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Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, there is one main difficulty that plays a significant role in the manner of determining the gain for the control signal: choosing appropriate weighting matrices. The selection of these matrices that directly affect the controller performance is generally performed by trial and error, which is laborious and time-consuming. Accordingly, various optimization algorithms have been utilized to determine the weighting matrices of the LQR controller. In this paper, the weighting matrices of the designed LQR controller were obtained using Standard Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Grey Wolf Optimization algorithms. The obtained weighting matrices of the LQR controller were tested on an unmanned aerial vehicle simulation, and the performance of optimization algorithms were presented comparatively. [ABSTRACT FROM AUTHOR]

In recent years, the development and implementation of artificial intelligence (AI) have attracted tremendous attention. The implementation of active control systems for building structures can be improved by using an AI controller. Non-AI controllers such as the Linear Quadratic Regulator (LQR) controller require full state variables of the structure to be measured, which is rarely feasible. To address this problem, two AI models, namely, artificial neural network (ANN) and fuzzy logic (FL), have been tried as AI-based controller in various studies. In the present study, both AI models were investigated to see their practicality and effectiveness. The AI models were implemented to control an active mass damper (AMD) in a three-story prototype-sized building. The simulation results from the structure with an LQR controller were used as benchmark and training data for the AI models. The results of the study demonstrated that although both AI models could reduce the structure responses, ANN was more practical and effective compared to FL as an AI-based controller for the given structure. Furthermore, the effectiveness of an ANN-based AMD was also shown by the experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated architecture for navigation and attitude control of low-cost suborbital launch vehicles, propelled by a solid motor. Single-nozzle, two Degrees-of-Freedom (DoF) Thrust Vector Control (TVC) actuation is adopted. For architecture design purposes, a non-linear, unstable, 6 DoF model for the generic thrust-vector-controlled launcher dynamics and kinematics is deduced, and a linear state-space representation is proposed. The navigation system provides full-state estimates resorting to novel complementary kinematic filters, whose design allows to establish an explicit relation with steady-state Kalman filtering. A globally stable estimation solution is obtained, apart from the singularities arising from the use of Euler angles. The attitude control law is derived from the Linear Quadratic Regulator (LQR) using the state-space models for each linearization point of the reference trajectory, with an integral action (LQI) added to improve robustness and to provide null steady state attitude tracking error. A correction method is proposed to allow for pitch and yaw control in the presence of spinning motion, precluding the need of a supplementary roll control system. The control system is implemented through gain scheduling, resorting to an altitude-based linear parametric varying method. The architecture is implemented in a realistic simulation environment, composed by the 6 DoF non-linear model, the Navigation and Control solutions, and the environmental disturbances, to assess its performance through Monte Carlo simulations. The navigation system is able to provide accurate estimates of the state vector, while the control system satisfies attitude tracking performance and robustness to both external disturbances and model parametric uncertainties. • An integrated architecture for navigation and attitude control of suborbital launch vehicles is proposed. • An original time-varying state-space representation is deduced. • Novel complementary kinematic filters are designed to provide accurate full-state estimation. • LQI-based control allows for accurate pitch and yaw reference tracking via thrust vectoring. • The architecture performance is assessed through extensive Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The study of missile guidance systems is a well‐known nonlinear control engineering area of research. To enhance the control performance of a missle guidance system, several technologies have been proposed in existing works. To resolve the weighting matrix selection issue of a linear quadratic Gaussian (LQG) controller for the surface‐to‐air missile guidance control system, this study utilizes the particle swarm optimization (PSO) technique. Selecting the best state (Q) and input (R) weighting matrices is a significant difficulty in the design of the LQG controller for real‐time applications since it affects the controller's performance and optimality. The weighting matrices are often chosen by a trial‐and‐error method that not only complicates the design but also does not yield optimal outcomes. Therefore, in this paper, a PSO method is developed and used in the design of the linear quadratic regulator (LQR) and LQG controllers for the surface‐to‐air missile control system to choose the elements of the Q and R matrices in the best possible way. Finally, a comparative analysis between the designed controllers was presented. The results shows that a good performance was achieved by using the proposed PSO‐tuned design process. The LQG and LQR are designed by manually adjusting the weighting matrices and utilizing an intelligent procedure, PSO algorithm which achieved optimal results. Further results indicate that the designed controllers, the PSO tuned LQR and LQG achieved a better performance over the manually adjusted LQR and LQG controllers. [ABSTRACT FROM AUTHOR]

Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and cellular drivers that can vary significantly between individual patients and even within a single tumor. Though combination therapy is becoming more common in the treatment of cancer, it can be challenging to predict how various treatment modalities will interact and what negative effects they may have on a patient's health, such as increased gastrointestinal toxicities, or neurological problems. This paper aims to regulate immunity to tumor therapy by utilizing optimal control theory (OCT). This research suggests a malignant tumor model that can be regulated with a combination of immunological, vaccine, and chemotherapeutic therapy. The optimal control variables are employed to support the best possible treatment plan with the fewest potential side effects by reducing the production of new tumor cells and keeping the number of normal cells above the average carrying capacity. Also, the study addresses patient heterogeneity, individual variations in tumor biology, and immune responses for both young and old cancer patients. Finding the right doses for a treatment that works is the main goal. To do this, we conducted a comparative analysis of two optimum control approaches: the Single Network Adaptive Critic (SNAC) approach, which directly applies the notion of reinforcement learning to the essential conditions for optimality and the Linear Quadratic Regulator (LQR) methodology. Although the study's results show the promise of precision treatment plans, a number of significant obstacles must be overcome before these tactics can be successfully applied in clinical settings. It will be necessary to make considerable adjustments to the healthcare system's infrastructure in order to successfully offer personalized treatment regimens. This includes enhanced interdisciplinary care coordination methods, safe data management systems. [ABSTRACT FROM AUTHOR]

A ball-on-wheel system is a recently developed model in the field of automatic control. It serves as a simple model, meeting the learning and algorithmic research needs of students. With this model, there are various algorithms available for system control, such as PID controllers, fuzzy PID controllers, and sliding mode controllers, among others. In this paper, we construct a mechanical model for the system. We choose the Linear Quadratic Regulator (LQR) algorithm to design for this system. Simulation and experimental results demonstrate the effective operation of the LQR controller for the inverted pendulum on a cart system. Additionally, tuning experiments indicate that the parameters have been verified and confirmed to be consistent with the theoretical tuning principles of the LQR controller.

In this paper, we will survey two controllers, a linear control (LQR) and sliding mode control (SMC) on a central axis ball and beam (B&B) – a single input-multiple output (SIMO) system through simulation and experiment. In experiment, we present a hardware platform using STM32F407 for this model. Beside simulation results, the results in experiment prove the effectiveness of both methods in different cases. Thence, through this research, the advantages and disadvantages of the two controllers are demonstrated for designers to select in suitable cases.

This paper introduces a current control method for permanent magnet synchronous motors (PMSMs) using a disturbance sliding mode observer (DSMO) in conjunction with a linear quadratic regulator (LQR). This approach enhances control performance, streamlines the tuning of controller parameters, and offers robust optimal control that is resistant to system disturbances. The LQR controller based on state feedback is advantageous for its simplicity in parameter adjustment and achieving an optimal control effect easily under specific performance indicators. It is suitable for the optimal control of strong linear systems that can be accurately modeled. However, most practical systems are difficult to model accurately, and the time-varying system parameters and existing nonlinearity limit the engineering application of LQR. In the PMSM current control loop, there is strong nonlinear disturbance manifesting as the nonlinearity of its dynamic model. Additionally, substantial noise and variations in system parameters within actual motor circuits hinder the linear quadratic regulator from attaining optimal performance. A disturbance sliding mode observer is proposed to enhance the LQR controller, enabling superior performance in nonlinear current loop control. Simulation and actual hardware experiments were conducted to verify the performance and robustness of the control scheme proposed in this paper. Compared with the widely used PI controller in engineering and sliding mode control (SMC) specialising in disturbance rejection, it offers the advantage of straightforward parameter tuning and can swiftly achieve the robust and optimal control performance that engineers prioritize. [ABSTRACT FROM AUTHOR]

The engagement quality of a helicopter dry clutch has a significant impact on the service life and overall flight performance of the helicopter. The engagement oil pressure is an important factor affecting the clutch engagement quality. Firstly, a nonlinear input–output dynamic model for the dry clutch is developed to investigate the optimization and control of dry clutch engagement pressure in this paper. Secondly, to efficiently obtain the optimal pressure curve, an optimal method combining the developed dynamic model with the state feedback gain of a linear quadratic optimization regulator (LQR) solver is proposed. Thirdly, considering that hydraulic actuators may struggle with tracking certain pressure curves, a hydraulic actuator for accurately tracking pressure curves based on fuzzy PID is proposed. The simulation results indicate that the developed hydraulic actuator exhibits an excellent tracking performance. Moreover, compared with linear and segmented pressure curves, the optimal pressure curve derived from the proposed method significantly reduces jerk, friction work, and engagement duration, resulting in improved helicopter dry clutch engagement quality. [ABSTRACT FROM AUTHOR]

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios. [ABSTRACT FROM AUTHOR]

This study aims to control the oscillation and increase the pendulum energy with the movable arm of the rotary inverted pendulum, swinging the pendulum up from a stable equilibrium point to an unstable equilibrium point in a restricted zone. After the nonlinear model of the rotary inverted pendulum system and the linear models at the equilibrium points are given, the pendulum swing-up algorithm is introduced. The swing-up algorithm is based on increasing energy into the pendulum with the help of an arm at certain points while the pendulum is swinging. At this point, the arm acceleration is moved by a constant acceleration. The algorithm of the swinging-up is activated when the pendulum completes its period and moves to the point of maximum speed where the stable equilibrium point. These analyses are made by taking calculated limits in these operations into account. When the pendulum swing-up is achieved, a classical full-state feedback controller obtained from the linear model comes into play and keeps the pendulum at the unstable equilibrium point. Thus, a switching control structure is obtained. To verify the proposed control algorithm, the proposed algorithm is carried out in real-time on a rotary inverted pendulum. The comparative results obtained with the different arm acceleration values are presented. When the arm acceleration is increased from ±32 %&'/)* to ±64 %&'/)*, the pendulum swing up time decreases from approximately 9 sec to 5 sec. These results demonstrate the experimental success of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article proposes a novel data‐driven framework of distributed optimal consensus for discrete‐time linear multi‐agent systems under general digraphs. A fully distributed control protocol is proposed by using linear quadratic regulator approach, which is proved to be a sufficient and necessary condition for optimal control of multi‐agent systems through dynamic programming and minimum principle. Moreover, the control protocol can be constructed by using local information with the aid of the solution of the algebraic Riccati equation (ARE). Based on the Q‐learning method, a reinforcement learning framework is presented to find the solution of the ARE in a data‐driven way, in which we only need to collect information from an arbitrary follower to learn the feedback gain matrix. Thus, the multi‐agent system can achieve distributed optimal consensus when system dynamics and global information are completely unavailable. For output feedback cases, accurate state information estimation is established such that optimal consensus control is realized. Moreover, the data‐driven optimal consensus method designed in this article is applicable to general digraph that contains a directed spanning tree. Finally, numerical simulations verify the validity of the proposed optimal control protocols and data‐driven framework. [ABSTRACT FROM AUTHOR]

In this paper, the advantages of using adaptive controllers in active vehicle suspension systems to improve passenger comfort and safety are investigated. Based on Lyapunov analysis, the adaptation law of the controller is derived, where it uses the road profile and vehicle response to calculate the required control force. One of the advantages of the proposed controller is that it relies on a single tuning parameter, γ. In order to validate the proposed controller, MATLAB simulations are used to compare the performance of the proposed adaptively controlled system with that of both the uncontrolled and optimal controlled systems. The results show that the value of γ has significant effects on peak overshoot and the settling time of both vehicle displacement and acceleration. Furthermore, the results show a significant improvement between the adaptively controlled system and the uncontrolled system, with peak overshoot reductions of 44.6% and settling time reductions of 36%. The results also show that the adaptively controlled system proves its advantage by outperforming the optimal controlled system when dealing with large disturbances, but unfortunately, the adaptively controlled system typically requires a greater control force, twice as much as the optimal controlled system. Finally, the proposed adaptive controller exhibits remarkable performance and adaptability in responding effectively to changes in system parameters. This implies that, under many operational circumstances, it may be a trustworthy choice that ensures both the vehicle's safety and the comfort of its occupants. [ABSTRACT FROM AUTHOR]

For the problem of control of flexible linear double inverted pendulum (FLDIP), considering that the method of obtaining LQR parameters through traditional manual trial-and-error is too cumbersome, and the basic particle swarm optimization algorithm has a slower convergence speed and is prone to a local optimal solution, in this paper, a control method is proposed that it can achieve autonomous optimization based on improved the particle swarm optimization (PSO) algorithm. Firstly, the mathematical model of the FLDIPs is given to analyse the stability of the system. Then, the PSO algorithm is improved from the perspective of dynamically adjusting inertia weights to optimize linear quadratic regulator (LQR) parameters. Finally, through simulation comparison with the LQR control effect obtained by the manual trial and error method, the feasibility of the improved PSO algorithm is verified. This method can not only improve the control performance of the system but also effectively overcome the problem of blindness in selecting Q for traditional LQR optimal control.

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the stability and tracking accuracy of intelligent driving vehicle tracking controller, a lateral tracking control strategy based on LQR control algorithm and driver preview model is proposed, and combined with the longitudinal PID control algorithm to achieve lateral and longitudinal control. Firstly, a LQR controller with feed-forward is established, and the weight parameters of the LQR controller are optimized by gradient descent optimization algorithm. On this basis, the preview model of the driver is introduced, and an adaptive controller for preview distance based on experience is designed. Secondly, dual PID longitudinal controllers are established for velocity control. Finally, the test verification is carried out through Carsim and Matlab/Simulink joint simulation and real vehicle. The results show that the maximum lateral deviation of simulation is less than 0.035 m, the maximum heading deviation is less than 0. 09 rad, the overall trend of the planned trajectory can also be well followed under real vehicle test conditions, and the front wheel rotation angle and yaw rate change smoothly. The controller can ensure high-precision and smooth trajectory tracking, and it is more obvious at high velocity. [ABSTRACT FROM AUTHOR]

Quadrotor unmanned aerial vehicles (UAVs) are small, agile four-rotor systems suitable for various applications, from surveillance to disaster support missions. Hence, achieving high-precision trajectory tracking is crucial for their successful deployment. This research focuses on modelling, parameter identification, and Linear Quadratic Regulator (LQR) control design for quadrotors, aiming to enhance their trajectory following capabilities. The quadrotor dynamics are a sixth degree-of-freedom (6DOF) equation of motion derived from Newton's second law, encompassing moment of inertia, centre of gravity, weight, and thrust of propeller parameters. Experimental measurements are conducted to accurately determine these parameters, ensuring a realistic representation of the quadrotor system. Subsequently, a linearized model is constructed to provide a suitable plant for control system development. The LQR control design is intended to improve the trajectory tracking performance. This control strategy is validated through simulation and practical experiments, demonstrating its effectiveness in achieving high-precision trajectory following capabilites. The proposed approach demonstrates that LQR control effectively guides the quadrotor to resemble a predefined trajectory, experiencing only 3 % overshoot observed during the initial phase of flight. [ABSTRACT FROM AUTHOR]

Conventionally, the Three-Loop Autopilot is dominated by three main parameters namely the damping factor, time constant and the open-loop crossover frequency, to achieve the desired performance. In developing the autopilot gains, the same gains as the conventional approach can also be obtained using the general optimality theory. The basic idea of setting the design parameters and the feedback gains using methods of optimal control revolves around the proper selection of the performance index for which the control is optimized. In this paper, an explicit formula for autopilot gains is driven by taking into account the relations between the desired performance and the open-loop frequency, phase margin and time constant without the need to adjust the LQR weights. Since each set of design parameters selection doesn’t guarantee the existence of the optimal control law, the optimization criteria for the three-loop autopilot are derived to know the set of design parameters for which the control is optimized. Finally, an optimal criterion to select the design parameters and therefore the autopilot gains is developed, where the time constant is set to the designer objective while the open-loop crossover frequency and the phase margin as design constraints. [ABSTRACT FROM AUTHOR]

Summary: A novel method of an adaptive linear quadratic (LQ) regulation of uncertain continuous linear time‐invariant systems is proposed. Such an approach is based on the direct self‐tuning regulators design framework and the exponentially stable adaptive control technique developed earlier by the authors. Unlike the known solutions, a procedure is proposed to obtain a non‐overparametrized regression equation (RE) with respect to the unknown controller parameters from an initial RE of the LQ‐based reference tracking control system. On the basis of such result, an adaptive law is proposed, which under mild regressor finite excitation condition provides monotonous convergence of the LQ‐controller parameters to an adjustable set of their true values, which bound is defined only by the machine precision. Using the Lyapunov‐based analysis, it is proved that the mentioned law guarantees the exponential stability of the closed‐loop adaptive optimal control system. The simulation examples are provided to validate the theoretical contributions. [ABSTRACT FROM AUTHOR]

The underlying principles of inverted pendulums are widely applied to develop stabilization control strategies for under-actuated robotic systems in various applications. This article methodically designs an adaptive fractional-order linear quadratic regulator to optimize the position regulation and disturbance compensation ability of an inverted-pendulum-like robot. The proposed adaptive controller is realized by employing fractional-order differentiation operators in the baseline linear-quadratic-regulator. These fractional orders are adaptively modulated via an error-phase-based online adaptation law. The AFO modulation supplements the controller’s agility to efficiently steer the input trajectory as the state error’s phase changes, aiding the closed-loop system in robustly rejecting the external perturbations while maintaining a time-optimal behavior. The said propositions are verified by conducting customized experimental trials on the Quanser rotary pendulum platform. The proposed adaptive controller reduces the system’s transient recovery time by 35.8%, overshoots by 35.1%, control-energy expenditure by 37.2%, and offsets by 38.2% under transient disturbances, in comparison to the baseline linear quadratic regulator. The experimental data validates the superior time optimality and disturbance compensation of the proposed control law.

In this paper, an intelligent Model Reference Adaptive Control (MRAC) based on a neural network is proposed for robust tracking control of quadrotor UAV under external disturbances and parameter variations. First, the singularity-free dynamic model of the quadrotor is developed using Newton-Quaternion formalism. Then, conventional MRAC is designed to generate training data. With the generated data, the Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) are trained offline to get an initial set of network parameters for position controller parameters estimation and attitude control of the quadrotor, respectively, and an online learning algorithm is developed to update those network parameters in real-time. Finally, the performance of the designed Neural network-based MRAC has been evaluated using a numerical simulation in a nominal scenario and by introducing parametric variation and external disturbances as matched and unmatched uncertainties into the system. The simulation results show that the proposed controller has a better tracking performance and disturbance rejection capability compared with the Linear Quadratic Regulator (LQR) and conventional MRAC. Furthermore, the utilized control efforts are minimal and smooth proving functional safety and economical use of the controller. Therefore, the suggested controller is feasible for real-time implementation of the quadrotor UAV.

Bio-inspired robots are devices that mimic an animal’s motions and structures in nature. Worm robots are robots that are inspired by the movements of the worm in nature. This robot has different applications such as medicine and rescue plans. However, control of the worm robot is a challenging task due to the high-nonlinearity dynamic model and external noises that are applied to that robot. This research uses an optimal data-driven controller to control the worm robot. First, data are obtained from the nonlinear model of the worm robot. Then, the Koopman theory is used to generate a linear dynamic model of the Worm robot. The dynamic mode decomposition (DMD) method is used to generate the Koopman operator. Finally, a linear quadratic regulator (LQR) control method is applied for the control of the worm robot. The simulation results verify the performance of the proposed control method.

The article presents an analysis of the optimal control of speed and regenerative braking in an electric vehicle (EV). The presence of the continuous generating power is one of the major challenges for battery charging of electric vehicle. In order to meet the demand of continuous power, electric vehicles are driven by solar PV arrays. Initially, maximum power extraction (MPPT) is assessed from a solar photovoltaic (PV) array using the P&O method. The outputs of the solar PV array are integrated with electric vehicles. Such integration creates the problem of maintaining the power quality. The existing methods also address the issue of optimal speed and torque control with high transients and more settling time. In this article, an electric vehicle is modeled first and then operated in regenerative braking mode. To address the shortcomings of existing methods, the linear quadratic regulator (LQR) method is used; then, fuzzy logic controller (FLC)-based LQR is used for optimal speed and torque control during electric vehicle regenerative braking. In terms of reduced settling time and improved peak overshoot, optimal speed and regenerative torque of electric vehicles are better realized with LQR-based FLCs than with LQR controllers. In addition to this, stopping speed characteristics with various electrical parameters (voltage, current, and flux) have been analyzed with different methods in which LQR-based FLC shows its dominance to attain zero speed in regenerative mode with more sharp characteristics. [ABSTRACT FROM AUTHOR]

The world has become more like a small community thanks to the internet, which connects millions of people, businesses, and pieces of technology for a variety of uses. Because of the significant influence these networks have on our lives, maintaining their efficiency is important, which necessitates addressing issues like congestion. In this study, PI-controller gains are adjusted using a variety of optimization strategies to regulate the nonlinear TCP/AQM model. This controller commits controlled pressured signaling characteristics and modifies computer network congestion. First manual tune PI-Controller are used; then several optimization techniques were used to tune PIcontroller gains (Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO) and Simulated Annealing algorithm (SA)) and then Linear Quadratic Regulator theory are used. To test the reliability and effectiveness of each of the suggested controllers, several tests utilizing varied network parameter values, different queue sizes, and extra disturbances were conducted. MATLAB was used for all experiments., the results show the superiority of the LQR controller over PI controller with both manual and optimal tuning techniques. [ABSTRACT FROM AUTHOR]

Purpose: The full-face rock tunnel boring machines (TBM) generate intensive vibration while cutting rocks, which will lead to poor accuracy of the guidance system. Therefore, it is necessary to design an active isolation system to attenuate the vibration transmitted to the guidance system. Methods: This paper presents a single-degree-of-freedom active isolation system driven by a voice coil motor for the guidance system of TBM to work well in an intensive vibration environment. A hybrid control strategy based on feedforward control and feedback control is proposed according to the state equation of the active isolation system. Objective function based on the mounting plate acceleration is established for the linear quadratic regulator (LQR) controller. The weighting coefficients of the LQR controller are optimized by a genetic algorithm for better vibration control performance. Results: Simulations and experiments based on the proposed hybrid control strategy are carried out at excitation frequencies of 5, 8, 12, 15, 17, and 20 Hz, respectively. The results show that the designed electromagnetic active vibration isolation system has a displacement attenuation less than -17 dB and an acceleration attenuation less than -21 dB. Conclusions: The acceleration transmitted to the mounting plate under the action of the proposed active isolation system is less than 0.2 g, which meets the vibration requirements of the guidance system of TBM. [ABSTRACT FROM AUTHOR]

Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning (RL) based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online. [ABSTRACT FROM AUTHOR]

In this paper, linear quadratic regulator (LQR) control is applied to a Cuk converter, and mathematical modeling of the converter is done using state space averaging (SSA) in continuous conduction mode (CCM). The primary focus is to design a controller for the converter and maintain output voltage within 1% of the prescribed value when there is a unit step disturbance in the input. The design done by using MATLAB improved control of the converter from that with a conventional controller when tested with disturbances. The controller did not influence the system's stability, when assessed from the Bode plot. Despite the existence of disturbances, the findings prove the efficacy of the suggested approach, and the proposed controller can track the desired output voltage in less than 14 ms. The LQR control was developed for various applications of the Cuk converter, such as in a photovoltaic system and a wind system.

The engagement quality of a helicopter dry clutch has a significant impact on the service life and overall flight performance of the helicopter. The engagement oil pressure is an important factor affecting the clutch engagement quality. Firstly, a nonlinear input–output dynamic model for the dry clutch is developed to investigate the optimization and control of dry clutch engagement pressure in this paper. Secondly, to efficiently obtain the optimal pressure curve, an optimal method combining the developed dynamic model with the state feedback gain of a linear quadratic optimization regulator (LQR) solver is proposed. Thirdly, considering that hydraulic actuators may struggle with tracking certain pressure curves, a hydraulic actuator for accurately tracking pressure curves based on fuzzy PID is proposed. The simulation results indicate that the developed hydraulic actuator exhibits an excellent tracking performance. Moreover, compared with linear and segmented pressure curves, the optimal pressure curve derived from the proposed method significantly reduces jerk, friction work, and engagement duration, resulting in improved helicopter dry clutch engagement quality.

This paper introduces a current control method for permanent magnet synchronous motors (PMSMs) using a disturbance sliding mode observer (DSMO) in conjunction with a linear quadratic regulator (LQR). This approach enhances control performance, streamlines the tuning of controller parameters, and offers robust optimal control that is resistant to system disturbances. The LQR controller based on state feedback is advantageous for its simplicity in parameter adjustment and achieving an optimal control effect easily under specific performance indicators. It is suitable for the optimal control of strong linear systems that can be accurately modeled. However, most practical systems are difficult to model accurately, and the time-varying system parameters and existing nonlinearity limit the engineering application of LQR. In the PMSM current control loop, there is strong nonlinear disturbance manifesting as the nonlinearity of its dynamic model. Additionally, substantial noise and variations in system parameters within actual motor circuits hinder the linear quadratic regulator from attaining optimal performance. A disturbance sliding mode observer is proposed to enhance the LQR controller, enabling superior performance in nonlinear current loop control. Simulation and actual hardware experiments were conducted to verify the performance and robustness of the control scheme proposed in this paper. Compared with the widely used PI controller in engineering and sliding mode control (SMC) specialising in disturbance rejection, it offers the advantage of straightforward parameter tuning and can swiftly achieve the robust and optimal control performance that engineers prioritize.

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios.

In this article, the rotary double parallel inverted pendulum (RDPIP) is researched and presented. Additionally, some linear controllers, such as PID, PID, PID-LQR cascade, are proposed, developed to control RDPIP, and the impact of those controllers on the rotary double inverted pendulum was examined. The research and simulation results are implemented in the Matlab/Simulink toolbox to prove the ability of these types of controller in balancing this model.

In this paper, we use the calculus of variations to derive a sensitivity analysis for ordinary differential equations with events. This sweeping gradient method (SGM) requires a forward sweep to evaluate the original model and a backwards sweep of the adjoint to compute the sensitivity. The method is applied to canonical optimal control problems with numerical examples, including the sampled linear quadratic regulator and the optimal time-switching and state-switching for minimum-time transfer of the double integrator. We show that the application of the SGM for these examples matches the gradient determined analytically. Numerical examples are produced using gradient-based optimization algorithms. The emphasis of this work is on modeling considerations for the effective application of this method. [ABSTRACT FROM AUTHOR]

To deal with multiple constraints of vehicle active suspension system (ASS) including road handling and passenger safety, this paper presents an optimal linear quadratic regulator (LQR) approach which employs bat algorithm (BA) for selection of optimal state and input penalty matrices of LQR. We formulate the conflicting control objectives of ASS, namely, ride comfort and passenger safety as a multi-constraint optimization problem and employ the BA for weight selection of LQR. The key advantage of the proposed approach is that the local optima problem is avoided by utilizing the frequency tuning and random walk technique in BA. The performance of the proposed approach is experimentally tested using hardware in loop (HIL) testing on a quarter car ASS for realistic road profiles. Moreover, the performance is benchmarked against grey wolf optimization tuned LQR. Experimental results assessed based on ISO 2631 standards highlight the significant improvement in the ride comfort and passenger safety. [ABSTRACT FROM AUTHOR]