The recent industrial revolution puts competitive requirements on most manufacturing and mecha- tronic processes. Some of these are economic driven, but most of them have an intrinsic projection on the loop performance achieved in most of closed loops across the various process layers. It turns out that successful operation in a globalization context can only be ensured by robust tuning of controller parameter as an effective way to deal with continuously changing end-user specs and raw product prop- erties. Still, ease of communication in non-specialised process engineering vocabulary must be ensured at all times and ease of implementation on already existing platforms is preferred. Specifications as settling time, overshoot and robustness have a direct meaning in terms of process output and remain most popular amongst process engineers. An intuitive tuning procedure for robustness is based on linear system tools such as frequency response and bandlimited specifications thereof. Loop shaping remains a mature and easy to use methodology, although its tools such as Hinf remain in the shadow of classical PID control for industrial applications. Recently, next to these popular loop shaping methods, new tools have emerged, i.e. fractional order controller tuning rules. The key feature of the latter group is an intrinsic robustness to variations in the gain, time delay and time constant values, hence ideally suited for loop shaping purpose. In this paper, both methods are sketched and discussed in terms of their advantages and disadvantages. A real life control application used in mechatronic applications illustrates
Fuzzy concepts derive from fuzzy phenomena that commonly occur in the natural world. The concepts formed in human brains for perceiving, recognizing, and categorizing natural phenomena are often fuzzy concepts. An objective of fuzzy logic has been to make computers think like people. Boundaries of these concepts are vague. We will introduce the basic concept of fuzzy systems and control in this chapter. A specific type of knowledge-based control is the fuzzy rule- based control, where the control actions corresponding to particular conditions of the system are described in terms of fuzzy if-then rules. Fuzzy sets are used to define the meaning of qualitative values of the controller inputs and outputs. Figure 3 shows the basic structure of a fuzzy logic controller. The main building units of an FLC are a fuzzification unit, a
relevant methods of software design and modelling [31, 32] can be mentioned in this regard. More specific solutions for renewable energy production and environmental systems also received recently attention within the framework of new controldesignconcepts as “Glocal” framework for hierarchical multi-agent networked dynamical systems [33-35]. However, properly addressing the two following cyber-physical topics is of importance during the SoMS design. The first topic relates to overcoming or considerable minimizing communication latency  between individual systems composing SoMS to ensure a robust network-based spatial-temporal design process. Some recent studies in this area propose adaptive strategies to compensate communication time delays making distributed and seamless system development possible, e.g. by utilizing an intranet or the internet or by a real-time simulation cloud (see, for example, outcomes of the project ACOSAR ). The second topic relates to the creation of an efficient validation and verification testing methodology allowing reduction of the development costs despite high complexity of the systems and their operational environments. This topic is receiving much attention within the CLOVER framework and is explained in the next paragraphs in more details.
Attitude tracking of the quadrotor subject to disturbances and uncertainty based on the IVSMC1-BL and IVSMC1-Cnt methods is shown in Fig. 3 using the first approach. Fig. 3 shows that Euler angles start from their initial conditions and converge to the predetermined desired values. Furthermore, as it can be clearly seen from Fig. 3, the perturbation of the roll angle around the desired condition in IVSMC1-BL is higher than that in IVSMC1-Cnt. Time responses of control torques are illustrated in Figs. 4 and 5 based on IVSMC1-BL and IVSMC1-Cnt, respectively. Fig. 5 displays approximately additional control effort in the roll channel to justify the roll angle. Time history of Euler angles based on IVSMC2-Cnt and IVSMC3-Cnt is shown in Fig. 6 for two assumed reaching time as t s1 = 0.3 s and t s2 = 4 s. As expected, the second
Abstract: This paper presents the fuzzy design of sliding mode control (SMC) for nonlinear systems with uncertainties, which can be represented by a Takagi-Sugeno (T-S) model. There exist the parameter uncertainties in both state and input matri- ces, as well as the matched external disturbance. The key feature of this work is the great ability of the controller to deal with systems without assuming that the control matrices of each local T-S model to be same and knowing the priori information of the upper norm-bounds of uncertainties. A suﬃcient condition for the existence of the desired fuzzy SMC is obtained by solving a set of linear matrix inequalities (LMIs). The reachability of the speciﬁed sliding surface is proven. A numerical example is illustrated in order to show the validity of the proposed scheme.
LPV framework complemented by the Lyapunov theory, which gives convex dependency on scheduling and uncertain parameters without using the multi-convexity requirement, or major restrictions on the closed-loop system or controller matrices. In addition, only few papers are devoted to LPV-based linear quadratic regulator (LQR) formulation [2, 19, 28, 34, 29]. Triggered by real time applicability, we hereby focus to static, guaranteed cost, fixed- order output-feedback controllerdesign that has not been covered. In this paper, we hence present a new, generalized solution to this specific problem, without any large restrictions in controller or system matrices. The solution is generalized in a sense that it covers robust, robust gain-scheduled, and robust switched (with arbitrary switching sequence) controllerdesign. The control structure is defined as centralized/decentralized fixed-order output- feedback (similarly to a standard PID) controller. Although, the proposed approach can be used for full/reduced order dynamic output-feedback controllerdesign as well, since it can be reformulated to static output-feedback design . Furthermore, the performance metric is defined in a standard LQR fashion, so the presented approach can be viewed as an extension of the classical infinite-horizon LQR problems . The proposed sufficient robust stability and performance conditions are translated to an optimization problem subject to some LMI and BMI constraints, which can be solved by commercial as well as open source solver tools like PENBMI  and PENLAB  or can be further linearized. The obtained controller can adapt to changes in the plant dynamics due to the inappropriate scheduling parameters and is robust against any uncertainties in the scheduling measurement of plant modelled in polytopic fashion. Thanks to the polytopic uncertainty representation on the affine LPV system matrices, it is possible to shape the uncertainty polytopes for different working/operation points based on the scheduled parameters. This opens new possibilities in LPV modelling, too.
When there is no measured disturbance ( f ( s ) = 0 ), the design problem turns into the classical one and the conditions to guarantee setpoint tracking, non measured disturbance rejection and measurement error rejection over given frequency ranges within prescribed accuracy are well known. On the other hand, taking only the measured disturbance, i.e., for r ( s ) = d ( s ) = 0 , the block diagram in Fig. (1) becomes as shown in Fig. (2). This figure is equivalent to the one considered in reference (Jonckheere, 1999) in order to solve an aircraft propulsion control problem.
The classical IBVS uses a set of geometric features such as points, segments or straight lines in image plane as image features . The controller is designed using the inverse (or pseudo-inverse) of the image Jacobian matrix to obtain the control errors in Cartesian space, and normally the proportional control law is applied to achieve a local convergence to the desired visual features. This proportional controller is very easy to implement; however, its drawbacks are its possible unsatisfactory behavior due to the difficulty of constraint handling. Also, the stability of the system is only guaranteed in the region around the desired position, and there may exist image singularities and image local minima, leading to IBVS performance degradation. Moreover, if the errors between the initial position and the desired one are large, with the visibility constraint, the camera motion may be affected by loss of features, or conflict with the robot physical limitations, or even lead to servoing failure. Hence numerous published literatures focus on improving the control performance and overcoming the visibility problem [63, 64].
Three operating points (2, 4 and 5) as shown in Table 2 are selected to design the initial controller K 0 and the rest of the operating points are selected to test the robustness of the system. Both K 0 and L d 0 are used to calculate the new open-loop transfer function L di for all the models which is needed to design K s . The pa- rameter of Laguerre basis is chosen as 1 and the order of the controller is considered to be 3. The desired closed-loop bandwidth is c 10 rad sec , so the desired open loop transfer function is chosen (based on ) as L d 0 10 s . This choice is suitable for damping the desired inter-area modes. Tuned weighting filters W 1 as a low pass filter and W 2 as high pass filter are shown in (14). The transfer function of the designed third order controller is shown in (15).
Today the controller commissioning of industrial used servo drives is usually realized in the frequency do- main with the open-loop frequency response. In contrast to that the cascaded system of position loop, veloc- ity loop and current loop, which is standard in industrial motion controllers, is described in literature by us- ing parametric models. Several tuning rules in the time domain are applicable on the basis of these paramet- ric descriptions. In order to benefit from the variety of tuning rules, an identification method in the time do- main is required. The paper presents a method for the identification of plant parameters in the time domain. The approach is based on the auto relay feedback experiment by Åström/Hägglund and a modified tech- nique of gradual pole compensation. The paper presents the theoretical description as well as the implement- tation as an automatic application in the motion control system SIMOTION. The identification results as well as the achievable performance on a test rig with a PI velocity controller will be presented.
Sliding mode control (SMC) has various attractive features such as fast response, good transient performance, order reduction and, particularly, robust with matched uncertainties, and is well known to be an effective way to handle many challenging problems of robust stabilization. Over the past decades, SMC has been one of the most popular control methods among the control community and has found wide applications to automotive systems, observers design, chemical processes, electrical motor control, aero-engineering and so on (see, e.g., Choi (2007), Gouaisbaut, Dambrine, and Richard (2002), Hu, Ge, and Su (2004), Hu, Ma, and Xie (2008), Jafarov (2005), Li and Decarlo (2003), Utkin (1992), Utkin, Guldner, and Shi (1999), Edwards, Akoachere, and Spurgeon (2001), Oucheriah (2003) and the references therein). Generally speaking, SMC uses a discontinuous control law (relays) to force and restrict the state trajectories to a predefined sliding surface on which the system has some desired properties such as stability, disturbance rejection capability and tracking (see Gouaisbaut et al. (2002), Li and Decarlo (2003) and Utkin (1992)).
Active suspension systems use actuator; an actuator (linear motor, hydraulic cylinder, etc.) parallel to the suspension systems is placed between the wheel and the vehicle body. When designing an active suspension, two important issues must be considered: the possible failure of the external energy source, and the transfer of a large quantity of mechanical energy in a structure that has the potential to destabilize the controlled system. However, the active suspension systems significantly improve car comfort, handling performance and driving safety, realizing an
Networked predictive controllers were proposed in [10, 11] where a sequence of future control predictions is generated on the side of the controller. An algorithm was employed at the actuator node to choose the appropriate control input according to the delay occurring in the forward channel. In [12, 13], the methods of [10, 11] were improved using a controller with switching gains which are chosen according to different values of the delay. In the mentioned approaches, a switched Lyapunov function which varies with respect to different delay values in the communication channels was used. However, the observer was located on the plant side, and, thus, delayed estimates of states are used in the controller; furthermore, the observer gain in [12, 13] is constant for different delay values.
3. CONCLUSIONS AND FUTURE WORK In this paper a robust methodology for the design of PID controllers for systems with bounded time variable delay using QFT and PSO algorithm is proposed. This combination delivers the best controller performance fulfilling the design specifications even in the worst cases as imposed by the uncertainty. The PSO algorithm converges to one of the possible optimal solutions with low dispersion as measured by the variance of the parameter vector converge value. Good results are obtained in an application for an actual control scheme queue in networks with changing operating conditions. Performance criteria of the control theory and the network analysis are both taken into account. Comparisons with published results are made and reinforce the methodology effectiveness. Simulations results using the non-linear model implemented with Simulink validate the design. The stochastic nature of the process was included by using random parameters in the network description. Good results are also expected with the use of a net simulator such as the ns2. A non trivial extension of this work would be to consider a network topology with multiple links.
The proposed FLC based FOPID controller uses a two dimensional linear rule base (Table:4.1) for the error, and fractional rate of change of error and the FLC output with standard triangular membership functions and Mamdani type inferencing . The triangular membership function is chosen over the other types like Gaussian, trapezoidal, bell-shaped, π-shaped etc. as it is easier to implement in practical hardware. In Fig.4.2, the fuzzy linguistic variables NB, NS, Z, PS, and PB with range [-1 1] represent negative big, negative small, zero, positive small and positive big respectively. The FLC output is determined by using centre of gravity method by defuzzification.
In this paper, a new comparative approach was proposed for reliable controllerdesign. Scientists and engineers are often confronted with the analysis, design, and synthesis of real-life problems. The first step in such studies is the development of a 'mathematical model' which can be considered as a substitute for the real problem. The mathematical model is used here as a plant. Fractional integrals and derivatives have found wide application in the control of dynamical systems when the controlled system and the controller are described by a set of fractional order differential equations. Here the stability and robustness of fractional order system is checked at the different level and it is found that the stability region is large in the complex plane. This large stability region provides the more flexibility for system implementation in the control engineering. Generally, an analytically or experimentally approaches are used for designing the controller. If a fractional order controllerdesign approach used for a given plant then the controlled parameter gives the better result.
This paper presented a robust PID controllerdesign for time delay system. This controller will be utilizing the gain-phase margin method; a specification-oriented parameter region in the parameter plane that characterizes all admissible controller coefficients sets can be obtained. Tuning of a PID controller refers to the tuning of its various parameters (P, I and D) to achieve an optimized value of the desired response. The basic requirements of the output will be the stability, desired rise time, peak time and overshoot. A compromise between the performances of the system with Ziegler-Nichols method is also included. Here we are measuring the plant response with and without delay condition with respect to original reference signal. Small modeling error is encountered and it is gradually detuned to a PID controller.
Considering the results for the non-parametric modelling in Fig. 2, where the total variances are 40 dB below the BLAs, and considering the matching quality in Fig. 4, for the application of structural control in Fig. 1, the identified model may be assumed as very accurate. However, in real applications of active vibration control, e.g., robotics in harsh environment (Montazeri et al. 2016), the noise contributions can be significantly higher. Therefore, to investigate the sensitivity of the model, the estimated covariance of the FRM estimates can be used to perturb the non-parametric model with realistic random noise with the normal distribution. For this purpose, three levels of perturbation with the magnitudes of 22 dB (case 1), 18 dB (case 2), and 8 dB (case 3) below the BLA are investigated. Considering the system dimension, 500 Monte Carlo simulation is performed. This requires significantly large system memory and processing power. In each sample, the subspace system identification is carried out on a perturbed BLA with the same 19, 18. In Fig. 6, the variation of the poles and zeros of the system for two inputs (piezo-actuator 1 and shaker) and one measurement output (acceleration) is shown for the sake of conciseness. In this figure, the first, second, and third rows indicate cases 1, 2, and 3, respectively while the nominal values are in black colour and the results from Monte Carlo simulations are shown in gray colour.
The literature regarding attacks in Networked ControlSystems (NCS) indicates that covert and accurate attacks must be designed based on an accurate knowledge about the model of the attacked system. In this context, the literature on NCS presents the Passive System Identification attack as a metaheuristic-based tool to provide the attacker with the required system models. However, the scientific literature does not report countermeasures to mitigate the identification process performed by such passive metaheuristic-based attack. In this sense, this work proposes the use of a randomly switching controller as a countermeasure for the Passive System Identification attack, in case of failure of other conventional security mechanisms – such as encryption, network segmentation and firewall policies. This novel countermeasure aims to hinder the identification of the controller, so that the model obtained by the attacker is imprecise or ambiguous, in such a way that the attacker hesitates to launch covert or model-dependent attacks against the NCS. The simulation results indicate that this countermeasure is capable to mitigate the mentioned attack at the same time that it performs a satisfactory plant control.