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It has been proven that model predictive control (MPC) is an efficient method for closed-loop insulin delivery in clinical studies. This paper aims to design an observer-based fractional-order nonlinear MPC for type 1 diabetes mellitus (T1DM) patients. It is assumed that the proposed model is nonlinear and contains parametric uncertainty. To estimate unknown states, optimal non-fragile H∞ observer is designed for Lipschitz nonlinear fractional-order systems including parametric uncertainty and the existence of input disturbance. The min–max optimization-based robust fractional model predictive control (RFMPC) has been presented in the following for insulin delivery. Since sensor noise of continuous monitoring of interstitial glucose concentration is considered non-Gaussian, the performance of the proposed controller is improved under non-Gaussian measurement noise by selecting a proper cost function based on generalized correntropy, and as a contrast, the performance of the mean square error (MSE)-based controller is simulated. According to the results, not only is the performance of the proposed controller better under non-Gaussian situations but also effectively reaches the set point in the case of disturbance and uncertainty and provides higher control accuracy and robustness compared with the MSE-based MPC.

In this article, an observer-based adaptive hybrid attitude controller is presented that overcomes the topological obstructions associated with the global attitude stabilization. This hybrid controller consists of two major parts: an adaptive trimodal hybrid controller to eliminate $unwinding$ phenomenon, and a centrally synergistic potential function. In the adaptive trimodal hybrid controller, the first logic variable selects, which quaternion representation of the desired attitude should be tracked. Furthermore, an adaptive technique is introduced to collaborate with the second logic variable to adapt the hysteresis width. The third logic variable controls the mismatch between the auxiliary dynamical system and the actual attitude. Moreover, an auxiliary dynamical system and a nonlinear angular velocity observer are presented whose outputs are utilized in the proposed feedback control law to attain the necessary damping. Furthermore, a new centrally synergistic potential function is presented to cooperate with the trimodal hybrid controller to overcome the topological obstruction associated with the vector bundle structure. The robust global asymptotic stability of the consequent closed-loop system is guaranteed through a Lyapunov analysis. A comparative analysis in simulations illustrates the effectiveness and superiority of the proposed control scheme.

Model predictive controllers (MPC) with the two-loop scheme are successful approaches practically and can be classified into two main categories, tube-based MPC and MPC-based reference governors (RG). In this paper, an enhanced two-loop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints. The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint. Contrary to the MPC-based RGs, the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints. Additionally, the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation. Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.

The consensus control with the leader of delayed linear multi-agent systems under the relative state saturations is investigated in this article. Using an incidence matrix, the consensus problem of delayed multi-agent systems (time-varying input delays) with relative state saturation is converted into the stability problem of edge dynamics on the closed sets. Then, a new linear control protocol is designed using a saturation function that guarantees stability for all agents under relative state saturation constraints. With the appropriate Lyapunov–Krasovskii functional definition, sufficient conditions are created for the consensus of the agents. Meanwhile, the relative state saturations do not happen. Also, the agents track the leader’s path well, while bounded delays can be time-varying and arbitrarily fast. This consensus and stability are achieved by maintaining graph connectivity. Finally, the correctness of the above content is confirmed by simulating a practical example.

In this paper, output tracking of a geometric path for a nonlinear uncertain system with input and state constraints is considered. We propose an enhanced two-loop model predictive control approach for output tracking of a nonlinear uncertain system. Additionally, we propose an optimal version of output path following control problem to improve the controller synthesis. Satisfaction of the dynamical constraints of a system such as velocity, acceleration and jerk limitations is added to the problem introducing a new augmented system. The recursive feasibility of the proposed method is demonstrated, and its robust stability is guaranteed such that relaxation on the terminal constraint and penalty are achieved. To validate the theoretical benefits of the proposed controller, it is simulated on a SCARA robot manipulator and the results are compared with a two-loop model predictive controller successfully.

In this paper, a novel method for the design of robust nonlinear observer in the [Formula: see text] framework for Lipschitz nonlinear systems is proposed. For this purpose, a new dynamical structure is introduced that ensures the stability of observer error dynamics. Design innovation is the use of dynamic gain in the sliding mode observer. The additional degree of freedom provided by this dynamic formulation is exploited to deal with the nonlinear term. The performance of this stable [Formula: see text] observer is better than conventional static gain observers and the dynamic Luenberger observer. The compensator is designed in such a way that, while ensuring the stability of the closed-loop system, it prevents performance loss in the presence of the nonlinearities. By the proposed approach, the observer is robust to nonlinear uncertainties because of increasing the Lipschitz constant. Also, the design procedure in the presence of system and measurement noises is addressed. Finally, the simulation of our methodology is conducted on a nonlinear system to illustrate the advantage of this work in comparison with other observers.

In this paper, the electro-thermal performance of a 12V 50Ah lead-acid battery (LAB) under different charging profiles is evaluated. For this aim, a mesoscopic model for LAB is introduced so that the effects of chemical reactions on the state of charge, temperature, and battery voltage can be indirectly considered. Then, different charging profiles for a LAB including constant current (CC), constant current-constant voltage (CC-CV), pulse charging (PC), and reflex method are defined according to the battery's datasheet so that the average current of all profiles is the same. Considering the restrictions of pulsating profile, a power electronics interface capable of providing all charging profiles for the battery is also developed. Finally, the battery performance in terms of charging time, temperature, and battery voltage in presence of charging profiles are compared. A multi-step constant current, where the magnitude of the charging current is varied according to the battery temperature, is also proposed to enhance the performance of charging process. The results show that the MSCC can be a very promising approach to reduce the charging time as well as temperature rising at the end of charging process.

This paper develops an optimal relative output-feedback based solution to the containment control problem of linear heterogeneous multi-agent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output (i.e., the desired or safe region), but also optimizes their transient performance. The proposed optimal control solution is composed of a feedback part, depending of the followers' state, and a feed-forward part, depending on the convex hull of the leaders' state. To comply with most real-world applications, the feedback and feed-forward states are assumed to be unavailable and are estimated using two distributed observers. That is, since the followers cannot directly sense their absolute states, a distributed observer is designed that uses only relative output measurements with respect to their neighbors (measured for example by using range sensors in robotic) and the information which is broadcasted by their neighbors to estimate their states. Moreover, another adaptive distributed observer is designed that uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of knowing the complete knowledge of the leaders' dynamics by all followers. An off-policy reinforcement learning algorithm on an actor-critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requirement of knowing the leaders' dynamics by all followers. Finally, the theoretical results are verified by numerical simulations.

This paper studies the global stabilization of a rigid body attitude, a task that is subject to topological obstacles. Theseobstructions preclude the existence of a globally stable equilibrium point. Consequently, the rigid body attitude cannotbe globally stabilized by continuous feedback control laws. In order to resolve this challenge, this paper presents anobserver-based hybrid feedback control law. Thereafter, in order to derive the proposed feedback law, a new kind ofsynergistic potential functions is presented which induces a gradient vector eld to globally stabilize a given set. Moreover, the gradient of the proposed synergistic potential functions is utilized to derive a hybrid angular velocityobserver. The outputs of the proposed observer are employed to produce the necessary damping from the noisy measurements of the attitude. Furthermore, this paper considers two types of constraints: angular velocity constraints, and input torque constraints. Afterward, these constraints are formulated in terms of the Linear Matrix Inequalities (LMI) optimization problem to perform constraints satisfaction at all times. Moreover, this paper introduces a novel hybrid quantizer to deal with the problem of the low-price wireless network. This paper analyzes the global asymptotic stability of the reference set via the Lyapunov's method. Finally, a comparative study in simulations is provided to assess the performance of the proposed control technique.

This paper peruses the attitude control problem of a rigid body, a task that is subjected to topological obstructions. These topological obstructions express that there is no globally asymptotically stable equilibrium point for a system evolving on a compact manifold. Therefore, there exist no continuous or discontinuous feedback control laws to globally stabilize the desired rigid body attitude. Thereafter, this paper presents quaternion-based hybrid feedback to defeat these topological obstacles. The proposed control law is derived from a new kind of centrally synergistic potential functions which is introduced in this paper. This function induces a gradient vector field that globally asymptotically stabilizes the desired attitude. Finally, a simulation study depicts the superior performance of the proposed quaternion-based hybrid feedback control technique.

This paper aims to investigate an efficient numerical scheme for the optimal control of fractional-order dynamic systems. By using the Grünwald–Letnikov approximation for the fractional derivatives and introducing a new transformation in the calculus of variations, the fractional optimal control problem under consideration is converted into a linear programming problem. Then, the internal model principle is employed in order to extend the new scheme for the fractional dynamic systems affected by the external persistent disturbances. Numerical examples and comparative results verify the validity and applicability of the new technique.

In this paper, an online optimal distributed learning algorithm is proposed to solve leader-synchronization problem of nonlinear multi-agent differential graphical games. Each player approximates its optimal control policy using a single-network approximate dynamic programming U+0028 ADP U+0029 where only one critic neural network U+0028 NN U+0029 is employed instead of typical actorcritic structure composed of two NNs. The proposed distributed weight tuning laws for critic NNs guarantee stability in the sense of uniform ultimate boundedness U+0028 UUB U+0029 and convergence of control policies to the Nash equilibrium. In this paper, by introducing novel distributed local operators in weight tuning laws, there is no more requirement for initial stabilizing control policies. Furthermore, the overall closed-loop system stability is guaranteed by Lyapunov stability analysis. Finally, Simulation results show the effectiveness of the proposed algorithm.

This paper develops a novel controller called adaptive iterative learning sliding mode (AILSM) to control linear and nonlinear fractional-order systems. This controller applies a hybrid structure of adaptive and iterative learning control in to sliding mode method. It can switch between both adaptive and iterative learning control in order to use the advantages of both controllers simultaneously and therefore achieve better control performance. This controller is designed in a way to be robust against the external disturbance. It also estimates unknown parameters of fractional-order system. The proposed controller, unlike the conventional iterative learning control, does not need to apply direct control input to output of the system. It is shown that the controller performs well in partial and complete observable conditions. Illustrative examples verify the performance of the proposed control in presence of unknown disturbances and model uncertainties.

Nowadays wind energy introduces itself as one of the most important sources for electricity. Wind turbines are combined of different and complex mechanical and electrical components. In wind turbines any unbalanced parts will cause unwanted fluctuations which can be an intolerable problem. These fluctuations will be transferred to other parts of the turbine and will be observed on the different signals specially the pitch signal. Fluctuations may cause fatigue and unwanted loading in mechanical components and actuators. In this paper after explaining the structure and components of a typical wind turbine with permanent magnet synchronous generator, an idea to overcome the pitch signal fluctuations is represented. This method is applied to the gain scheduled PI pitch controller of an experimental simulator for 100 kW wind turbine.

This article presents a novel technique for the design of a robust dynamic nonlinear observer in the H ∞ scheme for the Lipschitz nonlinear model of the wind turbine. It is a challenging problem due to the aerodynamic nonlinearity of blades. A drive train of the 100 KW wind turbine benchmark model, developed by Aalborg University and KK-electronic a/c, is conducted to exhibit the effectiveness of the proposed observer approach. The new Lipschitz constant definition for this system is expressed. Based on the new dynamic method, the Lipschitz constant is increased. Therefore, the operating region is increased. A new dynamic sliding mode observer is introduced for torque estimation in the drive train. The design methodology is the use of dynamic gain instead of static one in the sliding mode observer. The dynamic observer design offers an extra degree of freedom over the classical static gain observer. We established the conditions for the suggested dynamic observer stability. The proposed approach provides the observer robustness against nonlinear uncertainties due to the maximizing the Lipschitz constant. Moreover, the design procedure with the effect of system and sensor noises is considered. Results illustrate the advantage of this work in comparison with other observers. Finally, the results are evaluated with the wind turbine simulator software FAST.

A method was introduced for control of the detachment frequency in automatic GMAW process to achieve more regularity in drop detaching. The deposition area, heat input and voltage to current ratio were regulated as the performance indicators. This method was designed based on a three-layer cascade control strategy. A four-input four-output matrix transfer function was assigned to the system using the experimental data. A decoupling MIMO controller was designed for the internal loop. In the middle control loop, a pattern was introduced to neutralize the angle effects on the drop detachment. In the external control loop, the detachment frequency was estimated by a Short Time Fourier Transform (STFT) block. A controller was designed to track the desired frequency reference. The results were evaluated by implementation on an automatic industrial pipeline GMAW system.

Wind turbine blade manufacturing is one of the most important projects executed in Sun and Air Research Institute (SARI). The Vacuum Infusion Process (VIP) is used in the blade manufacturing. Each blade concluded from layers of composite combined with resin. This combined material is heated based on the predefined temperature profile. In the heating process, the most important task is tracking the temperature profile and regulating the blade temperature with the reference signal. Therefore the temperature control should be executed through accurate control systems. In this paper the heating element voltage is controlled by using adaptive GPC and EDMC controllers. Controlling this voltage leads to blade temperature control. Simulation and control design is done in MATLAB Simulink. There are some limitations in the heating system that obligates the designer to utilize the adaptive control system. These limitations are listed as delay in heating system, different transfer function parameters in various blade sections and lack of the cooling system. In fact there is not a cooling system and therefore overshoot in the blade temperature is not acceptable and controllable during the heating process. It is necessary to utilize the adaptive control to overcome overshoot and over-heating as well as other limitations in heating system. The simulation results illustrate that the temperature of heating process alters smoothly moreover it tracks the desired temperature profile without any overshoot.

In this paper a fuzzy robust nonlinear model predictive controller based on state estimation for Stewart platform is designed. The control law is based on prediction model, which is carried out via Uncertainties fuzzy estimator. The optimal control is computed directly from the minimization of receding horizon cost function. There is no need to an online optimization. A fuzzy estimator is designed to deal with system uncertainties such as unknown external disturbances, unmodeled quantities and parametric uncertainties. The global stability closed loop system is proved analytically via Lyapunov stability theory. The performance of the proposed method is compared with PID controller.

In this paper the detailed model of the 100 KW wind turbine with permanent magnet synchronous generator constructed at the Sun and Air Research Institute of Ferdowsi University of Mashhad, is extracted by using mathematical equations and practical simulation. There are a lot of limitations in this turbine performance and control. Therefore the controller designing is a complex task and adaptive control with considering system limitations will lead to an appropriate approach because in that case both the limitations and uncertainties are investigated during the design procedure. In this paper a practical pitch controller is designed based on the self-tuning regulators (STR) and minimum variance (MV) algorithm. The designed controller is applied to the practical simulator and compared with the PI controller. These controllers are utilized and compared to regulation of the pitch angle in both simulation and practical application. The results show that the MV-STR pitch controller leads to smoother pitch signal and generator speed in the same wind conditions. The novelty of this paper is achievement to a detailed model and applying adaptive control method to an experimental case.

In this paper, our goal is to define a method of welfare maximization in which the level of customers' social welfare, before and after changing the pricing policy from a flat pricing method to a real time one, remain unchanged. Each user is given a smart meter that monitors their electricity usage and at the same time informs the customers of the new prices at the start of each hour. The new price signal is comprised of time and usage dependent parameters. This new pricing method encourages users to reshape their usage algorithm towards a responsible model in which the load peak and electricity costs for the users are reduced. In order to do so a cost function named welfare function, is introduced that minimizing it, will result in fulfillment of aforementioned goals. By implementing these new pricing methods and optimizing power consumption, the load peak will reduce by 21% and the cost of users' electricity bill will decrease by 16% and at the same time the total welfare level will remain nearly the same.