tracking of a planar object of unknown shape performed by an industrial SCARA robot manipulator. In particular, we adopt a hybrid force/velocity controller whose performance is improved by repeating the task. Differently from the typical applications of ILC, here a reference position signal is not present and therefore a new approach has been developed. Experimental results show the effectiveness of the technique

This paper deals with the experimental charactherization of an hybrid force/velocity controller for contour tracking tasks of objects with unknown shape performed by industrial robot manipulators. In particular, we address the problem of the presence of elasticities in joint transmissions, and we show that this can cause the occurrence of large force oscillations depending on the robot configuration. We also show that to compensate for them it is necessary to employ an additional normal velocity feedback loop. Experimental results have been obtained with a two degrees-of-freedom SCARA robot.

In this paper we experimentally analyze the performances of an hybrid force/velocity controller for the automatic edge following of 2D unknown planar contours performed by a robot manipulator. In this work we show that joint friction effects have to be taken into account in the controller design in order to achieve reasonable performances in terms of normal force and tangential velocities errors. For this reason we compare two model-based methods for coping with friction: a static one based on a previously identified model and an adaptive one recursively updating joint friction parameters. The proposed controllers are compared through an extensive experimental activity on a 2-DOF SCARA robot tracking an high stiffness aluminium-bar over a wide range of operative conditions.

A methodology, based on fuzzy logic, for the tuning of proportional-integral-derivative (PID) controllers is presented. A fuzzy inference system is adopted to determine the value of the weight that multiplies the set-point for the proportional action, based on the current output error and its time derivative. In this way, both the overshoot and the rise time in set-point following can be reduced. The values of the proportional gain and the integral and derivative time constant are determined according to the well-known Ziegler-Nichols formula so that a good load disturbance attenuation is also assured. The methodology is shown to be effective for a large range of processes and is valuable for industrial settings since it is intuitive, it requires only a small extra computational effort, and it is robust with regard to parameter variations. The tuning of the parameters of the fuzzy module can be easily done by hand or by means of an autotuning procedure based on genetic algorithms.

We present an approach for motion planning of servosystems endowed with elastic transmission. The open-loop control strategy proposed is based on noncausal system inversion and guarantees the absence of vibration both during and at the end of the point-to-point motion. The idea is to calculate the command input of the system after having determined a suitable parametrized motion law of the load. It can be demonstrated that, with an opportune choice of the motion law, the input function has a continuous derivative of a predefined order. Moreover, the time of the motion can be optimized with the purpose to minimize it, taking into account constraints on the actuators. The whole technique is explained in the paper and simulation results are given in order to show its feasibility.

A deterministic global optimization approach is proposed for the optimal trajectory planning of D-joint industrial robots. After mapping Cartesian knots into joint knots, by inverse kinematics, they are interpolated with spline functions under constraints on joint accelerations and jerks. An interval algorithm, a procedure based on interval analysis techniques, is presented to determine an optimal piecewise trajectory which globally minimizes the total travelling time. With arbitrarily sharp precision, this procedure solves the nonlinearly constrained optimization problem which is obtained using the branch-and-bound principle where the branching is performed by uniform subdivision and the bounding via interval inclusion functions. The interval algorithm, implemented in C++ with the PROFIL/BIAS libraries, has been applied to the optimal four-spline planning of a 6-joint arm, and computational results are included.

This paper describes a novel method of object recognition based upon the piece-wise linear approximation of ultrasonic echoes which, when tested with examples typical of a work cell environment, achieves classification success rates of 92-98%. The approach uses decision tree classifiers constructed from simple features derived from the echoes of a monostatic sonar and taken from a number of view points. The results illustrate the effect upon the method's classification success of the use of a single view point, the inclusion of additional view points, and the possibility of object rotations, for 22 classes of objects. The processes of feature extraction and classification are accomplished within a time comparable with the time of flight of the pulse, and hence the method has potential for real time applications.

We describe an object recognition technique based upon the extraction of simple features from the initial part of ultrasonic echoes. Features collected from a single or multiple viewpoints are classified using a decision tree. Since only the initial part of the echo is examined, the approach has potential for faster classification than alternative techniques requiring processing of the entire waveform. To emulate a workcell scenario, the approach has been verified mounting a Polaroid sensor at the wrist of a Puma 560 manipulator and implementing a simple modification of the proprietary circuitry (Polaroid Ranging Unit 6500). When tested with a set of 8 small plastic objects with regular shapes, the recognition technique has achieved classification success rates from 72% to 98%, depending upon the number and selection of echoes exploited for recognition. The paper illustrates classification performance using single or multiple viewpoints under both axis parallel and oblique decision trees.