Control Law Parameters Research Articles

Adaptive backstepping control for electro-hydraulic servo system in extension sleeve press-fitting process of bearing pressing machine

In the process of extension sleeve press-fitting of the bearing pressing machine, electro-hydraulic servo system of valve-controlled symmetrical cylinder (ESSVSC) is an important and critical control module, and its performance has a significant impact on the working accuracy of such equipment. People have proposed various related control algorithms. However, external disturbances and unmodeled dynamic factors are often inevitable in practical work and may produce significant influences on the performance of the control system. Existing researches on this issue remains to be enriched and the performance of related algorithms still needs further improvement. An adaptive backstepping control algorithm (ABCA) is proposed in this paper. Firstly, a mathematical model of the ESSVSC is established which takes into account external disturbances and unmodeled dynamic factors. Secondly, the adaptive backstepping controller is designed by using the backstepping algorithm, and the control law and adaptive parameter estimation law are given. The stability of the control system is also proved. The analysis results of the numerical examples show that the proposed algorithm can effectively suppress the adverse effects of typical external disturbances and unmodeled dynamic factors and maintain good control accuracy. The output displacement error of the proposed algorithm is smaller and the tracking performance is better. The control accuracy of our algorithm is improved by 48.33% and 94.76% compared to BSMCA and PID, respectively, which illustrates the rationality of the established mathematical model and the effectiveness of the algorithm. This work is expected to provide useful reference for improving the control performance during the pressing process of the extension cylinder and the algorithm design of ESSVSC.

Internal model control based sliding mode controller design for unstable second order delayed processes with a case study on CSTR

Controlling the unstable processes is challenging since one or more poles are located on the right side of the s-plane. The existence of dead time in these systems makes control much more difficult. In this paper, internal model control based sliding mode control has been proposed for the control of unstable processes with dead time. Two sliding surfaces based on PID and PIDPI are used to design of the proposed controller. The parameters of continuous and discontinuous control law are obtained using differential evolution optimization technique. An objective function is constituted in terms of performance measure (integral absolute error) and control effort measure (total variation of controller output). Illustrative examples demonstrate the superiority of the proposed controller over earlier reported work in this realm, especially in terms of load disturbance rejection. A case study on temperature management of a continuous stirred tank reactor during an irreversible exothermic process also serves to highlight the applicability of the proposed system. Furthermore, robustness of the proposed controller is also investigated by inclusion of perturbations in the parameters. The obtained results clearly show how well the suggested controller works.

Adaptive Disturbance Rejection Motion Control of Direct-Drive Systems with Adjustable Damping Ratio Based on Zeta-Backstepping.

Direct-drive servo systems are extensively applied in biomimetic robotics and other bionic applications, but their performance is susceptible to uncertainties and disturbances. This paper proposes an adaptive disturbance rejection Zeta-backstepping control scheme with adjustable damping ratios to enhance system robustness and precision. An iron-core permanent magnet linear synchronous motor (PMLSM) was employed as the experimental platform for the development of a dynamic model that incorporates compensation for friction and cogging forces. To address model parameter uncertainties, an indirect parameter adaptation strategy based on a recursive least squares algorithm was introduced. It updates parameters based on the system state instead of output error, ensuring robust parameter convergence. An integral sliding mode observer (ISMO) was constructed to estimate and compensate for residual uncertainties, achieving finite-time state estimation. The proposed Zeta-backstepping controller enables adjustable damping ratios through parameterized control laws, offering flexibility in achieving desired dynamic performance. System stability and bounded tracking performance were validated via a second-order Lyapunov function analysis. Experimental results on a real PMLSM platform demonstrated that, while achieving adjustable damping ratio dynamic characteristics, there is a significant improvement in tracking accuracy and disturbance suppression. This underscores the scheme's potential for advancing precision control in biomimetic robotics and other direct-drive system applications.

Research on parameter optimization method of thrust vector/aerodynamic rudder composite control law based on singular value method

Advanced aircraft with thrust vectoring/aerodynamic rudder layout have significant control coupling problems. The traditional single-loop control parameter design method cannot take into account the performance of multiple control loops in this scenario. Therefore, a control law parameter optimization method based on the singular value of the hysteresis matrix is proposed. First, the control parameter optimization interval is determined using the time domain control performance index. Then, the singular value method is used to measure the stability margin of the multiple-input multiple-output (MIMO) system on this basis, and the corresponding optimal objective function is established to optimize the controller parameters. The feasibility of this method is verified by numerical simulation. The results show that the designed control parameter optimization algorithm has better time domain control performance and larger system stability margin than the traditional single-loop control parameter design method.

An Identification-Based Control Method for an Overhead Crane with a New Combined Sensor Placement

This paper is devoted to an automatic control method for an overhead crane under the current parametric uncertainty of the crane, transported cargo, and exogenous disturbances. The control objective is to move the cargo in the horizontal plane to a point ensuring the final delivery of the cargo to the designated place while parrying the angular oscillations of the suspension and reaching given dynamic characteristics. The approach is based on a control scheme with a current parametric identification algorithm, an implicit reference model, and “simplified” adaptability conditions to track cargo movements directly. The control law generates a given trolley speed for the servo drive. The passport data of the crane installation are used to select the control law parameters. Unlike previous publications on the topic, the solution proposed below is simpler, more reliable in terms of operation, and less expensive. This is achieved by placing a combined sensor (an accelerometer with an angular rate sensor (ARS)) on a suspension cable near the crane trolley and applying, first, an algorithmic solution without the preliminary calculation of the ARS drift and, second, a current parametric identification procedure of higher efficiency. Computer simulation results are provided to confirm these advantages of the new solution. A similar example is implemented on an experimental installation.

Research on Control Strategy of Oscillating Continuous-Wave Pulse Generator Based on ILADRC

To achieve fast and precise position servo control in a continuous-wave pulse generator and address issues such as internal and external disturbances and significant overshoot, this paper proposes an improved linear active disturbance rejection control strategy. First, a mathematical model of the permanent magnet synchronous motor is established, and a second-order linear active disturbance rejection controller is designed based on this model. To address the issue of large errors in disturbance estimation by the traditional extended state observer, a cascaded extended state observer is introduced. By designing an additional state observer to estimate the system’s residual disturbances, the impact of disturbances on system performance is further reduced. Through an in-depth analysis of the motion characteristics of the continuous-wave pulse generator, the trade-off between system overshoot and response speed is revealed. To address this, a new adaptive law is proposed. This law, based on the system’s periodic wave response and tracking error, adjusts the parameters of the linear state error feedback control law in real time, reducing system overshoot while improving response speed. To validate the effectiveness of the proposed control strategy, a simulation model of the position servo control system for the continuous-wave pulse generator was developed. The comparative analysis of the simulation results for the different control strategies shows that the improved linear active disturbance rejection control strategy significantly enhances the system’s dynamic response performance.

Optimal Synthesis of a Satellite Attitude Control System under Constraints on Control Torques and Velocities of Reaction Wheels

The problem of optimal synthesis of a nonlinear system’s control law parameters of satellite attitude control under constraints is considered. The maximum stability degree of a system is considered as an optimality criterion. The reaction wheels’ control moments and angular velocities achievable within the drives’ physical characteristics are considered as constraints. A linear model of the system converted from the original nonlinear model was used to solve the problem. The decomposition method, based on the transition from real time to relative time, is used to separate the tasks of optimality criterion maximization and constraints consideration. A method of synthesizing the optimal form of system transient process according to the maximum stability degree criterion is developed. An analytical method for determining the optimal values of control law parameters is proposed, based on the structural features of the linear differential equations system. A method that takes into account constraints on control moments and angular velocities of reaction wheels, based on transition scale from relative time to real time and its calculation algorithm, ensuring that conditions of all constraints are met, is developed. An example of solving the problem of optimal system synthesis is considered. The results demonstrate the effectiveness and simplicity of the proposed methods and algorithm for targeted selection of a system’s technical characteristics, taking into account constraints on reaction wheels’ maximum angular velocities and control moments values.

A Bioinspired Control Strategy Ensures Maneuverability and Adaptability for Dynamic Environments in an Underactuated Robotic Fish

Bioinspired underwater robots can move efficiently, with agility, even in complex aquatic areas, reducing marine ecosystem disturbance during exploration and inspection. These robots can improve animal farming conditions and preserve wildlife. This study proposes a muscle-like control for an underactuated robot in carangiform swimming mode. The artifact exploits a single DC motor with a non-blocking transmission system to convert the motor’s oscillatory motion into the fishtail’s oscillation. The transmission system combines a magnetic coupling and a wire-driven mechanism. The control strategy was inspired by central pattern generators (CPGs) to control the torque exerted on the fishtail. It integrates proprioceptive sensory feedback to investigate the adaptability to different contexts. A parametrized control law relates the reference target to the fishtail’s angular position. Several tests were carried out to validate the control strategy. The proprioceptive feedback revealed that the controller can adapt to different environments and tail structure changes. The control law parameters variation accesses the robotic fish’s multi-modal swimming. Our solution can vary the swimming speed of 0.08 body lengths per second (BL/s), and change the steering direction and performance by an angular speed and turning curvature radius of 0.08 rad/s and 0.25 m, respectively. Performance can be improved with design changes, while still maintaining the developed control strategy. This approach ensures the robot’s maneuverability despite its underactuated structure. Energy consumption was evaluated under the robotic platform’s control and design. Our bioinspired control system offers an effective, reliable, and sustainable solution for exploring and monitoring aquatic environments, while minimizing human risks and preserving the ecosystem. Additionally, it creates new and innovative opportunities for interacting with marine species. Our findings demonstrate the potential of bioinspired technologies to advance the field of marine science and conservation.

Model-Scale Evaluation of Autonomous Ship Landing Guidance and Control Modes

This paper presents the results of an extensive model-scale experimental evaluation of autonomous ship landing guidance and control modes, with flight tests performed in the Maneuvering and Seakeeping (MASK) Basin at the U.S. Naval Surface Warfare Center Carderock Division. The experiments were performed using a commodities-based multirotor unmanned aerial vehicle (UAV) operating from a 20-ft-long model scale ship subject to scaled wave conditions. During testing, two separate guidance algorithms were evaluated: a quadratic programming (QP) based landing algorithm that plans the trajectory to a forecasted deck state and a simpler “baseline??? method that tracks deck motions while closing the distance between the aircraft and deck at a constant rate. Both algorithms commanded a Froude-scaled explicit model following control law, and the control law parameters were modified to progressively degrade aircraft tracking bandwidths. The results showed that the predictive capabilities of the QP algorithm allowed more direct landing paths to be planned when compared to the baseline algorithm and also allowed the QP algorithm to land with lower tracking bandwidths. But while the QP algorithm performed well in the majority of cases, there were several landings where a combination of poor deck state predictions and how the QP algorithm utilizes predictions to choose a land time resulted in significant terminal velocity and attitude errors. The baseline guidance algorithm, on the other hand, proved to be both simple and reliable when the UAV was in high bandwidth configurations but required a high reference tracking bandwidth.

Event‐triggered control of switched linear systems with state‐dependent switching law and unstabilizable modes

AbstractThis paper studies the event‐triggered control of switched linear system with state‐dependent switching law. A dynamic term is introduced to the switching law to acclimatize the dynamic mode‐dependent event‐triggered controller. Besides, neither the switching law nor the controller needs continuous detection of the system states. Because of the event‐triggered and sampling behavior of the controller, asynchronous between the controller and the system modes are encountered. A new multiple Lyapunov functional is proposed to appropriately characterize the synchronous and asynchronous working modes of the system, and a sufficient exponential stability condition is obtained, which is applicable for the cases when some modes or all modes of the switched system are unstabilizable. Moreover, the event‐triggered control and switching law parameters are jointly designed using linear matrix inequalities. Numerical and comparison studies are offered to provide the validity and superiority of the result.

Drag-based analytical optimal de-orbiting guidance from low earth orbit via Deep Neural Networks

Controlled de-orbiting is crucial for a low earth orbiting (LEO) satellite or capsule intending to land in a desired location and to prevent damage to people and property on the ground caused by debris. Drag modulation is one possible mechanism to control de-orbiting, exploiting atmospheric drag variation to reduce the necessary orbital energy from the initial conditions to the re-entry interface. In this context, this paper proposes a novel targeted de-orbiting Artificial Neural Network (ANN) and drag-based guidance algorithm for a LEO artificial satellite. It relies on the combination of empirical and analytical relations with Deep Neural Networks (DNNs) to estimate the main parameters of the optimal control law in real time. The training set is composed of several optimal control solutions generated with a previously developed optimal control algorithm, exploiting the formulation in equinoctial modified orbital parameters to manage the large time scale of the problem and the computational cost. An innovative procedure for the training set generation led to the achievement of general results with a limited number of samples. The successful outcome of the guidance algorithm on over 1000 cases demonstrates its robustness and generalization of results. To conclude, a Linear Quadratic Regulator (LQR) feedback control is applied to one case to deal with a more realistic and uncertain density model.

Nonlinear predictor‐feedback cooperative adaptive cruise control of vehicles with nonlinear dynamics and input delay

SummaryWe construct a nonlinear predictor‐feedback cooperative adaptive cruise control (CACC) design for homogeneous vehicular platoons subject to actuators delays, which achieves: (i) positivity of vehicles' speed and spacing states, (ii) string stability of the platoon, (iii) stability of each individual vehicular system, and (iv) regulation to the desired reference speed (dictated by the leading vehicle) and spacing. The design relies on a nominal, nonlinear adaptive cruise control (ACC) law that we construct, which guarantees (i)–(iv) in the absence of actuator delay, and nonlinear predictor feedback. We consider a classical (for ACC/CACC design) third‐order, nonlinear model subject to input delay, for the vehicles' dynamics. The proofs of the theoretical guarantees (i)–(iv) rely on derivation of explicit estimates on solutions (both during open‐loop and closed‐loop operation), capitalizing on the ability of predictor feedback to guarantee complete delay compensation after the dead‐time interval has elapsed, and derivation of explicit conditions on initial conditions and parameters of the nominal control law. We also present consistent simulation results, considering a platoon of ten vehicles, which validate the design developed.

Integrated design of lateral-directional control law and vertical tail load for civil aircraft

According to new requirements of airworthiness regulation CS25.353, the advanced lateral-directional P-Beta control law design is studied in this paper. The LQR method based on the tracking command model is used to design the lateral-directional control law parameters. At the same time, the vertical tail load model of aircraft for yaw maneuver is introduced into the lateral-directional control law design model. The aim is to build the integrated design of control law and flight load. Finally, the simulation results show that the P-Beta control law can not only improve Dutch roll damping and coordination turn characteristics, but it can also reduce the yaw maneuver load by 18.5%. Thus, the structure weight of the aircraft’s vertical tail can be reduced.

Sliding mode controller design for unstable processes with dead time with a case study on continuous stirred tank reactor

Unstable processes are difficult to control because one or more poles lie on the right-hand side of the s-plane. Control becomes further complicated by the presence of dead time in such systems. In this study, a sliding mode control (SMC) design is proposed for the control of unstable processes with dead time. To apply the SMC, a second order plus dead time (SOPDT) model of the unstable process is used, and a proportional-integral-derivative-acceleration type sliding surface is considered. The parameters of continuous and discontinuous control laws are obtained using the differential evolution optimization technique. The optimal control problem is solved by satisfying a new weighted bi-objective function constituting the performance index integral absolute error and control input total variation. The proposed control scheme has been satisfactorily extended to control unstable integrating and higher-order unstable processes with dead time by approximating them into the unstable SOPDT model. The efficacy of the suggested scheme has been evaluated on several benchmark unstable industrial chemical processes, including the continuous stirred tank reactor (CSTR). Further, this scheme has been compared with recently reported work, and the obtained results clearly demonstrate the effectiveness of the suggested controller.

Partial differential equation‐based multiagent systems formation‐containment control with α‐leader input delay

AbstractThis paper investigates the formation‐containment problem for a multiagent system, where the agents are classified into containment followers, formation leaders, anchor leader, and ‐leader. The ‐leader stabilizes formation leaders to desired formation deployment, while formation leaders adopt local state feedback distributed control law without desired formation position and the anchor leader fixes to its desired position. The containment followers adopted consensus distributed control law and converge to the convex hull spanned by the leader agents. Formation leaders' distributed control law parameters are proposed based on the discrete form of the desired partial differential equation (PDE) formation deployment, which is stabilized by the ‐leader agent and transited by communication topology. Based on its neighbor leader agent's delay state, ‐leader control law designed an integral‐type delay‐compensated control law by the backstepping method and the equivalence principle. In order to prove the rightness and existence of delay‐compensated control law, the well‐posedness of kernel functions are given by semigroup perturbation theory and solutions in the sense of distribution. Finally, a simulation example is given to verify the theoretical results.

Parameter Adjustment of the Autothrust Control Law by Flight Tests

Aiming at deriving precise automatic control of airspeed, a comprehensive flight test solution for autothrust control law parameter adjustment was proposed, providing a worthy reference for other aircraft types’ work, including the flight test points and methods, aircraft modification requirements for the test, the system performance analysis and evaluation methods, and the flight test risk analysis. A method invoking test scripts by a flight test interface unit and a control panel to configure parameters of autothrust control law and input excitation signals was proposed for the first time. The test scripts and the test devices cross-link can be fully verified in advance in both iron bird and real aircraft ground tests, which can greatly reduce unexpected risks of the test method based on the flight test engineers’ personal experience. According to the evaluation from three aspects of system response, robustness and throttle lever movement based on the test data, the optimal autothrust control law parameters configuration was given.

Integration of sEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System

This paper presents an adaptive Fuzzy Sliding Mode Control approach for an Assist-as-Needed (AAN) strategy to achieve effective human–exoskeleton synergy. The proposed strategy employs an adaptive instance-based learning algorithm to estimate muscle effort, based on surface Electromyography (sEMG) signals. To determine and control the inverse dynamics of a highly nonlinear 4-degrees-of-freedom exoskeleton designed for upper-limb therapeutic exercises, a modified Recursive Newton-Euler Algorithm (RNEA) with Sliding Mode Control (SMC) was used. The exoskeleton position error and raw sEMG signal from the bicep’s brachii muscle were used as inputs for a fuzzy inference system to produce an output to adjust the sliding mode control law parameters. The proposed robust control law was simulated using MATLAB-Simulink, and the results showed that it could instantly adjust the necessary support, based on the combined motion of the human–exoskeleton system’s muscle engagement, while keeping the state trajectory errors and input torque bounded within ±5×10−2 rads and ±5 N.m, respectively.

Data-driven cascade control system: Response estimation and controller design

In this study, we propose a data-driven design method for a cascade control system with inner and outer control loops. First, the input–output response of a controlled plant, which varies with the controller parameters of a fixed-structure inner-outer control law, is estimated directly from open-loop input–output data. Next, based on the estimated response, the controller parameters are optimized to minimize the difference between a reference model with a controlled closed-loop system. In the proposed method, the response of a fictitious reference input, which varies with the controller parameters, is estimated, and then the closed-loop response is estimated. Therefore, a closed-loop input–output data is not required, and the controller parameters are determined directly from an open-loop input–output data. Furthermore, the time constant of a reference model is also optimized so as to reduce the control error. The proposed method is compared with both conventional single-loop and cascade data-driven methods by means of numerical examples.

Dynamics Analysis of a Nonlinear Satellite Attitude Control System Using an Exact Linear Model

This study aims to analyze the nonlinear dynamics of a satellite attitude control system equipped with reaction wheels and a PD controller. Based on the angular momentum conservation theorem for a closed mechanical system, the nonlinear equations of the attitude control system dynamics are presented as a linear system of differential equations with time-varying parameters. The asymptotic properties of the angular momentum of a mechanical system including a satellite and reaction wheels as rigid bodies are investigated. A relation has been established between the dynamic parameters of the attitude control system and the initial value of the angular momentum of the satellite. The issue of asymptotic stability for differential equations with time-varying parameters is simplified to the asymptotic stability problem for the ultimate homogeneous system of linear differential equations with constant elements. The dependencies of the dynamic parameters of the attitude control system on the constant parameters of this ultimate system of linear differential equations, as well as the initial values of the satellite’s angular momentum, enable us to apply proven and effective engineering methods. These methods are used not only for analyzing the stability of the control system but also for synthesizing the parameters of the control law based on the quality requirements of transient processes such as the stability margin, responsiveness, oscillation, transient time, and overshoot. In this case, the calculation of the control law parameters will be grounded in exact equations, not on approximate equations of the control system dynamics obtained by linearization.

Design of Photovoltaic Power Generation Servo System Based on Discrete Adaptive Network Dynamic Surface Control Technology

In recent years, under the development of the dual carbon goal, the energy crisis has become increasingly serious, and China has also experienced serious power rationing. However, the research on dynamic surface control technology in solar tracking systems in nonlinear control systems is mostly based on continuous-time systems, while adaptive dynamic surface control based on discrete-time nonlinear control systems can describe an actual control system more accurately in the production process. It can effectively suppress interference with extremely high stability and safety. To solve the problem of low efficiency in photovoltaic power generation, this research first built a photovoltaic power generation servo system model based on the parameter of uncertainty. Then, a discrete adaptive neural network dynamic surface (DANNDS) controller was designed to solve the problems in the design of the traditional backstepping method. Finally, based on the designed method of a dynamic surface controller, a discrete adaptive neural network quantization controller (DANNQC) for the photovoltaic power generation servo system was designed by introducing external disturbance. The control parameters and their studied ranges were as follows: The reference signals were or1=sin(0.1t) and or2=cos(0.1t). The parameters of the virtual control law and the final control law were m11=0.01, m22=0.01, m12=0.02, m13=0.02, and m23=0.02. The time constant of the low-pass filter was ζ12=ζ13=ζ22=ζ23=0.005. The parameters of the parameter regulation law were ρ12=ρ13=ρ22=ρ23=0.0005 and a12=a13=20, a22=a23=22. The research results show that the MTE, RMSTE, and 2NTE scores of the height angle servo motor of the DANNDS control method were 0.0026, 7.0279 × 10−4, and 0.3552, respectively. The scores for each index of the azimuth servo motor were 0.0028, 8.9237 × 10−4, and 0.4511, respectively. The height angle tracking error for the DANNQC control method was [−0.02,0.022]. The azimuth tracking error was [−0.03,0.03]. In summary, the photovoltaic power generation servo system based on the DANNQC has a better control performance. By controlling the height angle and azimuth angles, it can better track the position of the sun and adjust the position of the photovoltaic panel in real time. The sun’s rays illuminate the photovoltaic panel at an appropriate angle to achieve maximum power generation efficiency, which is of great practical significance for the development of solar technology.