A multi-objective design approach for tuning LQR controllers is proposed.A new reinforced quantum-behaved PSO algorithm is introduced.Comparative studies among nine various techniques are conducted. A novel and generic multi-objective design paradigm is proposed which utilizes quantum-behaved PSO (QPSO) for deciding the optimal configuration of the LQR controller for a given problem considering a set of competing objectives. There are three main contributions introduced in this paper as follows. (1) The standard QPSO algorithm is reinforced with an informed initialization scheme based on the simulated annealing algorithm and Gaussian neighborhood selection mechanism. (2) It is also augmented with a local search strategy which integrates the advantages of memetic algorithm into conventional QPSO. (3) An aggregated dynamic weighting criterion is introduced that dynamically combines the soft and hard constraints with control objectives to provide the designer with a set of Pareto optimal solutions and lets her to decide the target solution based on practical preferences. The proposed method is compared against a gradient-based method, seven meta-heuristics, and the trial-and-error method on two control benchmarks using sensitivity analysis and full factorial parameter selection and the results are validated using one-tailed T-test. The experimental results suggest that the proposed method outperforms opponent methods in terms of controller effort, measures associated with transient response and criteria related to steady-state.
In @cite_15 a method is proposed to determine the weighting matrices using PSO with pole region constraint for controlling a flexible-link manipulator. In @cite_38 it is suggested that PSO-based LQR produces better result compared to trial-and-error approach for the active suspension system. In @cite_64 authors applied ordinary LQR, PSO-based LQR, AWPSO-based LQR and ICA-based LQR for optimal load frequency control and concluded that AWPSO-based LQR outperforms other approaches in terms of settling-time and maximum overshoot. A PSO-based optimal real-time LQR controller for stabilizing an inverted pendulum system is proposed in @cite_2 . In @cite_61 wavelet-PSO is proposed for tuning LQR controllers and is applied to an optimal structural control. In @cite_57 it is concluded that contrary to trial-and-error approach, a PSO-based state feedback controller does not have sub-optimal performance in case of partial state feedback. @cite_21 proposed a quantum-behaved PSO algorithm for tuning a given LQR controller. They exploited a conventional weighted aggregation of control objectives which can only provide the designer with one solution. More studies on PSO-based LQR design can be found in @cite_9 @cite_7 @cite_59 @cite_47 .
@cite_61 @cite_38 @cite_47 @cite_64 @cite_7 @cite_9 @cite_21 @cite_57 @cite_59 @cite_2 @cite_15
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With the development of construction techniques, it is possible to build large-span bridges, pipelines, dams, and other essential structures in seismically active regions or in active faults. A new method to find the optimal control forces for active tuned mass damper is presented in this paper. Three algorithms: discrete wavelet transform (DWT), particle swarm optimization (PSO), and linear quadratic regulator (LQR). DWT are used to obtain the local energy distribution of the motivation over the frequency bands. PSO is used to determine the gain matrices through the online update of the weighting matrices used in the LQR controller while eliminating the trial and error. The method is tested on a 10-story structure subject to several historical pulse-like near-fault ground motions. The results presented in this paper indicate that the proposed method is more effective at reducing the displacement response of the structure in real time than conventional LQR controllers. In this paper, the state feedback control design problem for tracking control of a flexible link manipulator is considered. The calculation of state feedback control gains is conventionally handled by pole placement method or LQR method via Riccati equation. Unfortunately, they still possess trial and error approach of choosing some parameters. Particularly, choosing elements of Q and R matrices in the state feedback control using LQR method has to be done by trial. Therefore, an intelligent method to resolve this problem is proposed by adopting PSO algorithm. The experimental work is carried out to evaluate the effectiveness of the proposed method. In this paper, state feedback control design for tracking control of a flexible link manipulator is considered. The computation of state feedback control gains is conventionally handled by pole placement method or LQR method via Riccati equation. Unfortunately, they still possess trial-and-error approach in choosing their parameters. Particularly, choosing elements of Q and R matrices in the state feedback control design using LQR method has to be done by trial. Therefore, an intelligent method to resolve this problem is proposed by adopting PSO-based optimization. Experimental work is carried out to evaluate effectiveness of the proposed method. This paper presents the design of an active suspension control of a two-axle railway vehicle using an optimized linear quadratic regulator. The control objective is to minimize the lateral displacement and yaw angle of the wheelsets when the vehicle travels on straight and curved tracks with lateral irregularities. In choosing the optimum weighting matrices for the LQR, the Particle Swarm Optimization (PSO) method has been applied and the results of the controller performance with weighting matrices chosen using this method is compared with the commonly used, trial and error method. The performance of the passive and active suspension has also been compared. The results show that the active suspension system performs better than the passive suspension system. For the active suspension, the LQR employing the PSO method in choosing the weighting matrices provides a better control performance and a more systematic approach compared to the trial and error method. Aiming at the difficulty of designing weighting matrices for linear quadratic regulator (LQR), a multi-objective evolution algorithm (MOEA) based approach is proposed. The LQR weighting matrices, state feedback control rate and optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time, smaller amplitude value deviating from steady-state than the pole assignment LQR weighting matrices design approach. So the validity of the proposed approach is confirmed. This paper discusses design and performance issues of three design techniques for DSTATCOM controller with an aim to improve the robustness. The state feedback controller of DSTATCOM has been designed using Linear Quadratic Regulator (LQR), Linear Quadratic Regulator with prescribed degree of stability and Particle Swarm Optimization (PSO). Weak ac source and unbalanced nonlinear load are considered. The performance of the controller designed through the three methodologies has been compared under different operating conditions. Particle Swarm Optimization based controller is more robust and is suitable for a DSTATCOM working under uncertain parametric conditions like feeder impedance and load that are most common in a distribution system. This controller ensures stability under all possible operating conditions and has superior performance over the other two controller design techniques. The performance of the DSTATCOM controller designed through the three different methodologies is verified by detailed simulation studies. Abstract This paper presents an optimized reconfigurable control design methodology by separating control commands distribution task from flight controller for different types of fault handling. The proposed strategy improves the flight control performance in normal and fault situations. The particle swarm optimization (PSO) based multi-input multi-output (MIMO) linear quadratic regulator (LQR) is used to produce virtual command signals. A modified weighted pseudo-inverse (WPI) based cascaded re- allocation technique is employed for effective implementation of commands to redundant control surfaces in a realistic nonlinear aircraft benchmark model. Control surface fault modelling is performed for the evaluation of optimized reconfiguration based modular flight control strategy. Simulation results show that acceptable fault tolerant control (FTC) performance can be achieved by using swarm intelligence based optimization technique for modular control design. The main focus of this paper is to develop an optimization method for the automatic fighter tracking (AFT) problem. The AFT problem is similar to a general evader-pursuer maneuvering automation problem between the dynamic systems of two highly interactive objects. This paper proposes a particle swarm optimizer-based variable feedback gain controller (PSO-based VFGC) for dealing with AFT problems. The PSO-based VFGC is designed to obtain the control value of a pursuer through an error-feedback gain controller. Once conditions of system closed-loop stability have been satisfied, the optimal feedback gains can be obtained through PSO, and the actual control values can be derived from the obtained values. Simulation results confirm the capabilities of the proposed method by comparing the results against two other methods in the field: the weight matrix value defined Ricatti equation, and the linear matrix inequality (LMI) based linear quadratic regulator (LQR). The performance of the proposed method is superior to that of its alternatives. In this paper the method of Linear Quadratic Regulator (LQR) is employed for controlling accelerator PWM power supply, and improving its dynamic functioning. A method based on the Particle Swarm Optimization is proposed to optimize the weighting matrix Q and R. In order to control the dynamic behavior of the power supply, the fitness function of PSO is acquired considering the needs of the power properties. The results of simulation proved that the proposed method was much better than simple LQR method and LQR-GA method, and the performance of the power supply was improved dramatically. In this paper, considering some important indices such as closed-loop pole locations, speed of response and combining them into an objective function an optimization problem is defined in order to select the weighting matrices in Linear Quadratic Regulator (LQR) controller. To solve this optimization problem the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized and compared. The proposed method is applied to rotational inverted pendulum. Simulation results show the relative superiority of PSO over GA. This paper introduces an application of Multi-Objective Evolution Algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR). Considering the difficulty of designing weighting matrices for a linear quadratic regulator, a multi-objective evolutionary algorithm based approach is proposed. The LQR weighting matrices, state feedback control rate and consequently the optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time and smaller amplitude value deviating from steady-state than a Non-dominated Sorting Genetic Algorithm LQR ( NSGA- LQR )weighting matrices design approach. This paper established a mathematical model of the inverted pendulum system and put forward a design method of LQR based on PSO. A LQR is designed by this method according to the computer simulation experiments. The simulation results show that the inverted pendulum system with the LQR has good performance. Then the real-time control of the pendulum has been accomplished successfully based on the real-time control model built in Simulink. The LQR designed in the computer simulation experiments is applied to real-time controls. The real-time control experiments also validated the correctness of the theoretical analysis and the rightness of the computer simulation. One of the main observed problems in the control of automatic generation control systems is the limitation to access and measurement of state variables in the real world. In order to solve this problem, an optimal output feedback method, the linear-quadratic regulator controller, is used. In the output feedback method, only measurable state variables within each control area are required to use for feedback. But in order to improve dynamic performance and provide a better design for this controller, the concept of an intelligent regulator is added to the linear-quadratic regulators; as a result, the particle swarm optimization based linear-quadratic output feedback regulator and the imperialist competitive algorithm based linear-quadratic output feedback are proposed to calculate the global optimal gain matrix of con- troller intelligently. The optimal control law of this controller must be determined by minimizing a performance index under the output feedback conditions leading to coupled matrix equations. In conventionalmethods, the control law is handled by pole placement, iterative, or trial-and-error methods for choosing controller gains; thus, intelligent optimization techniques are applied to solve this problem. The proposed controllers are tested on a two-area automatic generation control power system, and a complete comparison between the proposed output feedback controllers with adaptive weighted particle swarm optimization, particle swarm optimization, the imperialist competitive algorithm, and a conventionaloutput feedbackcontroller is presented.The results show that the proposed intelligent controller improved the dynamic response of the system faster than the conventional controller and provided a control system that satisfied the load frequency control requirements. Abstract Visual tracking algorithms based on structured output support vector machine (SOSVM) have demonstrated excellent performance. However, sampling methods and optimization strategies of SOSVM undesirably increase the computational overloads, which hinder real-time application of these algorithms. Moreover, due to the lack of high-dimensional features and dense training samples, SOSVM-based algorithms are unstable to deal with various challenging scenarios, such as occlusions and scale variations. Recently, visual tracking algorithms based on discriminative correlation filters (DCF), especially the combination of DCF and features from deep convolutional neural networks (CNN), have been successfully applied to visual tracking, and attains surprisingly good performance on recent benchmarks. The success is mainly attributed to two aspects: the circular correlation properties of DCF and the powerful representation capabilities of CNN features. Nevertheless, compared with SOSVM, DCF-based algorithms are restricted to simple ridge regression which has a weaker discriminative ability. In this paper, a novel circular and structural operator tracker (CSOT) is proposed for high performance visual tracking, it not only possesses the powerful discriminative capability of SOSVM but also efficiently inherits the superior computational efficiency of DCF. Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters. Furthermore, an implicit interpolation is applied to convert the multi-resolution feature maps to the continuous domain and make all primal confidence score maps have the same spatial resolution. Then, we exploit an efficient ensemble post-processor based on relative entropy, which can coalesce primal confidence score maps and create an optimal confidence score map for more accurate localization. The target is localized on the peak of the optimal confidence score map. Besides, we introduce a collaborative optimization strategy to update circular and structural operators by iteratively training structural correlation filters, which significantly reduces computational complexity and improves robustness. Experimental results demonstrate that our approach achieves state-of-the-art performance in mean AUC scores of 71.5 and 69.4 on the OTB2013 and OTB2015 benchmarks respectively, and obtains a third-best expected average overlap (EAO) score of 29.8 on the VOT2017 benchmark. The application of LQR controller in inverted pendulum system is common, but the choice of weight matrix Q and R of LQR controller has always been a difficult. This paper presents a new method to achieve the double inverted pendulum system LQR controller parameters optimization through the particle swarm optimization algorithm. The simulation result shows that the method used in the choice of matrix Q and R achieves very good results. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. In this paper, the problem of feedback and active user selection in multiple-input single-output (MISO) wireless systems such that the system’s stability region is as big as possible is examined. The focus is on a system in a Rayleigh fading environment where zero forcing precoding is used to serve all active users in every slot. Acquisition of the channel states is done via uplink training in time division duplexing mode by the active users. Clearly, only a subset of users can perform uplink training and the selection of this subset is a challenging and interesting problem especially in MISO systems. The stability regions of a baseline centralized scheme and two novel decentralized policies are examined analytically. In the decentralized schemes, the transmitter broadcasts periodically the queue state information and the users contend for the channel in a carrier sense multiple access-based manner with parameters based on the outdated queue state information and real-time channel state information. We show that, using infrequent signaling between the base station and the users, the decentralized policies outperform the centralized policy. In addition, a threshold-based user selection and training scheme for discrete-time contention is proposed. The results of this paper imply that, as far as stability is concerned, the users must be involved in the active user selection and feedback training decision. This should be leveraged in future communication systems. The main contribution of this paper is in the development of vision-based estimation and control system for robotic fruit harvesting and rigorous stability analysis to guarantee performance of the closed-loop system. The presented cooperative visual servo controller benefits from the large field-of-view of a fixed camera and the accuracy of a camera-in-hand (CiH). Computationally inexpensive perspective transformation-based range estimation method obtains 3D fruit position using a monocular camera to enable real-time manipulator control. A rotation controller is developed to orient the robot such that the target fruit selected by the fixed camera can be viewed by the CiH attached to the end-effector. Subsequently, the end-effector can be servoed to the target fruit location using the presented pursuit guidance based hybrid translation controller. Lyapunov-based stability analysis guarantees global exponential regulation of the end-effector. Numerical simulations verify the feasibility of the developed controller while the performance is evaluated on a seven degrees-of-freedom kinematically redundant manipulator using an artificial citrus tree. The position of the fruit was randomly selected, and the closed-loop visual servo control experiment was performed 21 times to analyze the repeatability and accuracy of the developed controller. With 95 confidence level the expected position of the robot end-effector is observed to lie within the confidence ellipsoid. The accuracy of the controller was observed to be about 15mm, thus making the system suitable for harvesting medium and large varieties of citrus fruit but may limit operation for small varieties such as page and blood oranges. This work presents a method to build a robust controller for a hose transportation system performed by aerial robots. We provide the system dynamic model, equations and desired equilibrium criteria. Control is obtained through PID controllers tuned by particle swarm optimization (PSO). The control strategy is illustrated for three quadrotors carrying two sections of a hose, but the model can be easily expanded to a bigger number of quadrotors system, due to the approach modularity. Experiments demonstrate the PSO tuning method convergence, which is fast. More than one solution is possible, and control is very robust. We study a distributed antenna system where L antenna terminals (ATs) are connected to a central processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). This model has been widely investigated both for the uplink (UTs to CP) and for the downlink (CP to UTs), which are instances of the general multiple-access relay and broadcast relay networks. We contribute to the subject in the following ways: 1) For the uplink, we consider the recently proposed “compute and forward” (CoF) approach and examine the corresponding system optimization at finite SNR. 2) For the downlink, we propose a novel precoding scheme nicknamed “reverse compute and forward” (RCoF). 3) In both cases, we present low-complexity versions of CoF and RCoF based on standard scalar quantization at the receivers, that lead to discrete-input discrete-output symmetric memoryless channel models for which near-optimal performance can be achieved by standard single-user linear coding. 4) We provide extensive numerical results and finite SNR comparison with other “state of the art” information theoretic techniques, in scenarios including fading and shadowing. The proposed uplink and downlink system optimization focuses specifically on the ATs and UTs selection problem. In both cases, for a given set of transmitters, the goal consists of selecting a subset of the receivers such that the corresponding system matrix has full rank and the sum rate is maximized. We present low-complexity ATs and UTs selection schemes and demonstrate through Monte Carlo simulation that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the noninteger penalty affecting CoF RCoF at high SNR. Comparison with other state-of-the art information theoretic schemes, show competitive performance of the proposed approaches with significantly lower complexity.