Controller Design For Systems Research Articles

Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control

Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent’s step size relative to the algorithm’s iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively.

Dynamic Modelling and Feed-forward Control of an Overhead Crane

The article presents: modelling, simulation, and controller design of a laboratory overhead crane system. A nonlinear model was developed using Lagrange equation and linearization process is performed for the purpose of analysis and controller design. Accuracy of the linear model is assessed by comparing simulation results of nonlinear and linear dynamic equations. A feed-forward control based on a low-pass filter was designed based on the crane dynamics. The controller performance is discussed in terms of trolley position response and payload sway reduction. The proposed control approach is able to suppress the payload sway, but at the expense of a slight delay in the trolley motion

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

Networked explicit hybrid predictive control of an industrial benchmark process

Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance.

Event-triggered $$H_{\infty }$$ controller design for uncertain fractional-order systems with time-varying delays

Event-triggered $$H_{\infty }$$ controller design for uncertain fractional-order systems with time-varying delays

Robust-optimal control design for current-controlled electromagnetic levitation system with unmatched input uncertainty

Robust-optimal control design for current-controlled electromagnetic levitation system with unmatched input uncertainty

Adaptive region-reaching control for a non-holonomic mobile robot via system decomposition approach

This article focuses on the region-reaching control for a non-holonomic mobile robot system with two actuated wheels. The region-reaching control task consists of three basic control objectives: reaching an objective region, maintaining a rest state in the objective region, and stabilising at a desired posture. The constraints imposed on the motion of the mobile robot system are not integrable and have no inverse resolution, and thus increase the complexity of the controller design for non-holonomic systems. A system decomposition approach is proposed to convert the non-holonomic constraint control system to the cascade holonomic constraint control subsystems, and an adaptive control gain technique is presented to guarantee the stability of the non-holonomic system. Then, a novel torque controller is presented to implement the region-reaching control task. Specially, the objective region can be considered as a parking spot for a mobile robot.

The Influence of Current Magnitudes and Profiles on the Sedimentation of Magnetorheological Fluids: An Experimental Work

Magnetorheological fluids (MRFs) are widely used for various kinds of controllable devices since their properties can be controlled by an external magnetic field. Despite many benefits of MRFs, such as fast response time, the sedimentation arisen due to the density mismatch of the compositions between iron particles and carrier oil is still one of bottlenecks to be resolved. Many studies on the sedimentation problem of MR fluids have been carried out considering appropriate additives, nanoparticles, and several carrier oils with different densities. However, a study on the effect of current magnitudes and profiles on the sedimentation is considerably rare. Therefore, this study experimentally investigates sedimentation behaviors due to different current magnitudes and different magnitude profiles such as square and sine waves in different diameters. The evaluation was performed by visual observation to obtain the sedimentation rate. It was found that the average sedimentation rate of the square type of current is slower compared to the sinusoidal type. It has also been identified that the higher intensity of the applied current results in a stronger electromagnetic field, which could slow down the sedimentation. The results achieved in this work can be effectively used to reduce particle sedimentation in the controller design of various application systems utilizing MRFs in which the controller generates a different magnitude and different profile of the external magnetic field.

Adaptive robust control for fuzzy underactuated mechanical systems: A Stackelberg game-theoretic optimization approach

Adaptive robust control for fuzzy underactuated mechanical systems: A Stackelberg game-theoretic optimization approach

Adaptive Prescribed-Time SOSM Controller Design for Nonlinear Systems With Prescribed Performance

Adaptive Prescribed-Time SOSM Controller Design for Nonlinear Systems With Prescribed Performance

Anti-lock braking system control design using a non-linear multi-body dynamic model for a two wheeled vehicle

This paper investigates the design of an Antilock Braking System for a Two Wheeled Vehicle using a non-linear multi-body dynamic model developed by an approach widely used in robotics. The Antilock Braking System is controlled using Matlab/Simulink by applying the Bang-Bang, Proportional-Integral-Derivative and Fuzzy Logic controllers in order to set the longitudinal slip to a desired value. The developed model is used to study the braking performances of a Two Wheeled Vehicle in a straight-line maneuver under different road conditions: dry, wet and a transition between both. The simulation results indicate that the non-linear dynamic model can effectively simulate the behavior of the Two Wheeled Vehicle under emergency braking conditions and with the different types of controllers. The braking performance of the Two Wheeled Vehicle with Antilock Braking System was improved by decreasing the stopping distance, avoiding wheel lockup and therefore contributes to ensure the vehicle control and stability and to reduce the risk of falling. Also, it can be seen through comparative analysis that the Fuzzy logic is one of the most efficient controllers that improve the braking safety.

LQR controller design for affine LPV systems using reinforcement learning

In this paper, a data-driven sub-optimal state feedback is designed for a continuous time linear parameter varying (LPV) system using reinforcement learning. Time-varying parameters lie in a poly top and the system matrix has an affine representation for the parameters. Two novels, on-policy and off-policy algorithms, are proposed using available data from vertex systems of polytop to minimise a performance index and admit a common Lyapunov function (CLF). A convex optimisation problem is derived for each iteration based on Lyapunov inequality. Algorithms yield stabilising feedback gain in each iteration and convergence to a common lyapunov function. We demonstrate the efficacy of the proposed method by simulation of two case studies.

Nonlinear feedforward controller design of gasoline engine air-path system for reducing engine torque overshoot

In gasoline engines, the amount of fresh air in the cylinder becomes temporarily excessive owing to the slow response in exhaust gas recirculation (EGR) gas under a high EGR ratio. This phenomenon should be avoided, as it causes an engine torque overshoot because the amount of fuel also increases to follow the stoichiometric ratio, and can cause discomfort to the driver. In this study, we investigate reducing torque overshoot using a feedforward controller, which is a simple and effective method for improving the response of a control system. The feedforward controller is typically designed based on the inverse of the plant model. However, designing a feedforward controller for a high-order and nonlinear plant, such as an engine air-path system, can be challenging. Therefore, we exploit the fact that if a given nonlinear system satisfies the flatness property, a feedforward controller based on the inverse model can be easily obtained. The effectiveness of the proposed feedforward controller is evaluated through simulations.

Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances

In this paper, the adaptive practical finite-time tracking control problem for a class of strictly feedback nonlinear systems with multiple actuator constraints is investigated using backstepping techniques and practical finite-time stability theory. The effects of deadband and saturated nonlinear constraints on the controller design of nonlinear systems are addressed by the equivalent transformation method. The problem of complexity explosion due to the derivatives of virtual control signals is solved by using the virtual control signals as inputs to the command filters and using the outputs of the command filters to perform the corresponding control tasks. An adaptive neural network tracking backstepping control strategy based on the command filter technique and the backstepping design algorithm is proposed by approximating an unknown nonlinear function using a neural network. The control strategy ensures the boundedness of all variables in the closed-loop system, and the output tracking error fluctuates in a small region near the origin. Finally, simulations verify the effectiveness of the control strategy designed in this paper.

A linear matrix inequality approach to optimal reset control design for a class of nonlinear systems

AbstractIn this article, the problem of the optimal reset control design for Lipschitz nonlinear systems is addressed. The reset controller includes a base linear controller and a reset law that enforces resets to the controller states. The reset law design is strongly dependent on the appropriate design of the base controller. For this reason, in this article, the base controller and reset law are simultaneously designed. More precisely, an optimal dynamic output feedback is considered as the base controller which minimizes the upper bound of a quadratic performance index, and a reset law is used to improve the transient response of the closed‐loop system. This design is done in a full offline procedure. The problem is transformed into a set of linear matrix inequalities (LMIs), and the reset controller is obtained by solving an offline LMI optimization problem. Finally, two examples are presented to illustrate the effectiveness and validity of the proposed method.

Predictive Control Co-Design (pCCD): A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive MPC in CCD

Abstract Control co-design, or CCD, offers a promising solution for coordinating plant and control design of complex systems to better meet next generation demands. Most CCD algorithms optimize open-loop control signals that solve the problem with a single horizon, yet yield system designs lacking robustness to uncertainties. Recent work has integrated modern MPC policies into CCD. While this results in systems that are more robust, the recursive nature of receding-horizon MPC is computationally expensive and necessitates a bi-level (nested) optimization process to solve sequential MPC problems over smaller horizons.} In this work, I present a single-level predictive control co-design (pCCD) optimization framework that approximates the solution to optimizing a recursive MPC within CCD within a single optimization horizon without the need for nested optimization. The pCCD framework leverages elements of static gain matrices as decision variables to integrate a predictive controller into the algorithm that approximates the benefits afforded by embedding a MPC policy in CCD. The formulation reduces algorithm computational complexity by optimizing over the entire operating horizon at once while retaining key robustness and constraint-handling advantages of MPC. Through a comparative case study for a dual-tank thermal management system, this work shows the pCCD algorithm yields superior robustness to disturbance uncertainties compared to an analogous open-loop CCD system while converging on an optimal system/control design with a 92% reduction in run time compared to an analogous system optimized using a recursive MPC policy within the same CCD algorithm.

Improved Design of Concurrent Synchronization System Controller Based on Petri Net

At present, the advancement of science and technology has contributed to the emergence of various network parallel environments, web service concurrency environments and massively parallel processors. In order to abstract some practical systems into concurrent system models, it is necessary to conduct model analysis, and to perform deadlock detection of the concurrent system. Despite prior studies on the control problem of the synchronous concurrent system, there remains room for improvement in terms of workload, time, and efficiency. Some very practical methods are explored by introducing locking and unlocking to synchronize the concurrent system, and the algorithm that involves the specific regulation is provided. The Petri net model of the controller is developed to provide an effective method applicable to find the deadlock and to prevent and eliminate the deadlock for synchronous concurrent systems.

Enhanced disturbance observer-based hybrid cascade active disturbance rejection control design for high-precise tracking system in application to aerospace satellite

Enhanced disturbance observer-based hybrid cascade active disturbance rejection control design for high-precise tracking system in application to aerospace satellite

Adaptive predefined time neural filtered control design for an uncertain nonlinear system and application to flight control

Adaptive predefined time neural filtered control design for an uncertain nonlinear system and application to flight control

Fixed-Time Observer-Based Output Feedback Control for Nonlinear Systems With Full-State Constraints

This brief investigates the problem of fixed-time state observer-based output feedback control design for nonlinear systems with full-state constraints. First, a fixed-time state observer is constructed to estimate the unknown states, which can accurately estimate the system states within a fixed settling time. Then, by combining the integral barrier Lyapunov function with the backstepping method, the full-state constraints are realized after the fixed time. Meanwhile, the proposed control strategy can directly constrain the states rather than impose constraints on errors, which reduces the conservativeness of the constraint satisfaction condition. The proposed control method ensures that all signals in the closed-loop system are bounded and symmetric state constraints are strictly maintained after the fixed settling time. Finally, simulation results prove the effectiveness of the proposed method.