Search Results (22)

This study presents a loop-shaping methodology for the attitude control of a fixed-wing unmanned aerial vehicle (UAV). The proposed controller design focuses on achieving desired frequency–domain characteristics—such as specified phase and gain margins—to ensure stability and robustness. Unlike many existing approaches that rely on oversimplified plant models or involve mathematically intensive robust-control formulations, this work develops controllers directly from a high-fidelity six-degree-of-freedom UAV model that captures realistic aerodynamic and actuator dynamics. The loop-shaping procedure translates multi-objective requirements into a transparent, step-by-step workflow by progressively shaping the plant’s open-loop frequency response to match a target transfer function. This provides an intuitive, visual design process that reduces reliance on empirical PID tuning and makes the method accessible for both hobby-scale UAV applications and commercial platforms. The proposed loop-shaping procedure is demonstrated on the pitch inner rate loop of a fixed-wing UAV, with controllers discretized and validated in nonlinear simulations as well as real flight tests. Experimental results show that the method achieves the intended bandwidth and stability margins on the desired design target closely. Full article

To address the issues of external unknown disturbances and roll motion in the tracking control of underactuated unmanned surface vehicle (USV) formation, a cooperative formation control method based on nonlinear model predictive control (NMPC) algorithm and finite-time disturbance observer is proposed. Initially, a tracking error model for the USV formation is established within a leader–follower framework, utilizing a four-degree-of-freedom (4-DOF) dynamic model to simultaneously account for roll motion and trajectory tracking. This error model is then approximately linearized and discretized. To mitigate the initial non-smoothness in the desired trajectories of the follower USVs, a tracking differentiator is designed to smooth the heading angle of the leader USV. Thereafter, a quasi-infinite horizon NMPC algorithm is developed, in which a terminal penalty function is constructed based on quasi-infinite horizon theory. Furthermore, a finite-time disturbance observer is developed to facilitate real-time estimation and compensation for unknown marine disturbances. The proposed method’s effectiveness is validated both mathematically and in simulation. Mathematically, closed-loop stability is rigorously guaranteed via a Lyapunov-based proof of the quasi-infinite horizon NMPC design. In simulations, the algorithm demonstrates superior performance, reducing steady-state tracking errors by over 80% and shortening convergence times by up to 75% compared to a conventional PID controller. These results confirm the method’s robustness and high performance for complex USV formation tasks. Full article

This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 10³ arithmetic operations per cycle and less than 10² kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems. Full article

In the field of intralogistics, new systems are continuously being studied to increase flexibility and adaptability while striving to maintain high handling capabilities and performance. Among these new systems, this article focuses on a novel under-actuated intelligent surface capable of performing various handling tasks with a simplified design and without employing motors. The technology behind the device involves idle rotors, i.e., without motor-driven spinning, whose axis of rotation can be controlled in a few discrete positions. The system’s operation and digital model have already been tested and validated; however, a control system that makes the surface “smart” has not yet been developed. In this context, the following work analyzes control methodologies for the concept. Specifically, in a first phase, a threshold-based method is introduced and tested on a prototype of the surface for sorting and orientation operations. This basic technique involves actuating the surface modules according to pre-assigned rules once a chosen threshold condition is reached. In a second phase, instead, a decentralizd PID control is described and simulated based on real and potential industrial applications. Unlike the first method, in this case, it is the control law that defines the actuation and, through the dynamic description of the device, determines the best combination to achieve the goal. Additionally, the article illustrates how the difficulties introduced by the numerous nonlinearities, due to the under-actuation and the simplifications of the physical system, were overcome. For both control methods, promising results were obtained in terms of handling capability and errors in achieving the desired movement. Full article

Due to irregular hydrodynamic–aerodynamic coupling, the modeling and simulation of near-surface flight are extremely complex. For the present study, a practical dynamic model and a complete motion simulation method for the solution of such problems were established for engineering applications. A discrete non-linear time-varying dynamics model was employed in order to ensure the universality of the method; thereafter, force models—including gravity, aerodynamic, hydrodynamic, control, and thrust models—were established. It should be noted that a non-linear approach was adopted for the hydrodynamic model, which reflects the influences of waves in real-world situations; in addition, a Proportional–Integral–Derivative (PID) control law was added to realize closed-loop simulation of the motion. Considering a take-off flight as a study case, longitudinal three Degrees of Freedom (DoF) motion was simulated. The velocity, angle of attack, height, and angular velocity were selected as the state vectors in the state–space equations. The results show that, with the equilibrium state as the initial setting for the motion, reasonable time–history curves of the whole take-off phase can be obtained using the proposed approach. Furthermore, it is universally applicable for aircraft operating under hydrodynamic–aerodynamic coupling scenarios, including amphibious aircraft, seaplanes, Wing-in-Ground-Effect (WIGE) aircraft, and Hybrid Aerial–Underwater Vehicles (HAUVs). Full article

This paper proposes a high-performing, hybrid method for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The approach is based on an intelligent Nonlinear Discrete Proportional–Integral–Derivative (N-DPID) controller with the Perturb and Observe (P&O) method. The feedback gains derived are optimized by a metaheuristic algorithm called Particle Swarm Optimization (PSO). The proposed methods appear to present adequate solutions to overcome the drawbacks of existing methods despite various weather conditions considered in the analysis, providing a robust solution for dynamic environmental conditions. The results showed better performance and accuracy compared to those encountered in the literature. We also recall that this technique provides a systematic design procedure in the search for the MPPT in photovoltaic (PV) systems that has not yet been documented in the literature to the best of our knowledge. Full article

This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application. Full article

New biofuels are in demand and necessary to address the climate problems caused by the gases generated by fossil fuels. Biohydrogen, which is a clean biofuel with great potential in terms of energy capacity, is currently impacting our world. However, to produce biohydrogen, it is necessary to implement novel processes, such as Pressure Swing Adsorption (PSA), which raise the purity of biohydrogen to 99.99% and obtain a recovery above 50% using lower energy efficiency. This paper presents a PSA plant to produce biohydrogen and obtain a biofuel meeting international criteria. It focuses on implementing controllers on the PSA plant to maintain the desired purity stable and attenuate disturbances that affect the productivity, recovery, and energy efficiency generated by the biohydrogen-producing PSA plant. Several rigorous tests were carried out to observe the purity behavior in the face of changes in trajectories and combined perturbations by considering a discrete observer-based LQR controller compared with a discrete PID control system. The PSA process controller is designed from a simplified model, evaluating its performance on the real nonlinear plant considering perturbations using specialized software. The results are compared with a conventional PID controller, giving rise to a significant contribution related to a biohydrogen purity stable (above 0.99 in molar fraction) in the presence of disturbances and achieving a recovery of 55% to 60% using an energy efficiency of 0.99% to 7.25%. Full article

An experimental validation of fractional-order PID (FOPID) controllers, which were applied to a two coupled tanks system, is presented in this article. Two FOPID controllers, a continuous FOPID (cFOPID) and a discrete FOPID (dFOPID), were implemented in real-time. The gains tuning process was accomplished by applying genetic algorithms while considering the cost function with respect to the tracking error and control effort. The gains optimization process was performed directly to the two-tanks non-linear model. The real-time implementation used a National Instruments PCIe-6321 card as a data acquisition system; for the interface, we used a Simulink Matlab and Simulink Desktop Real-Time Toolbox. The performance of the fractional controllers was compared with the performance of classical PID controllers. Full article

DC–DC converters are used in many power electronics applications, such as switching power supply design, photovoltaic, power management systems, and electric and hybrid vehicles. Traditionally, DC–DC converters are linearly modeled using a typical operating point for their control design. Some recent works use nonlinear models for DC–DC converters, due to the inherent nonlinearity of the switching process. In this sense, a standout modeling technique is the Takagi–Sugeno fuzzy exact method due to its ability to represent nonlinear systems over the entire operating range. It is more faithful to system behavior modeling, and allows a nonlinear closed-loop control design. The use of nonlinear models allows the testing of controllers obtained by linear methods to operate outside their linearization point, corroborating with robust controllers for specific applications. This work aims to perform the exact fuzzy Takagi–Sugeno modeling of a buck–boost converter with non-ideal components, and to design a discrete proportional–integral–derivative (PID) controller from the pole cancellation technique, obtained linearly, to test the controller at different operating points. The PID control ensured a satisfactory result compared with the stationary value of the different operating points, but it did not reach the desired transient response. Since the proposed model closely represents the operation of the buck–boost converter by considering the components’ non-idealities, other control techniques that consider the system’s nonlinearities can be applied and optimized later. Full article

The control of a simulated moving bed (SMB) is always a challenging chemical control topic due to its complexity and nonlinearity. Its mathematical model must undergo an affine transformation and digitization before it can be controlled. Basically, there are three aspects that need to be considered in the nonlinear control of an SMB. First, the nonlinear characteristics are more complicated due to the switching time parameters of discrete events. Second, the control objective is not to minimize the control output error, but to make the separated concentrations between the components of the substance reach a certain ratio. Finally, the control variables are highly coupled. So far, the vast majority of the industry still uses relatively simple PLC controls; a few use specific controllers based on materials to be separated such as model predictive controls and PID controllers. Therefore, there is no unified intelligent processing mode. In this paper, a type-II fuzzy controller is presented and used as an SMB control. The interference of the related parameters was tested to observe the stability and robustness of the controller. The type-II fuzzy control was based on type-II fuzzy sets, which resulted in the type-II fuzzy controller having more flexible attribution function values. The results showed that the type-II fuzzy controller was not only more accurate in the control, but also better for robustness and adaptability than an ordinary fuzzy controller and PID controller. Full article

There exist several methods aimed at human–robot physical interaction (HRpI) to provide physical therapy in patients. The use of haptics has become an option to display forces along a given path so as to it guides the physiotherapist protocol. Critical in this regard is the motion control for haptic guidance to convey the specifications of the clinical protocol. Given the inherent patient variability, a conclusive demand of these HRpI methods is the need to modify online its response with neither rejecting nor neglecting interaction forces but to process them as patient interaction. In this paper, considering the nonlinear dynamics of the robot interacting bilaterally with a patient, we propose a novel adaptive control to guarantee stable haptic guidance by processing the causality of patient interaction forces, despite unknown robot dynamics and uncertainties. The controller implements radial basis neural network with daughter RASP1 wavelets activation function to identify the coupled interaction dynamics. For an efficient online implementation, an output infinite impulse response filter prunes negligible signals and nodes to deal with overparametrization. This contributes to adapt online the feedback gains of a globally stable discrete PID regulator to yield stiffness control, so the user is guided within a perceptual force field. Effectiveness of the proposed method is verified in real-time bimanual human-in-the-loop experiments. Full article

This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller. Full article

Most of the traditional PID tuning methods are heuristic in nature. The heuristic approach-based tuned PID controllers show only nominal performance. In addition, in the case of a digital redesign approach, mapping of the heuristically-designed continuous-time PID controllers into discrete-time PID controllers and in case of the direct digital design approach, mapping of the continuous-time plant (forward converter) into the discrete-time plant, results in frequency distortion (or warping). Besides this, nonlinear elements such as ADC and DAC, and delay in the digital control loop deteriorate the control performance. There is a need to tune conventionally-designed digital controllers to enhance performance. This paper proposes optimized discrete-time PID controllers for a forward DC–DC converter operating in continuous conduction mode (CCM). The considered conventional digital PID controllers designed on the basis of the digital redesign and direct digital approaches are tuned by one of the multivariable unconstrained pattern search methods named Hooke–Jeeves (H–J) search method to ensure excellent output voltage regulation performance against the changes in input voltage and load current. Numerical results show that the H–J-based optimized PID compensated forward converter system shows tremendous improvement in performance compared to its unoptimized counterpart and simulated annealing (SA)-based compensated system, thus justifying the applicability of the H–J method for enhancing the performance. Full article

The main purpose of this paper is to present a novel algorithmic reinforcement learning (RL) method for damping the voltage and frequency oscillations in a micro-grid (MG) with penetration of wind turbine generators (WTG). First, the continuous-time environment of the system is discretized to a definite number of states to form the Markov decision process (MDP). To solve the modeled discrete RL-based problem, Q-learning method, which is a model-free and simple iterative solution mechanism is used. Therefore, the presented control strategy is adaptive and it is suitable for the realistic power systems with high nonlinearities. The proposed adaptive RL controller has a supervisory nature that can improve the performance of any kind of controllers by adding an offset signal to the output control signal of them. Here, a part of Denmark distribution system is considered and the dynamic performance of the suggested control mechanism is evaluated and compared with fuzzy-proportional integral derivative (PID) and classical PID controllers. Simulations are carried out in two realistic and challenging scenarios considering system parameters changing. Results indicate that the proposed control strategy has an excellent dynamic response compared to fuzzy-PID and traditional PID controllers for damping the voltage and frequency oscillations. Full article