Prof. Dr. Diego Eckhard

The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The shape of the cost function, and hence the difficulty in solving the optimization problem, depends directly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. Therefore, it seems plausible to improve the convergence to the global minimum by properly choosing the spectrum of the input; in this paper, we address this problem. We present a condition for convergence to the global minimum of the cost function and propose its inclusion in the input design. We present the application of the proposed approach to case studies where the algorithms tend to get trapped in nonglobal minima.

Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H2 performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization - no process model is used. The convergence to the global minimum of the performance criterion depends on the initial controller parameters and on the step size of each iteration. This paper discusses these issues and provides a method for choosing the step size to ensure convergence to the global minimum utilizing the lowest possible number of iterations.

The Output Error Method is related to an optimization problem based on a multi-modal criterion. Iterative algorithms like the steepest descent are usually used to look for the global minimum of the criterion. This algorithms can get stuck at a local minimum. This paper presents sufficient conditions about the convergence of the steepest descent algorithm to the global minimum of the cost function. Moreover, it presents constraints to the input spectrum which ensure that the convergence conditions are satisfied. This constraints are convex and can easily be included in an experiment design approach to ensure the convergence of the iterative algorithms to the global minimum of the criterion.

In this work a modified Resonant Controller is proposed to deal with the tracking/rejection problem of periodic signals robust to period variations and parametric uncertainties in the plant. The control strategy is based on a resonant structure in series with a notch filter, which will be responsible to improve the robustness to period variation. A robust state feedback controller is designed by solving a linear matrix inequality (LMI) optimization problem guaranteeing the robust stability of the closed loop system. A numerical example is presented to illustrate the method.

This paper addresses the problem of tracking constant references for linear systems subject to control saturation. Considering an unitary output feedback loop, containing an integral action, conditions in LMI form are proposed to compute a state feedback and an integrator anti-windup gain. These conditions ensure that the trajectories of the closed-loop system are bounded in an invariant ellipsoidal set, provided that the initial conditions are taken in this set and the references and the disturbances belong to a certain admissible set. Based on these conditions, optimization problems aiming at the maximization of the invariant set of admissible states and/or the maximization of the set of admissible references/disturbances are proposed.

This study addresses the control of linear systems subject to both sensor and actuator saturations and additive L2-bounded disturbances. Supposing that only the output of the linear plant is measurable, the synthesis of stabilising output feedback dynamic controllers, allowing to ensure the internal closed-loop stability and the finite L2-gain stabilisation, is considered. In this case, it is shown that the closed-loop system presents a nested saturation term. Therefore, based on the use of some modified sector conditions and appropriate variable changes, synthesis conditions in a quasi-linear matrix inequality (LMI) form are stated in both regional (local) as well as global stability contexts. Different LMI-based optimisation problems for computing a controller in order to maximise the disturbance tolerance, the disturbance rejection or the region of stability of the closed-loop system are proposed.

The wireless sensor networks (WSN) are gradually gaining attention because it is a key technology for the Internet of Things. For most of these networks, the data is usually collected in a manual way, by removing a memory unit or connecting the collector node to a personal computer. This is a constraint, because it demands the manipulation of the collector radio by the operator, which consists in a problem in practical applications. The main goal of this work is to present a non-invasive alternative way to collect the data by means of Bluetooth technology. The approach allows the development of hermetic devices, which is a desirable feature for practical deployment of the sensor nodes.

A method for computing time-varying dynamic output feedback controllers for discrete-time linear systems subject to input saturation is proposed. The method is based on a locally valid polytopic representation of the saturation term. From this representation, it is shown that, at each sampling time, the matrices of the stabilising time-varying controller can be computed from the current system output and from constant matrices obtained as a solution of some matrix inequalities. Linear matrix inequality-based optimisation problems are therefore proposed in order to compute the controller aiming at the maximisation of the basin attraction of the closed-loop system, as well as aiming at ensuring a level of {L2} disturbance tolerance and rejection.

In the present work a systematic methodology for computing dynamic output stabilizing feedback control laws for nonlinear systems subject to saturating inputs is presented. In particular, the class of Lur'e type nonlinear systems is considered. Based on absolute stability tools and a modified sector condition to take into account input saturation effects, an \{LMI\} framework is proposed to design the controller. Asymptotic as well as input-to-state and input-to-output (in a L2 sense) stabilization problems are addressed both in regional (local) and global contexts. The controller structure is composed of a linear part, an anti-windup loop and a term associated to the output of the dynamic nonlinearity. Convex optimization problems are proposed to compute the controller considering different optimization criteria. A numerical example illustrates the potentialities of the methodology.

Industrial wireless networks are gradually being adopted in industry, especially due to the low installation costs and low maintenance provided by these systems. For the use in harsh environments, the communication system should provide reliable radio links and low power to allow battery powered devices. In this context, WirelessHART, ISA SP100.11a and WIA-PA protocols were developed to meet these requirements. The standards of these protocols define a minimum number of RF fixed power and selectable only by commissioning. Within this scenario, it is important to adapt RF power modulation, a procedure already used in several communication protocols to minimize the energy consumption of transceivers while maintaining link quality. In this paper, three ways to modulate RF power are analyzed within WirelessHART field devices. A specific approach that works in a decentralized way is developed and the results show it can save 50% of energy in certain cases. The proposal has the extra advantage to be compatible with the standard.

This paper considers the design and implementation of a discrete-time fast tracking controller for quadrotor vehicles subject to perturbations. The proposed controller consists of a model-based disturbance observer and a Composite Nonlinear Feedback (CNF) controller. The CNF control law introduces nonlinear damping to the system so that it possesses a fast rise time without overshoot. The least square identification method is applied to develop a model based disturbance observer, thus decoupling the problems of track following and disturbance rejection. Experimental results are provided in order to validate the proposed approach.

Virtual Reference Feedback Tuning (VRFT) is a data-driven technique used to design controllers without the need of a process model, only input-output data is utilized. When the process has non-minimum phase (NMP) zeros, the original method usually presents poor performance, because scarcely the reference model has the same NMP zeros as the process. To overcome this problem, a flexible criterion has been proposed to the VRFT method, in a way that both the controller parameters and the NMP zeros of the process are estimated together. In this paper we present the application of the VRFT method with flexible criterion to the level control of a MIMO pilot plant. We show that a sequential controller design may incorporate NMP behavior to the process. We then use the VRFT method with flexible criterion to design the controller using only closed-loop data from the process.

Wireless networks are gaining space in industrial environments due to the low installation costs and low maintenance. Robustness is also one of the main requirements for these systems to be adopted, and, in this context, WirelessHART (WH), ISA SP100.11a, and WIA-PA protocols met these characteristics. In order to provide low maintenance, these protocols must provide reliable radio links while keeping low power consumption to allow battery powered devices. Unfortunately, the standards of these protocols do not impose any RF power modulation technique, which is a form to increase even more the battery endurance of a wireless field device. Instead, RF power levels are fixed and selected by commissioning, and must allow the longest link per device. In this case, devices in closer ranges waste energy during transmissions, as they could save energy by modulating the RF power. This paper presents a RF power modulation technique that employs a proportional-integral controller and allows reduction of energy consumption while keeping the robustness of RF links. A proof of concept of the power modulation technique is implemented and verified showing good results and proving that the proposed controller is feasible. The proposal has the advantage to be fully compatible with the standard.

Iterative Feedback Tuning (IFT) is a data-driven method used to tune parameters of feedback controllers minimising an H2 criterion. The method uses data from experiments to estimate the gradient of the criterion, and uses iterative quasinewton algorithms to adjust the controllers. When the method is used in cascade systems, usually the inner loop is firstly adjusted, and after the outer loop. In this article we describe an extension to the IFT method that adjusts both inner and outer loop at the same time using only data from closed-loop experiments at each iteration.

Quadcopter is a type of Unmanned Aerial Vehicle which is lifted and propelled by four rotors. The vehicle has a complex non-linear dynamic which makes the tuning of the roll and pitch controllers difficult. Usually the control design is based on a mathematical model which is strongly related to physical components of vehicle: mass, moment of inertia and aerodynamic. When a tool is attached to the vehicle, a new model must be computed to redesign the controllers. In this article we will adjust the controllers of a real experimental quadcopter using the Cascade Iterative Feedback Tuning method. The method is data-driven, so it does not uses a model for the vehicle; all it uses is input-output data collect from the closed-loop system. The method minimizes the \{H2\} error between the desired response and the actual response of the vehicle angle using the Newton-Raphson algorithm. The method achieves the desired performance without the need of the vehicle model, with low cost and low complexity.