As a future study, the development of Kalman filter in order to detect the leakages in pipelines based on LabVIEW could be investigated. Also different types of **controller** could be developed such as DMC(Dynamic Matrix Control), OGPC(Optimal **Generalized** **Predictive** **Controller**). An ideal control may well be actualised with the purpose of reducing the expense of the distribution method within the system beneath sure operation

In this paper, a hardware in the loop simulation (HIL) is presented. This application is purposed as the first step before a real implementation of a **Generalized** **Predictive** Control (GPC) on a micro-grid system located at the Military University Campus in Cajica, Colombia. The designed GPC, looks for keep the battery bank State of Charge (SOC) over the 70% and under the 90%, what ensures the best performance in the battery bank according its technical specifications. The GPC algorithm was embedded on a STM32 microcontroller and the micro-grid model was embedded on an ARDUINO MEGA microcontroller.

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dependent variable. ANOVA capabilities were first introduced in [15]-[16]. In [16][17], ANOVA is used for path-finding, and it is used in soft computing in reference [18], also in [19] microwave applications of ANOVA can be found. In [20] ANOVA is used to tune the **Generalized** **Predictive** Controllers (GPC) for FOPDT plants. More recently, [11] has employed ANOVA for tuning of **Generalized** **Predictive** **Controller** (GPC) for Second Order plus Dead time (SOPDT) models and a new analytical equation for is obtained.

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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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Abstract-The Newton-Raphson method is one of the most widely used methods for minimization. It can be easily **generalized** for solving non-linear differential equation systems. In this study, **Generalized** **Predictive** **Controller** (GPC) was applied to a 6R robot manipulator based on joint control. Newton-Raphson (N-R) method was used to minimize the cost function existing in the GPC that represents errors between reference trajectory and actual trajectory in the control of robot. The Newton-Raphson method requires less iteration numbers for convergence and reduces the calculation. This study presents a detailed derivation of the **Generalized** **Predictive** Control algorithm with Newton-Raphson minimization method. The results of angular path and position errors belonging to joints were examined and compared with Recursive Least Square (RLS) implemented **Generalized** **Predictive** Control. The simulation results showed that Newton-Raphson method improved control performance of the GPC.

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Consequently, a new fuzzy model **predictive** **controller** is designed by using the proposed T-S fuzzy model as its dynamic **predictive** model and by using (V)ABC algorithm as its rolling optimizer. Taking the superheated steam temperature in power plant as the example, some experiments were conducted to validate the performance of the proposed fuzzy model **predictive** **controller**. The experimental results show that the proposed fuzzy model **predictive** **controller** has powerful performance. In addition, we compare our method with the popular **generalized** **predictive** **controller** and PI. It shows that our method outperforms the **generalized** **predictive** **controller** and traditional PI **controller**.

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In this study, a sliding mode control with **Predictive** PID sliding surface has been proposed. An unstable plant is used for the per- formance analysis. Simulation was carried out using MATLAB to test the effectiveness of the proposed method. In the proposed method, a hyperbolic tangent function has been used in order to avoid the chattering phenomena. The proposed **controller** en- sures the invariance property against parameter uncertainties, set point variations, and disturbances compared with **Predictive** PID **controller** and **Generalized** **predictive** **controller**.

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MPC is a **controller** that uses an identifiable model of a certain process to predict its future behaviour over an extended prediction horizon and the aim is to minimize the cost function. The manipulated variable moves is implemented at a sampling instants over the control horizon is evaluated. The feedback is achieved by implementing the first move only and then the sequence will be repeated again and this is known as moving horizon concept [8] Most of the research work done is the application of MPC **controller** in specific processes such as gas recovery unit [9], gaseous pilot plant [7], shell and tube heat exchanger [10] and pasta drying process[11] Large prediction horizon improves nominal stability of the closed loop but too large of prediction horizon will take long computational time [7] Advance control strategy such as MPC can lead to energy efficiency. This can implement by combining the control structures with online process measurements which can reduce the energy consumption [11].

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DC motors are used extensively in adjustable speed drives and position control applications. Their speeds below the base speed can be controlled by armature voltage control. Speeds above the base speed are obtained by field-flux control. As speed control method for DC motors are simpler and less expensive than those for the AC motors, DC motors are preferred where wide speed range control is required. There are various techniques to control the speed of DC motor. One such technique is implemented in this paper in order to control the speed of DC Motor using MPC **controller** toolbox in MATLAB. The term Model **Predictive** Control does not designate a specific control strategy but a very ample range of control methods which make an explicit use of a model of the process to obtain the control signal by minimizing an objective function. These design methods lead to linear controllers which have practically the same structure and present adequate degrees of freedom. The various MPC algorithms only differ amongst themselves in the model used to represent the process and the noises and the cost function to be minimized. The MPC Toolbox is a collection of functions

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12 As with control of mobile robot systems, the USV control problems can be divided into setpoint position [13– 18] and trajectory tracking [19–27] control. In this paper, several examples of nonlinear control laws for the USV setpoint and trajectory tracking control problems are presented. First, SMCs are presented for tracking [27] and setpoint control [28]. The advantage of these controllers is that they require very little computation and can be implemented on the small scale model USV system. A disadvantage of these controllers, however, is that tuning the control parameters can be very non-intuitive and often the optimal parameters for one initial condition can yield poor performance given different initial conditions. Next, a new MPC method is applied to trajectory tracking and setpoint control [29, 30]. MPC is based on solving an open- loop optimal control problem at each sampling instant. As a result, open-loop optimal performance can be achieved regardless of initial conditions, constraints or disturbances. Another advantage of this method is that the setpoint and trajectory tracking controllers use the same formulation. The disadvantage, however, is that the **controller** requires significant computation time making it challenging to implement on-line for small scale USV systems with relatively fast dynamics. Therefore, a cascade MPC and SMC is presented which effectively combines the fast computation of the sliding mode tracking **controller** with the optimal performance of the MPC.

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ABSTRACT: Distribution Static Compensator (DSTATCOM) is a shunt compensation device that is used to solve power quality issues. The control strategy of the DSTATCOM plays an important role in reducing current harmonics and power factor correction. In this paper a model **predictive** **controller** based DSTACOM for power quality improvement is done. In model **predictive** current control method, a discrete-time model of the system to predict the future current behaviour for all the possible voltage vectors generated by the DSTATCOM, and then the vector which minimizes a cost function is selected and applied. This **controller** allow DSTATCOM to tackle power quality issues by providing power factor correction, harmonic elimination, load balancing and voltage regulation. MATLAB based simulink model is used to determine the effectiveness of the proposed controllers.

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Regarding the today’s wide application of the **predictive** control systems to control industrial and complex systems due to its robustness, optimality and ability to face uncertainty. The use of this method in controlling and reinstating the stabilizing loop of a two axis seeker is proposed. Now We shall examine this proposed seeker model a missile along with motion and flight dynamics equations. Such a consideration, a two axis seeker model with this control method has not been introduced in any article or publication up to now. We shall continue to provide some research in this field.

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This paper addresses the problem of designing state feedback robust model **predictive** **controller** with input constraints for a class of time-delay singular systems with polytopic uncertainty. The existing sufficient conditions of the robust **predictive** **controller** are presented using Lyapunov stability theory and linear matrix inequality (LMI) method. At each sample time, the **controller** could be determined when these conditions have feasible solutions. We get a piecewise state feedback control sequence { K k } k ∞ = 0 when k changes from 0 to ∞ . The closed-loop system which is composed of piecewise state feedback control sequence { K k } k ∞ = 0 is regular, impulse-free and asymptotically stable. Finally, a numerical example demonstrates the applicability of the proposed approach.

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A modulated model **predictive** control with optimized over- modulation has been proposed in this work. In steady state, the constant switching frequency achieved with this technique is an important advantage because it solves the problem of distributed harmonic content in FS-MPC. The modulated voltage actuation of the power converter improves the tracking of the current references and achieves the desired concen- trated PWM switching harmonics. On the other hand, the overmodulation optimization introduced in this work improves the performance of the method during large demand changes, achieving minimum time transients. With this optimization, the inherent voltage error generated when the equivalent reference is located outside of the actuation capability of the inverter is such that the resulting current error is minimized using a sim- ple geometrical consideration. This optimized overmodulation guarantees the fastest dynamic response possible, achieving the same transient performance as FS-MPC for large tran- sients. Furthermore, the use of overmodulation with two active

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Static Var compensator (SVC) improves the system performances by controlling the magnitude of voltage. Thyristor controlled phase angle regulator (TCPAR) controls the phase angle of voltage, while thyristor controlled series compensator (TCSC) changes the effective impedance of transmission line to the system performance. The unified power flow **controller** (UPFC) offers to combine all three functions in one device [4,5]. The control of system parameters can be carried out concurrently or sequentially with transfer from one type control (phase shift) to another one (series compensation) in real time. The other devices of FACTS **controller** family are static compensator (STATCOM), static synchronous series compensator (SSSC), **generalized** unified power flow **controller** (GUPFC) and interline power flow **controller** (IPFC) etc. [6-8].

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(1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, Shandong, China; 2. Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Taian 271018, Shandong, China; 3. College of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China) Abstract: To improve the trajectory tracking robust stability of agricultural vehicles, a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model **predictive** control was presented. Through the proposed method, the path tracking problem can be divided into two problems with speed and steering angle constraints: the trajectory planning problem, and the trajectory tracking optimization problem. Firstly, the nonlinear kinematics model of the agricultural vehicle was discretized, then the derived model was inferred and regarded as the prediction function plant for the designed **controller**. Second, the objective function characterizing the tracking performance was put forward based on system variables and control inputs. Therefore, the objective function optimization problem, based on the proposed prediction equation plant, can be regarded as the nonlinear constrained optimization problem. What’s more, to enhance the robust stability of the system, a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control. To validate the theoretical analysis before, the Matlab simulation was performed to investigate the path tracking performance. The simulation results show that the **controller** can realize effective trajectory tracking and possesses good robust stability. Meanwhile, the corresponding experiments were conducted. When the test vehicle tracked the reference track with a speed of 3 m/s, the maximum lateral deviation was 13.36 cm, and the maximum longitudinal deviation was 34.61 cm. When the added horizontal deviation disturbance Yr was less than 1.5 m, the **controller** could adjust the vehicle quickly to make the test car return to the reference track and continue to drive. Finally, to better highlight the **controller** proposed in this paper, a comparison experiment with a linear model **predictive** **controller** was performed. Compared to the conventional linear model **predictive** **controller**, the horizontal off-track distance reduced by 36.8% and the longitudinal deviation reduced by 32.98% when performing circular path tracking at a speed of 3 m/s.

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Abstract-- The processes in most of the industries are highly non-linear and dynamic. The quadruple tank system is a benchmark system used to analyse the nonlinear effects in a multivariable process. The quadruple tank process is thus used to demonstrate coupling effects and interactions occurring in multivariable control systems. This project presents a neural network **predictive** **controller** for a quadruple tank system. The process data will be obtained from the mathematical model of the laboratory scale experimental setup. The model obtained from training the system via neural network will be used in controlling the quadruple tank by neural network **predictive** **controller**. The simulation results will be compared with the closed loop response and constrained and unconstrained model **predictive** control algorithm results.

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In this work, a modified nonlinear **predictive** PI **controller** (MNPPI) has been developed for control of conical tank level system. This control scheme (MNPPI) has been derived from a Nonlinear PI control structure. The MNPPI **controller** has implemented for conical tank system and simulations studies are carried out for different operating points, based on simulations result the conclusions are drawn and presented.

time compensations, and permits pulse-width modulator replacement. However, these kinds of applications present disadvantages related to oscillations and instability created from unknown load parameters. One advantage of the proposed algorithm is that it fits well in active power filter applications, since the power converter output parameters are well known. These output parameters are obtained from the converter output ripple filter and the power system equivalent impedance. The converter output ripple filter is part of the active power filter design and the power system impedance is obtained from well- known standard procedures. In the case of unknown system impedance parameters, an estimation methodcan be used to derive an accurate R–L equivalent impedance model of the system. This paper presents the mathematical model of the 4L- VSI and the principles of operation of the proposed **predictive** control scheme, including the design procedure. The complete description of the selected current reference generator implemented in the active power filter is also presented. Finally, the proposed active power filter and the effectiveness of the associated control scheme compensation are demonstrated through simulation results.

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In this project, a control system that coordinates the operation of multiple distributed generation inverters in a microgrid for grid-connected and islanded operations will be presented. The proposed **controller** for the distributed generation inverters is based on a newly developed model **predictive** control algorithm which decomposes the control problem into steady-state and transient sub problems in order to reduce the overall computation time.