saeed shamaghdari

Satellites attitude control based on their mission always is one of the main challenges in attitude control. Actually, in a certain class of satellites when satellites placed in a predetermined attitude or axis, sometimes an oscillating or chaotic behavior occurs, therefore, various models have been presented to analyze of this class. In this paper, H∞  Robust output feedback control under saturation is designed for this class of satellites that are also under external disturbance. The main point in this paper actually is design of the static output feedback (SOF) controller in the mentioned conditions in the form of solving a linear matrix inequality (LMI) problem. Then, in the same way, the stable region (B∈) based on Lyapunov's theorem is obtained, which finally enlarges the region of attraction (ROA) for this control system. The proposed method was implemented on the presented model and the results showed that in addition to the appropriate speed of the system states in convergence, the control signal did not enter the saturation area and the system has become stable with the least cost and energy. The procedure design of output feedback controller is specified in the form of pseudo-code.

In this article, a method for designing a fault-tolerant optimal attitude tracking control (FTOATC) for a quadrotor UAV subject to component and actuator faults is presented. The proposed fault-tolerant method is based on safe reinforcement learning (SRL) and is capable of ensuring input and state constraints without the need for prior knowledge of the quadrotor dynamics. To this end, the proposed optimal method is presented with a dual neural network (NN) structure consisting of identifier-critic neural networks. In the identifier NN update law, in addition to considering the variable forgetting factor dependent on measurement noise, the experience response method is used, which increases convergence speed and robustness to measurement noise and reduces estimation error. In this method, solving the constrained FTOATC problem is equivalent to solving an unconstrained optimal stabilization problem for an augmented system, where control input constraints and states are guaranteed by selecting suitable cost functions on the input signal and appropriate control barrier functions (CBF)on the states, respectively. Furthermore, fault detection is performed without the need for any model or filter bank, simply by comparing the residual value of the Hamilton-Jacobi-Bellman (HJB) equation with a predetermined threshold. The Uniformly Ultimately Boundedness (UUB) of identifier and critic NN weight errors and, as a result, the convergence of the control input to the neighborhood of the optimal solution are all proved by Lyapunov theory and the performance of the method is validated through simulation results.

In this paper, a decentralized energy management system is presented for intelligent microgrids with the presence of distributed resources using reinforcement learning. Due to the unpredictable nature of renewable energy resources, the variability of load consumption, and the nonlinear model of batteries, the design of a microgrid energy management system is associated with many challenges. In addition, centralized control structures in large-scale systems increase computational volume and complexity in control algorithms. In this paper, a fully decentralized multi-agent structure for a microgrid energy management system is proposed and the Markov decision process is used to model the stochastic behavior of agents in the microgrid. Electrical and thermal distributed resources, batteries, and consumers are considered intelligent and independent agents. They have the learning ability to explore and exploit the environment in a fully decentralized manner and achieve their optimal policies. The proposed method for hourly microgrid management is model-independent and based on learning. The method maximizes the profits of all manufacturers, minimizes consumer costs, and reduces the dependence of the microgrid on the maingrid. Finally, using real data from renewable energy sources and consumers, the accuracy of the proposed method in the Iranian electricity market is simulated and verified.

In this paper, a satellite attitude control system with the reaction wheels based on suboptimal robust tube based model predictive control is designed. The satellite receives many external bounded disturbances in space. Due to the fact that disturbances have a specific range, it is possible to satellite attitude control by using a robust predictive controller based on a tube. But since the satellite has a system with complex dynamics, the challenge of increasing the volume of calculations arises when calculating the minimal Robust Positive Invariant set. The challenge of increasing the volume of calculations of this set (tube) in complex systems such as satellites is caused by the large number of state variables of the system. The large number of system state variables causes an exponential increase in the volume of calculations due to the formation of multiple Minkowski sums in the tube calculation. In order to solve this challenge, a new solution of robust predictive controller based on suboptimal tube has been presented. This solution reduces the volume of tube calculations. Simulation has been done for the accuracy of the design of the predictive controller based on the suboptimal tube to attitude control of satellite.

This paper presents distributed training approach for a nonlinear and heterogeneous multi-UAV system to solve a safe and optimal formation control problem. The objective of control is to ensure safety while achieving optimal performance. In this regard, the position and attitude controllers are considered in series. First, the optimal formation control design is defined as the optimal performance in position control and is modeled by the cost function. In this article, with the integration of cost functions and local control barrier functions (CBFs), a novel distributed optimization problems are introduced. Existing the local CBF in the augmented cost function ensures the safety of the position control, and as a result, collisions do not occur along the path of UAVs. The proposed method considers the safe and optimal position controllers by solving unconstrained optimization problems instead of constrained ones. In the next stage, the reference attitudes are driven by virtual position control. The attitude tracking optimal control is considered the optimal performance in the attitude control, and the related cost function models it. Finally, the stability and safety of the proposed controllers are proven. These optimal and safe policies are obtained sequentially using off-policy multi-agent reinforcement learning (MARL) algorithms which do not require knowledge of UAVs' dynamics. The proposed algorithms are validated by simulating the formation control problem of 6 UAVs with collision avoidance constraints.

In this paper, we propose a method for sub-optimal control of time-varying polynomial systems and use it for pursuits guidance law design. Since, engagement equations between pursuit and target are depend on the range between them and this range is varying during the flight, guidance law designer is faced with a time-varying system. The developed methods for control of time-invariant systems are not directly applicable for time-varying systems. One of the conventional approaches for pursuits guidance law design is the optimal control. Approximate dynamic programming is a well-known method for solving the optimal control problem. One of the challenges of using this method for control of nonlinear time-varying systems is the difficulty of solving the Bellman equation. In the proposed method of this paper, solving the Bellman equation has been relaxed with solving a sum-of-squares optimization problem. It will be proved that the designed control policy with this method is globally exponentially stabilizing. Finally, performance of the proposed method for pursuits guidance will be illustrated with numerical simulations.

In this article, a new approach is proposed to design robust switching gain-scheduled dynamic output feedback control for switched uncertain continuous-time linear parameter varying (LPV) systems. The proposed robust switching gain-scheduled controllers are robustly designed so that the stability and H2-gain performance of the switched closed-loop uncertain LPV system can be guaranteed even under controller switching determined by scheduling parameters. Hysteresis switching law is exploited for the switching controller synthesis. The system matrices are supposed to depend polynomially on both the scheduling and uncertain parameters which are assumed to belong to intervals with a priori known bounds. The proposed approach is formulated in terms of solutions to a set of parameter-dependent LMIs using parameter searches for two scalar values. Finally, the method is applied to electromagnetic suspension system to verify the applicability of the presented approach.

In this paper, a satellite attitude control system (SACS) based on tube-based robust model predictive control (TMPC) methodology is designed which is robust to disturbances. All Euler angles and their derivatives are ensured not to deviate more than a determined limit under those disturbances with known bounds. It is conducted based on the concept of the minimal robust positive invariant (mRPI) set. Actuators and Euler variables constraints could be considered in the SACS. The dynamics are guaranteed to be robustly stable. Given that the satellite dynamics consists of a great number of states, it is not possible to implement A TMPC scheme on the SACS in real-time. The number of satellite system states in this article is 6. Which has practically increased the volume of calculations. Makes it impossible to use TMPC theory. A theory will be presented based on reduced calculations to demonstrate a practical TMPC scheme that results in a real-time SACS.

This paper investigates a new scheme in the design of a dynamic observer for generator torque estimation in the wind turbine. Using a dynamic sliding mode observer design that has never been used for a wind turbine, we increase the Lipschitz constant, which is equivalent to increase the operation range and increasing the observer's robustness to the nonlinear term. Design innovation is the use of dynamic gain in the design of the SMO and guarantee of asymptotic stability condition by  optimal control problem. The additional degree of freedom proposed by this dynamic formula is used to cope with the nonlinearity. Finally, using the real wind profile, the design procedure is implemented on the 100 kW wind turbine of the Sun and Air Research Institute and the results are also evaluated with the wind turbine simulator software FAST. Also, as the second contribution of this paper, by adding process noise and measurement noise to the turbine, the superiority of the proposed algorithm over others is investigated.

In this paper, an algorithm is proposed to improve the design of constrained PID controller based on convex-concave optimization. This design method is based on the optimization of a performance cost function, taking into account the stability and efficiency constraints with frequency domain analysis in which the concepts of sensitivity and complementary sensitivity has been used. It is shown, using a counter example that the previous methods are not effective for some systems, the optimization problem becomes unbounded and interrupts. To solve this problem, attentions are paid to conditions that the optimization problem fails to have any response, and then previous limitations are eliminated by representing a new designing method. The performance of the proposed method is shown by applying it to the counter example. In the end, the controller is designed for an unstable system.

This paper proposes a new method of gain scheduling control design for a nonlinear system which is described as linear parameter varying form. A performance measure based on Linear Matrix Inequality is introduced. To consider stability and performance measures in design process، the H∞ loop-shaping method is used to design the local controllers، which can be described as state feedback observer based structure. By introducing the stability and performance covering condition for the linear parameter varying system، a new interpolation law is proposed، and it is proofed that the resultant controller can preserve the performance measure for the observer based structure for all values of the scheduling parameter. Also the closed loop stability is guaranteed. The method is successfully applied on the control of a well-known benchmark system، namely، the autopilot for a pitch-axis model of an air vehicle. The performance and effectiveness is evaluated against disturbances and parameter uncertainties using computer simulation.