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This paper proposed a new method for maximum power point tracking in photovoltaic power generation systems by combining super-twisting sliding mode control and active disturbance rejection method. An incremental guidance method is used to find the point of maximum power. The non-linear extended state observer is applied to estimate the unmodeled dynamics and external disturbance. The ADRC based on a super-twisting sliding mode is designed to bring the state variables to the desired state. In the next step, the stability of NESO and ADRC are theoretically proved. Finally, the simulation results have been compared with the results of the PI controller, classical sliding mode control, and terminal sliding mode control (TSMC) presented in other articles. The results show the effectiveness and superiority of the proposed method. Also, to check the performance of the proposal method in real-time, real-time results have been compared with non-real-time results. The results obtained from the real-time and non-real-time simulations exhibited a minimal difference. This fact indicates the high accuracy of the modeling and simulations performed. Indeed, the mathematical models and non-real-time simulations have been able to accurately mimic the actual behavior of the photovoltaic system under various operating conditions.
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Lyophilization (aka freeze drying) has been shown to provide long-term stability for many crucial biotherapeutics, e.g., mRNA vaccines for COVID-19, allowing for higher storage temperature. The final stage of lyophilization, namely secondary drying, entails bound water removal via desorption, in which accurate prediction of bound water concentration is vital to ensuring the quality of the lyophilized product. This article proposes a novel technique for real-time estimation of the residual moisture during secondary drying in lyophilization. A state observer is employed, which combines temperature measurement and mechanistic understanding of heat transfer and desorption kinetics, without requiring any online concentration measurement. Results from both simulations and experimental data show that the observer can accurately estimate the concentration of bound water in real time for all possible concentration levels, operating conditions, and measurement noise. This framework can also be applied for monitoring and control of the residual moisture in other desorption-related processes.
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In this paper, a feedback controller based on the extended state observer is proposed for fully actuated systems. First, a generalized proportional-integral observer is designed to estimate states and disturbances simultaneously. Using the linear parameter varying approach and the convexity principle, a linear matrix inequality condition is given to obtain the observer gains. Second, on the basis of the full-actuation property and the estimated states, a feedback controller, utilizing estimated disturbances to compensate for system disturbances, is designed to make all the states of the closed-loop system uniformly ultimately bounded. In addition, if disturbances are constant or slow time-varying, the observation errors and the states of closed-loop system are all exponentially convergent. Two illustrations are provided to show the validity and practicality of the proposed approach. Simulation results show that the estimated disturbances can follow the true values with relatively small errors, so compensating the system disturbances with estimated values can effectively reduce the ultimate bounds of states of the closed-loop system.
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Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.
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In this paper, the problem of highly performance motion control of tank bidirectional stabilizer with dead zone nonlinearity and uncertain nonlinearity is addressed. First, the electromechanical coupling dynamics model of bidirectional stabilizer is developed finely. Second, the dead zone nonlinearity in bidirectional stabilizer is characterized as the combination of an uncertain time-varying gain and a bounded disturbance term. Meanwhile, an adaptive robust controller with dead zone compensation is proposed by organically combining adaptive technique and extended state observer (ESO) through backstepping method. The adaptive technique is employed to reduce the impact of unknown system parameter and dead zone parameter. Furthermore, the ESO is constructed to compensate the lumped uncertainties including unmodeled dynamics and dead zone residual, and integrated together via a feedforward cancellation technique. Moreover, the adaptive robust control law is derived to ensure final global stability. In stability analysis, the asymptotic tracking performance of the proposed controller can be guaranteed as the uncertainty nonlinearities in tank bidirectional stabilizer are constant. It is also guaranteed to achieve bounded tracking performance when time-varying uncertainties exist. Extensive co-simulation and experimental results verify the superiority of the proposed strategy.
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This paper studies a safe model predictive control (MPC)-based disturbance rejection control for a broad range of uncertain nonlinear systems subject to complex state safety constraints. The system under study is composed of a nominal model and an uncertain term that encapsulates modeling uncertainty, control mismatch, and external disturbances. In order to estimate the system state and total uncertainty, an extended state observer (ESO) is first designed. Utilizing the output of the ESO, the control compensates for the total uncertainty in real time and concurrently implements a control barrier function (CBF)-based MPC for the compensated system. The proposed control framework guarantees both safety and disturbance rejection. Compared to the baseline algorithm CBF-MPC, the proposed method significantly enhances system stability with a smaller root mean square (RMS) error of the system state from the equilibrium point. Rigorous theoretical analysis and simulation experiments are provided to validate the effectiveness of the proposed scheme.
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In this paper, angle attitude control is investigated for a networked pneumatic muscle actuators system (NPMAS) with input quantization and disturbance. A hysteretic quantizer is presented to effectively avoid the problem of high frequency oscillation in the process of quantization. A novel prescribed-time nonlinear extended state observer (PTNESO) is designed to continuously observe states and lumped disturbances of NPMAS, which ensures that the observation error converges in prescribed time. An active disturbance rejection control (ADRC) method based on PTNESO is designed to compensate for the lumped disturbances and achieve accurate angle tracking. A sufficient condition of bounded stability for NPMAS is given by the Lyapunov method. Finally, comparative experiments are provided to verify the effectiveness of the proposed control method.
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Disturbance observer (DOB) and extended state observer (ESO) are extensively utilized to handle external disturbances and model uncertainties in the control system. Nevertheless, the integration of these two methods to improve disturbance suppression remains an open question. In this research, the disturbance compensation mechanism of DOB is employed to compensate the disturbance estimation error of ESO, thereby achieving an effective integration of DOB and ESO. Additionally, a generalized ESO (GESO) is proposed to replace ESO. A robust internal mode control (RIMC) scheme is then developed by incorporating GESO into a two-degree-of-freedom internal mode control (TDF-IMC) framework. Moreover, the equivalence of RIMC and classical TDF-IMC is given by a rigorous derivation under the frequency domain description. Finally, the RIMC is applied to the control of a two-inertia system to verify its superiority in terms of robustness, disturbance rejection, and resonance suppression.
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In this paper, for macro-micro composite motion platform with piezoelectric hysteresis, an finite-time adaptive robust control method based on extended hysteresis observer is proposed. The dynamic model of macro-micro system is constructed at first. An extended hysteresis observer was designed to estimate the actual displacement and speed of motion system. Then, an adaptive robust control law is designed to eliminate the uncertain hysteresis model parameters. After this, exponential convergence result of the proposed control method is given and proved. By setting the expected bandwidth of macro-micro system, the gain adjustment process of the control method can be reduced in computation. The effectiveness of the proposed control method is demonstrated by comparison with other control methods in simulation, and the proposed control method has more stable tracking effect and smaller tracking error.
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Due to the disturbance of couplings, the anthropomorphic finger lacks sufficient stability and accuracy in joint motion control, which further affects the performance of complex grasping and operating for anthropomorphic hands. In order to obtain stable and accurate joint motion control effect, an anthropomorphic finger control strategy is proposed for an anthropomorphic finger driven by pneumatic artificial muscles (PAMs) in this paper. A nonlinear extended state observer (NESO) is presented to observe the disturbance of couplings for the anthropomorphic finger. An integral sliding mode controller (ISMC) is proposed to realize joint motion control and improve steady state performance. The convergences of the NESO and the ISMC are demonstrated by Lyapunov methods. Furthermore, experimental results illustrate the validity of the proposed control strategy.
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This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.
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This study proposes a controller design for the fixed-time stabilisation of a double integrator system (DIS), taking into account both output and state constraints. The controller combines Barrier Lyapunov Function (BLF)-based control method with homogeneous fixed-time controllers to provide faster convergence, eliminate the singularity of BLF-based fixed-time control algorithms, and guarantee precise convergence to the origin while maintaining the constraints. Three different cases including output constraint, output and state constraints, and fixed-time extended-state observer-based control for the disturbed DIS are studied. Several sets of simulations are carried out and comparisons are made with several finite-time and fixed-time controllers. In addition, the proposed control system is applied to the lateral control of an autonomous vehicle. Simulation results outline faster convergence, faster response, less sensitivity to sensor noise, more capability in disturbance rejection, and more accurate positioning compared to previous control systems.
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This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane.
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Active disturbance rejection control (ADRC) is widely used in airborne optoelectronic stabilization platforms due to its minimal reliance on the mathematical model of the controlled object. The extended state observer (ESO) is the core of ADRC, which treats internal parameter variations and external disturbances as total disturbances, observes the disturbances as extended states, and then compensates them into the control loop to eliminate their effects. However, the ESO can only achieve a precise estimation of constant or slowly varying disturbances. When the disturbance is periodically changing, satisfactory results cannot be obtained. In this paper, a generalized high-order extended state observer (GHOESO) is proposed to achieve the precise estimation of known frequency sinusoidal disturbance signals and improve disturbance suppression levels. Through numerical simulations, a traditional ESO and GHOESO are compared in terms of disturbance observation capability and disturbance suppression ability for single and compound disturbances based on our prior knowledge of disturbance frequency. The effectiveness of the proposed GHOESO method is verified. Finally, the algorithm is applied to an airborne optoelectronic stabilization platform for a 1°/1 Hz swing experiment on a space hexapod swing table. The experimental results demonstrate the superiority of the GHOESO proposed in this paper.
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This paper presents a pioneering cascade estimator, CRESO, which merges reduced-order and full-order extended state observers (ESO) in a novel manner. CRESO is designed to navigate the trade-off between robustness, estimation accuracy, and noise amplification inherent in active disturbance rejection control (ADRC) schemes. An analysis in the frequency domain substantiates CRESO's performance and robustness capabilities compared to those of single-level ESO and cascade ESO (CESO). These features are quantified using practical metrics, such as stability margins, sensitivity bandwidth, and estimation error at low frequencies. Additionally, the discussion encompasses the impact of selecting bandwidths for the cascade levels of CRESO on noise suppression. Experimental validation on a synchronous buck converter demonstrates the effectiveness of CRESO-based ADRC against control gain uncertainties, frequency-varying external disturbances, and sensor noise. The results highlight the advantages of the proposed approach over ADRC strategies employing singular ESO, two-level CESO, and two independent ESOs, as evidenced by several quality indices derived from the tracking errors and control signals.
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When performing complex tasks such as position transfer and material transportation, the distributed driving unmanned platform with variable configurations needs to address the challenge of multi-wheel cooperative torque distribution control to achieve high-performance differential steering and enhance vehicle dynamics. The configuration change will impact the dynamic performance of the unmanned platform, posing a challenge to the performance of the existing control strategy based on mathematical model development. In order to address the aforementioned issues, this paper analyzes the impact of changes in vehicle configuration on steering gain and proposes a hierarchical adaptive differential steering strategy based on variable vehicle configurations. Firstly, the response characteristics of the yaw angle relative to the active yaw moment under the influence of changes in wheelbase and tread are analyzed. Based on this analysis, two structural modes, maneuverable and balanced, are selected. Secondly, a localized-modelling sliding mode control method with an extended state observer is proposed to estimate the desired yaw moment in the upper controller, considering the motor's execution delay. Then, the lower controller optimizes the torque of each wheel in real-time using the whale optimization algorithm. It aims to optimize tire energy dissipation and tire load rate while ensuring driving stability and achieving differential steering. Finally, through co-simulation and experiments on a scaled prototype, the reliability of the dynamics theory and the superiority of the control algorithm are validated. This optimization has led to significant improvements in the tire dissipation energy index and tire load rate index.
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This study presents a novel hybrid control strategy for single-link flexible-joint robot manipulators, addressing inherent uncertainties and nonlinear dynamics. By integrating nonlinear reduced-order active disturbance rejection control (NRADRC) with backstepping control, the proposed method effectively estimates and mitigates nonlinear dynamics and external disturbances. Utilizing a nonlinear reduced-order extended state observer (NRESO) enhances resilience to internal and external uncertainties. The global stability of the proposed controller is rigorously established using the Lyapunov approach. Numerical comparisons with state-of-the-art nonlinear control methods demonstrate the superior efficiency and robustness of the proposed approach, especially under varying payloads and disturbances, advancing robotic control solutions.
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This paper proposes a robust tracking control method for wheeled mobile robot (WMR) against uncertainties, including wind disturbances and slipping. Through the application of the differential flatness methodology, the under-actuated WMR model is transformed into a linear canonical form, simplifying the design of a stabilizing feedback controller. To handle uncertainties from wheel slip and wind disturbances, the proposed feedback controller uses sliding mode control (SMC). However, increased uncertainties lead to chattering in the SMC approach due to higher control inputs. To mitigate this, a boundary layer around the switching surface is introduced, implementing a continuous control law to reduce chattering. Although increasing the boundary layer thickness reduces chattering, it may compromise the robustness achieved by SMC. To address this challenge, an active disturbance rejection control (ADRC) is integrated with boundary layer sliding mode control. ADRC estimates lumped uncertainties via an extended state observer and eliminates them within the feedback loop. This combined feedback control method aims to achieve practical control and robust tracking performance. Stability properties of the closed-loop system are established using the Lyapunov theory. Finally, simulations and experimental results are conducted to compare and evaluate the efficiency of the proposed robust tracking controller against other existing control methods.
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In this study, Active Disturbance Rejection Control (ADRC) has been designed for motion control of knee-joint based on exoskeleton medical robot. The extended state observer (ESO) is the main part of ADRC structure, which is responsible for estimating both actual states and system uncertainties. The proposed control scheme has adopted two versions of observers as disturbance estimators: linear extended state observer (LESO) and nonlinear extended state observer (NESO). The efficacy of proposed ADRC is strongly related to the performance of used ESO. As such, a comparison study has been conducted to evaluate the performance of two ADRCs in terms of disturbance-rejection capability and robustness to variation in system parameters under two version of ESO (LSO and NLESO). In order to enhance the dynamic performance of ADRC, Particle Swarm Optimization (PSO) algorithm has been used to optimally tune the design parameters of control scheme. To show the effectiveness of proposed LESO-based ADRC and NLESO-based ADRC, numerical simulation have been conducted. The proposed controllers have tested for an uncertain exoskeleton-knee system, where a 20% change in parameters was permitted over their nominal values. The results indicate that the ADRC algorithm based on LESO outperforms the one based on NESO in terms of disturbances rejection ability and robustness to parameters' variations.
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Switched mode DC-DC converters play an important role in today's renewable energy systems, electric Vehicles, consumer electrical appliances, and many more. While many researchers have designed controllers for DC-DC converters, achieving low implementation cost remains a significant hurdle. This paper proposes a cost-effective observer-based pole placement controller for a PWM voltage-controlled Buck converter with clear step by step design approach. The state space averaged model of the DC-DC power converter is used to design pole placement with integrator compensator and full state observer. Pole placement is employed to improve the transient and steady state behavior of Voltage reference tracking response and full state observer is applied to extract the inductor current with the aim of removing the actual current sensor. Design specifications are set to a settling time of less than 5 msec and an overshoot of less than 5 %. While designing Observer-Controller combination, the separation principle, designing the controller and observer independently, is utilized by placing the observer poles far to the left of the controller poles for the Observer to converge faster. The designed controller and observer is verified with simulation in MATLAB/SIMULINK software and design of developed controller and observer are realized using low-cost analog electronics circuit components The results shows that the proposed system's strength in tracking the reference output voltage even in the presence of input voltage disturbance and the mean value of inductor current is well extracted having only the output voltage and MOSFET gate signal.