When the process is highly uncertain, even linear minimum phase systems must sacrifice desirable feedback CONTROL benefits to avoid an excessive ‘cost of feedback’, while preserving the robust stability. In this paper, the CONTROL structure of SUPERVISORY based SWITCHING Quantitative Feedback Theory (QFT) CONTROL is proposed to CONTROL highly uncertain plants. According to this strategy, the uncertainty region is suitably divided into smaller regions. It is assumed that a QFT CONTROLler-prefilter exits for robust stability and robust performance of the individual uncertain sets. The proposed CONTROL architecture is made up by these local CONTROLlers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the CONTROLler corresponding to the best fitted model. A hysteresis SWITCHING logic is used to slow down SWITCHING for stability reasons. Besides, each CONTROLler is designed to be stable in the whole uncertainty domain, and as accurate in command tracking as desired in its uncertainty subset to preserve the robust stability from any failure in the SWITCHING.

In this paper, a SUPERVISORY based SWITCHING strategy is employed to solve the H2/H¥ multi objective CONTROLler design. Using linear matrix inequalities, two distinict CONTROLlers are designed to meet the H2 and H¥ performance specifications. A state realization for each CONTROLler transfer matrix is found such that the asymptotical stability of the closed-loop SWITCHING system is guaranteed for any SWITCHING sequence. A supervisor is used to diagnose the exogenous input changes and switch to the relevant CONTROLler. Simulation results show that SWITCHING strategy improves the performance of the CONTROLler and reduces the conservation in comparison with the common H2/H¥ CONTROLler.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust ADAPTIVE CONTROL method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) CONTROLler. By applying a single line to ground fault in the network, the robustness of the designed CONTROL systems is demonstrated. Also, the simulation results of the method used in this paper are compared with CONTROLlers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust ADAPTIVE CONTROL method based on dynamic programming for the stabilizing design of the power system and the complementary SVC CONTROLler. The performance of the CONTROL method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

In this paper, the problem of SUPERVISORY based SWITCHING Quantitative Feedback Theory (QFT) CONTROL is proposed for the CONTROL of highly uncertain plants. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a QFT CONTROLler-prefilter exists for robust stability and performance of the smaller uncertainy subsets. The proposed CONTROL architecture is made up by these local CONTROLlers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor compares the candidate local model behaviors with the one of the real plant and selects the CONTROLler corresponding to the best fitted model. A hysteresis SWITCHING logic is used to slow down SWITCHING for stability reasons. It is shown that this strategy improves closed loop performance, and can also handle the uncertainty sets that cannot be tackled by a single QFT robust CONTROLler. The multirealization technique to implement a family of CONTROLlers is employed to achieve bumpless transfer. Simulation results show the effectiveness of the proposed methodology.

The purpose of this paper is design of a three axis attitude CONTROL algorithm for sample remote sensing satellite. According to the orbital and physical characteristics of sample satellite, three attitude CONTROL algorithms are designed. These algorithms can simultaneously CONTROL the satellite’s attitude and its rotational rate in the presence of parametric uncertainties, unknown and nonlinear dynamics and variable disturbances. The proposed structure provides the possibility of designing reduced order CONTROL system and increases stability and robustness of system against the uncertainties. This approach can also lead to simplification at implementation of CONTROL system. The first algorithm is inverse dynamic based CONTROL which is nonlinear and static CONTROL method. Second algorithm is designed based on ADAPTIVE CONTROL principal. This way has the capability to CONTROL satellite in the presence of uncertainties. In this method, when the value of angular velocity is large, the magnitude of CONTROL effort is high. This property results in increased power consumption and actuator saturation. On the other hand, the ADAPTIVE CONTROL algorithm in some conditions becomes unstable. So using this method is only suitable for pointing and imaging mode. Hence to solve the problem of this method, SUPERVISORY ADAPTIVE CONTROL method is proposed. in the structure of SUPERVISORY ADAPTIVE CONTROL, a high level supervisor which works based on fuzzy logic determines the contribution of low level CONTROLlers according to the characteristics of them. In this CONTROL algorithm, accuracy is higher than the tow algorithms that were introduced above, while CONTROLling torque will be less. This approach avoid the saturation of actuators while have the advantages of other two algorithms are presented as well. The advantages of SUPERVISORY algorithm can be pointed to the proper operation and optimum performance in different CONTROL modes. All three designed CONTROL algorithms are evaluated with simulations.

In this paper, we present a new method for designing higher order sliding mode CONTROLler (HOSMC) with ADAPTIVE SWITCHING gain by defining a new PI sliding surface and employing a linear state feedback. The objective is to force the outputs of a nonlinear SISO system and their derivatives up to a certain order, track the states of a linear system with desired properties. The main property of proposed CONTROLler is that it does not need an upper bound for the uncertainty and moreover, the SWITCHING gain increases and decreases according to the system circumstances by employing an adaptation procedure. Then, chattering is removed completely by using the HOSMC with a small SWITCHING gain. Finally, we have used the proposed method to CONTROL and synchronize of chaotic uncertain systems.

This article investigates the problem of simultaneous attitude and vibration CONTROL of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. ADAPTIVE integral sliding mode CONTROL is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant CONTROL led to discontinuous commands in the CONTROL signal, resulting in chattering. This issue was resolved by introducing an ADAPTIVE rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback CONTROL algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

This article investigates the challenging problem of simultaneous CONTROL of discrete and continuous inputs in SWITCHING systems. SWITCHING systems, which are a special type of hybrid systems, require advanced CONTROL methods that can simultaneously manage continuous and discrete inputs due to their hybrid nature. An innovative approach is introduced in this paper, combining ADAPTIVE Fuzzy Sliding Mode CONTROL (AFSMC) with Reinforcement Learning (RL). This method not only manages both types of inputs simultaneously but also operates ADAPTIVEly and robustly with minimal model information, performing learn and optimize online. To evaluate the performance and verify the proposed algorithm, the two-tank system is selected as a benchmark example in this field. The simulation results showed that the tank level tracking error is reduced to less than 1 cm despite the noise in the measurement with a standard deviation of 0.005 and also the sudden change of the system parameter. Additionally, the number of valve position changes decreased to 6 after 1000 episodes, indicating a significant reduction in SWITCHING frequency and an improvement in system stability. This algorithm achieves desired objectives with lower CONTROL costs compared to non-hybrid methods (management of discrete and continuous inputs). Furthermore, this approach can serve as a scalable framework for CONTROLling other complex systems with combined inputs across various engineering domains.

Design of an ADAPTIVE SUPERVISORY fuzzy sliding mode CONTROL for a planar cable-driven parallel robot is aided in this paper. The fuzzy logic CONTROLler is proposed to generate the SWITCHING CONTROL signal without occurring the chattering problem. For this purpose, an ADAPTIVE mechanism is suggested for online tuning of the output gain of the fuzzy sliding mode CONTROLler. Moreover, for better tracking, a SUPERVISORY CONTROL system is considered for online tuning of the PID sliding surface gains. The Grasshopper Optimization Algorithm is suggested for optimization of the membership functions selected for the fuzzy sliding surface. The stability proof of the closed-loop system is derived by using the Lyapunov stability theorem. Simulation results are reported to show the merits of the proposed CONTROLler on reduced chatter, and system robustness against parameter uncertainty, load disturbance, and nonlinearities.

Unfalsified ADAPTIVE CONTROL (UAC) is a recently proposed robust ADAPTIVE CONTROL strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-Model UAC is followed as an intermediate between UAC and multiple model CONTROL. Different approaches in UAC and MMUAC are studied. Also, Multi-Model Unfalsified Generalized Predictive CONTROL (MMUGPC) is proposed, which is a new CONTROL design strategy in the UAC framework. For an uncertain system, by utilizing several generalized predictive CONTROLlers and discrete SWITCHING between them with unfalsified CONTROL, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-Model Unfalsified Generalized Predictive CONTROL.