Abstract—The key point of the networked **control** systems is that conventional **generalized** **predictive** **control** algorithm cannot deal with random delays, packet losses and vacant sampling. In this paper, the time-stamped **generalized** **predictive** **control** (TSGPC) with fuzzy rectification is proposed to deal with this problem. The TSGPC use time- stamped to calculate the real delay and the LMS adaptive recursive algorithm of weight coefficient are used to estimate the induced delay. In order to reduce the transmission times between the controller and the actuator, a threshold that is set in advance would be used to decide whether the **control** information are sent to the actuator or not. The **control** information is ensured optional or sub- optional by using this method. The fuzzy rectification is used to **control** the increasing of the prediction error. The simulation results show that the TSGPC with fuzzy rectification can trace the desired output precisely.

Cascade **control** is one of the most popular structures for process **control** as it is a special archi- tecture for dealing with disturbances. However, the drawbacks of cascade **control** are obvious that primary controller and secondary controller should be tuned together, which influences each other. In this paper, a new Adaptive Cascade **Generalized** **Predictive** Controller (ACGPC) is intro- duced. ACGPC is a method issued from GPC and the inner and outer controllers of a cascade system are replaced by one cascade **generalized** **predictive** controller, where both loops model are up- dated by Recursive Least Squares method. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.

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number of effective approaches have been proposed to estimate, monitor and **control** the temperature and to guarantee Li-ion battery operation safety [3]. In terms of battery materials, novel electrolytes materials, and anode and cathode materials which can improve operation safety for Li-ion batteries under high temperature circumstance have been researched. Maleki et al. [7] researched high thermal Li-ion conductivity cells based on negative electrode material with high thermal conductive property. Kise et al. [8] presented a novel electrode which can improve safety for Li-ion batteries at high temperatures. For battery packages, researches are mainly focused on developing passive (i.e., using ambient environment) or active (i.e., an embedded source provides heating or cooling) systems to **control** the battery temperature under different situations. Based on the medium used, these systems can be further grouped as thermal management system using air [9], liquid [10], phase change materials [11], and combination of these mediums [12].

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In this study, **Generalized** **Predictive** **Control** (GPC) was applied to 6R robot manipulator for joint **control**. Newton- Raphson (N-R) method was used to minimize the cost function existing in GPC that represents errors between reference trajectory and actual trajectory in the **control** of robot. The results of angular path and angular velocity belonging to joints were examined and compared with results obtained from the Recursive Least Square implemented **Generalized** **Predictive** **Control**. Also, processing times of both algorithms were shown.

Because the orientation of satellites in station- ary orbits gradually is changed with time, atti- tude **control** is needed to maintain a satellite at- titude in the desired direction. Many **control** de- sign methods have been investigated to solve this problem until now. In general, these methods are classified as active or passive. Passive method has been utilized in [11]. Some of the active methods include **generalized** **predictive** **control** method [8], sliding-mode approach [3], **control** method based on Lyapunov [5], nonlinear **control** based on lin- ear matrix inequality [15], linear time-delay feed- back **control** [6], nonlinear H ∞ **control** [16], and Robust and optimal attitude **control** of spacecraft with disturbances [13].

With this purpose, we have carried out the MFPMPC scheme for this system using the Simulink-Matlab pro- gramming language, while the multi-fuzzy-based predic- tive model approach is organized based on the three fuzzy-based **predictive** model approaches and subse- quently the multi-fuzzy-based **predictive** **control** ap- proach is correspondingly organized based on the three fuzzy-based **predictive** model approaches. In these simu- lations, the tracking performance of the MFPMPC scheme is given in Figure 7, while the single model lin- ear **generalized** **predictive** **control** entitled SLMGPC scheme is used as a benchmark approach here. At first, the desired trajectory of the future output is given as 60 o C at 5 sec., while it is abruptly varied to 55 o C and

In this case, operation scheduling of BESS becomes critical for optimal and efficient use of it. In literature, researchers have proposed various operation scheduling approaches for BESS integrated with PV. Rule based **control** strategies for battery energy system for dispatching intermittent renewable resources were proposed in [7], [8]. A genetic algorithm was used in [9] to obtain optimal charging/discharging schedule of the BESS considering the line loss of distribution systems integrated with sizeable PV generation systems. A Lagrangian relaxation-based optimization algorithm was applied in [10] to determine the hourly charge/discharge commitment of battery in a utility grid. Deterministic optimization methods were used in [11] , [12] for system modelling and energy flow management in grid connected PV systems with battery. An energy management system for microgrid with PV generator was developed in [13], which is implemented in two parts - central and local management system, where communication is possible between both. An optimal power flow management algorithm was presented in [14] using dynamic programming considering the battery ageing into the optimization process and optimal **control** strategies regarding various optimization goals using the method of dynamic programming was pro- posed in [15]. Most of these approaches need predictions (load consumption profile and PV generation) to be employed in their optimization algorithm. There exists uncertainties and discrepancies in these predictions due to the intermittent nature of renewable energy sources and random load demand. However, in most cases these uncertainties have not been taken into consideration in designing algorithm for **control** of BESS in the literature.

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The present work proposes a modulated model **predictive** **control** technique with optimized response, including in the overmodulation region. The optimized response in the linear modulation region is solved based on the method presented in [15]. The optimized modulated actuation is found with a system of equations in which the error between the reference and model prediction is modulated to zero. In the overmod- ulation region, the modulated combination of the two active vectors that result in the fastest dynamic response is found. Furthermore, it will be proved that when the reference tracking error is too large, the optimized solution is achieved with a single voltage vector, smoothly converging to the FS-MPC solution, which is known to be optimal for large transient errors. A geometrical criterion that minimizes the current error between the reference and the achievable current vectors, located in the limit of the irregular predicted current error hexagon, is proposed. This criterion is similar to the one proposed in [20] but is applied to the current error, i.e. in the transformed space of the predicted current errors proposed in [15], optimizing the system response rather than the actuation voltage. The proposed technique is experimentally tested as the internal current loop of a permanent magnet synchronous machine drive, showing PWM-like switching performance in steady state, FS-MPC-like transient performance and a smooth transition between both operation conditions.

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Abstract: In this paper, subspace **predictive** **control** strategy is applied to design **predictive** controller. Given the state space model, the output estimations corresponding to the **predictive** output is derived to be one explicit function of the measured input-output data. Then using these output estimations, the problem of designing **predictive** controller is formulated as one optimization problem with equality and inequality conditions. In order to solve this constrain optimization problem, we use dual decomposition idea to change the original constrain optimization problem into an unconstrain optimization problem. So the classical gradient algorithm is put forth to solve the primal dual optimization problem. The problem of designing dual decomposition controller is studied for subspace **predictive** **control** strategy under fault condition. For state space equation with fault condition, we establish one function form between fault and residual using only input-output measured data sequence, and construct one least squares optimization problem to obtain fault estimation. The statistical property about residual is analyzed based on our derived output prediction, then the Kronecker product is used to derive the detailed structure corresponding to residual vector at every time instant. After substituting our output prediction into objective function of **predictive** **control**, one quadratic programming problem with equality and inequality constraints is considered. For solving this constrained optimization problem, fast gradient method is not suited for this complex optimization problem, as one regularization term is added in our objective function. So in order to solve this complex quadratic optimization problem, we propose a dual decomposition idea so that this dual decomposition idea can convert the former constrained optimization into unconstrained optimization, then one nearest neighbor gradient algorithm is given to solve its optimal value.

In a boiler the cold water increase leads temporarily to a decrease of the level as bubbles in the boiling water collapse. If the water feed becomes warmer, the level increases and achieves its new, higher steady-state value. Such a process is called inverse repeat or non-minimum-phase one, see Fig. 8. Fig. 9 shows PID and PFC **control**, respectively. In both cases standard tuning rules were used. It is clear that PFC performs better. Table 1 compares PFC to PI(D).

are sinusoidal, with the harmonic spectrum shown in Fig. 7 (b) and Fig. 7 (c). By comparing Fig. 7 with Fig. 3, it can be concluded that source current and output current harmonic spectrum validate the simulation results. However, the output current first harmonic amplitude is 3.75A, presenting a steady state error of 6.25%. This is a common problem related with model based **control** techniques, where the system model inaccuracies result in a steady-state error on the controlled variables. Regarding the harmonic content of the source current around the sampling frequency (10kHz), it can be noted a discrepancy between simulation and experimental results. This difference is related to the EMI filter on the input side, which helps to attenuate the high-order harmonics around the switching frequency and it is not included in the discretized model used for **control** design.

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For model based controllers such as MPC, it is important to assess the robustness of the technique towards parameter changes in the power system, especially if there are no parameter observer algorithms to adapt to the changes. Therefore, in order to test the robustness of the hybrid **control** scheme, parameter variation was introduced within the simulation model. Parameters that could change in actual drive systems due to operating temperature or other factors such as R s , L d,q ,

The **control** of the autoclave is designed on the basis of a mathematical model of the autoclave developed in [1], where the paper deals with the various types of heat transfer, basic heat-transfer equations, heat- transfer coefficients, heat flow, forced convection, conduction, thermal resistance, specific theories about dimensionless numbers like the Nusselt, Reynolds and Prandtl numbers, etc. The other process treated mathematically is pressure changing. The focus is the process inside the autoclave, which can be more simply described as heating, cooling and changing the pressure. Most of the used data are real and obtained from the autoclave manufacturer, but where this was not possible, the method of the model’s response fitting to the measured data was used. At the end of the paper we conclude that the designed model is usable for a variety of process **control**, due to the obtained very similar simulated and real process responses, considering some simplifications and using a curve- fitting procedure.

Using the results above, we fit a **generalized** Pareto distribution with ˆ γ = 0.213 and σ t = 130 to the k = 16 largest observations. The empirical distribution function, F k , the proposed GPD model and the **predictive** (PD) distribution functions are given in Figure 7. Figure 7 shows that the GPD gives a good fit to the data in the very largest observations, but poor model fit in the lower largest observations. The posterior **predictive** distribution gives reasonably good overall fit to the extreme data above t. However, a Q-Q plot of the **predictive** quantiles versus the observed, hereafter referred to as a posterior **predictive** quantile plot, is more informative. See Figure 7. From Figure 8, both the **predictive** distribution (PD) and the **generalized** Pareto distribution quantiles (estimated) are consistent with the observed annual maximum inflow data for the Okavango River. Notice, however, that that the estimated GPD quantiles and the predicted quantiles differ at the very largest observations. This weaker behavior of the ultimate tail of the annual maximum inflows distribution was observed in the **generalized** Pareto quantile plot (Figure 2). Overall, the posterior **predictive** quantile plot shows a good agreement between the predicted values and the observed values, implying the **generalized** Pareto distribution fits the annual maximum inflow data reasonably well.

DC–DC converters are switching systems due to the discrete input that assumes values in the set {0, 1}. This input corresponds to the on/off state of the switch. Although discrete actuation is a possible alternative, PWM converters are usually preferred in industrial applications. The main reason is the fixed switching frequency provided by the modulator which reduces the stress of the components. In addition, this permits to decouple switching and **control** frequencies, guaranteeing at the same time good ripple reduction and satisfactory sampling time. The model of PWM converters are averaged continuous model over a switching period . For the DC–DC buck converter considered in this paper, the averaged model is:

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In this work, we will focus on LTI plant models, which are (possibly) open-loop unstable. Thus, it is necessary to provide quantized **control** signals to the plant in a reli- able way to guarantee stability in the presence of data rate limitations, random packet delays and erasures. Our key idea is to design and use MDs in a novel way that dif- fers from how it is traditionally used. Traditionally, when the received descriptions are combined at the decoder, the approximation of a given source signal is improved. On the other hand, in the proposed work, when the

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In this work the possibility of using model based **control** in a metal forming process is investigated. The work is done in contribution to the MEGaFiT (Manufacturing Error-free Goods at First Time) project in which the University of Twente and Philips cooperate closely. Within the MEGaFiT project a demonstrator product is designed to study the feasibility of inline process **control**. The production step of interest is a bending process in which a force curve is measured. Finite Element (FE) analysis will be used to model this production step. The force curves from the finite element analyses are used to gain insight into the influence of (un)measurable parameters. However, as the used FE- model generally takes 1000 seconds to generate a force curve this model is too slow to be used in a **control** scheme. The 10 most influential parameters are therefore used to build a new, faster model to approximate the force curve. The approximation is based on a Proper Orthogonal Decomposition (POD) combined with Radial Basis Functions (RBF). This PODRBF-model can be used in an inverse analysis on which a **control** scheme can be based.

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4. RECURSIVE FEASIBILITY AND STABILITY Recursive feasibility is the main challenge for this approach. In contrast to conventional tube MPC, which uses linear- ity of the error dynamics and robust positive invariant (RPI) sets to allow the exact determination of constraint tightening margins for robustness, here the error dynamics are nonlinear and the constraint tightening is via scaling factors. In this section, we aim to establish conditions under which the proposed **control** scheme is recursively feasible and stable. Our approach here uses the notion of robust **control** invariant (RCI) sets (Raković et al., 2007): we show that, by suitable choices of scaling factors α x

Model **Predictive** **Control** to non minimum phase systems have been reported in literature [4, 8], showing that the non-minimum phase systems produce instability when the **control** horizon and the maximum **predictive** horizon are equal to 1, while [9, 10] demonstrated that this can be tuned to have stable behavior with unstable zero in the Single Input Single Output (SISO) case. MPC has instability problems because, for non-minimum phase plants, the controller achieves the optimal output by canceling the plant zeros, including the unstable zero, which leads to loss of internal stability of the feedback system [11, 12].

NMPC is a digital controller, that is, a method of discrete time. Thus when computation of the **control** is accomplished using a digital computer and at each sampling time, the results are applied online. NMPC obtains a ﬁnite horizon open-loop optimal **control** problem subject to system dynamics and constraints. Here, we investigate model **predictive** **control** for nonlinear **control** and optimization systems of the form [22]