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In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The action-critic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems. [ABSTRACT FROM AUTHOR]

In this paper, an optimal trajectory tracking control problem for general nonlinear systems is investigated. An adaptive critic control method with the digital twin (DT) theory is developed. Divergent from the existing tracking control methods, the advantages of adaptive dynamic programming (ADP) and the theory of DT are combined in this paper, and the novel multilayer artificial system structure is constructed. The action-critic structure is employed by each artificial system to obtain an approximate optimal control policy. The model network (MN) is built by using the actual input and output data sets of the controlled system, which means the dependence on the dynamics of the system is overcame. Then, the weights of the trained action network (AN) and MN are passed to the real system to realize the optimal tracking control. The feasibility of the algorithm is proved by theoretical analysis. Finally, the algorithm is applied to a simple nonlinear torsional pendulum system and an industrial wastewater treatment system (WWTS), and the effectiveness of the algorithm is verified. The algorithm effectively realizes the tracking control of nonlinear systems. [ABSTRACT FROM AUTHOR]

Traditional Adaptive Cruise Control (ACC) systems often struggle to dynamically adapt to rapidly changing traffic conditions, resulting in suboptimal performance. Additionally, with fuel consumption emerging as a critical consideration alongside safety, there is a pressing need for more advanced solutions. This paper presents a novel approach to address these challenges by integrating Fuzzy ACC with Model Predictive Control, denoted as FACMPC. This integration aims to enhance both the longitudinal safety of AVs and fuel efficiency by considering real-time traffic conditions. The FACMPC system utilizes fuzzy logic inside the MPC, adaptively generates controller's weighting factors, allowing the system to adapt instantly to varying traffic environments and driving circumstances. The findings show that this adaptation improves the balance between driving safety, efficiency, and comfort. Additionally, three interruption scenarios, Alpha, Beta and Gama, are examined. In Alpha, the study evaluates the sensitivity of the FACMPC to disturbances by applying band-limited white noise to the lead vehicle velocity. In Beta, the AV experiences a loss of the lead vehicle velocity signal for a defined period, prompting safety considerations and assumptions. The Gama scenario includes a sensitivity analysis to account for variations and uncertainties in parameters by considering a range of ±5% around the nominal values for four key parameters: road slope, wind speed, wind direction, and rolling resistance. The findings indicate that the proposed controller's mean fuel consumption is 8.110, only a 3.21% increase over the nominal, compared to a 7.03% increase for the conventional ACC, demonstrating greater robustness against uncertainties. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven Fault-Tolerant Control (FTC) system based on Model-Free Adaptive Control (MFAC) for a class of nonlinear discrete-time systems. First, the original system is converted in to a Compact Form Dynamic Linearization (CFDL) data model using the Pseudo-Partial-Derivative (PPD) technique. Second, a Rough Neural Network (RNN) is employed as an observer for Fault Detection (FD) by generating residual. Moreover, the obtained residual is incorporated into the CFDL and the optimality criterion to reconstruct the FTC strategy. The key contributions of this research include: 1) Considering and compensating the sensor and actuator faults simultaneously, which improves the overall system's robustness.; 2) the use of RNN as a powerful predictor for noisy and uncertain industrial data for FD, thereby enhancing the accuracy of FD; 3) the direct embedding of the generated residual from RNN into the controller without the need to estimate the fault function, simplifying the control process and 4) the research relies on input and output data from the controlled system for FD and FTC processes, which reduces the computational burden and increases efficiency. Finally, the simulation results show the effectiveness of the proposed data-driven FTC approach in comparision with the existing data-driven FTC approaches in fault control of some sample systems including high-speed train and CSTR system. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of fault tolerant control (FTC) for reentry vehicles (RV) subject to actuator fault and perturbation. In contrast to the majority of papers assuming bounded and exogenous actuator fault effect, the adaptive dynamic programming (ADP) control algorithm has been considered here to tackle with the dependence of closed-loop states induced by the setup of an active FTC strategy. By this way, the FTC problem formulation takes theoretically into account the fact that an actuator fault can destabilize a closed-loop. First, the FTC problem in attitude tracking is transformed into an error-optimal control problem. Next, a cost function based on zero-sum game theory is considered to achieve tracking under the joint influence of state-dependent actuator faults and perturbations. Lyapunov theory has been next used to prove the stability of FTC architecture and the network weight convergence. Benefits of proposed ADP-FTC algorithm is finally highlighted on a numerical benchmark of RV. [ABSTRACT FROM AUTHOR]

In recent years, a class of soft (deformable) robots has attracted the attention of researchers due to their continuum features being advantageous over rigid robots for dexterous and safe compliant motions. These distinct advantages are obtained from the elastomer hyperelastic material properties because continuum robots are manufactured and actuated through embedded pneumatic energy fields. In this way, the continuum deformation raises novel problems in modeling and control due to its highly nonlinear behavior. That is, the deformable shape of its components varies continuously, unlike rigid robots, suggesting that the controller should compensate for its varying continuum model. Motivated by the deformable soft robot subject to parametric uncertainties and unmodeled dynamics, this paper proposes an adaptive, instead of constant, tuning of feedback gains of a model-free controller. The tuning mechanism is based on a knowledge-based scheme using a discrete wavelet neural network (DWN) by an efficient input–output identification updating its parameters and weights online, with a gradient descent algorithm driven by the identification error. At the same time, the control feedback gains are self-tuned, minimizing a convex cost function of tracking errors. Dynamic simulations show the performance of the proposed approach considering the parameters of a real soft robot mimicking a gelatin-like behavior. Finally, some discussions on the proposed scheme's computational cost, feasibility, and viability are briefly addressed. [ABSTRACT FROM AUTHOR]

An Automatic Voltage Regulator (AVR) system utilized to keep the terminal voltage of a synchronous generator at the desired level has received much attention among researchers. Designing an efficient and robust control scheme for the AVR system to maintain a specified voltage level is an important research area. From the control area perspective, reinforcement learning, an adaptive optimal control method, has received increasing attention in reference tracking problems. This article discusses a reinforcement learning approach to an AVR system and its experimental validation. A deep deterministic policy gradient (DDPG) agent working in continuous-time is designed offline to improve dynamic system characteristics of the AVR system besides its robustness against load disturbance, parameter uncertainties, and reference change. In the DDPG agent design process, the limits of the produced control signal are taken into account to perform a feasible simulation similar to a real-time application. The performance of the proposed learning-based controller is analyzed in three categories: transient and steady-state responses, stability analysis, and robustness analysis against parameter uncertainties, reference change, and load disturbance. A comparison with recently published papers employing Fuzzy-PID, PID-F, PI λ DND 2 N 2 , PIDD 2 ,and PID controllers in which various heuristic optimization algorithms were employed to optimally tune the controller parameters is made. Furthermore, to demonstrate that the behavior of the learning-based approach provides a stable and satisfactory performance, it is analyzed for a real synchronous generator connected to a 230 kV network using Matlab/Simulink environment. The results presented in this paper indicate that the proposed learning-based controller ensures the stability of the AVR system, significantly improves the regulating performance, and most impressively, is robust against parameter uncertainties, reference change, and load disturbance. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural trajectory tracking controller with a bounded-input property is developed for cable-driven parallel robots (CDPRs). Due to the fact that the cables in these robotic systems should always remain in tension in a trajectory tracking task, a more precise tracking controller is needed for CDPRs comparing to the conventional rigid-link robotic systems. To achieve this objective, this paper proposes a new nonlinear controller with a learning ability for the robot dynamics. The controller includes an adaptive multi-layer neural network to compensate for the modeling uncertainties of the system, and utilizes an auxiliary dynamics to provide a priori bounded tension command for the cables. In addition to this novelty, a bounded-input controller is designed for the dynamics of the actuators, coupled with gearboxes, in order to follow the tensions, defined through the controller of robot dynamics. The boundedness feature of the controller facilitates considering the upper limit of the actuators in choosing the control gains. Stability of the whole system is well studied, and the uniformly ultimately bounded stability is guaranteed. The effectiveness of the proposed control scheme is validated through simulations on a 4-cable planar robot in both nominal and perturbed conditions. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive stable backstepping controller(BSC) based on support vector regression (SVR) has been introduced for nonlinear dynamical systems. Stable BSC is designed over Lyapunov stability of the closed-loop system. The nonlinear system dynamics required to constitute the BSC architecture are identified via SVR. The prediction competency of SVR and the stable behavior of BSC are aggregated in this architecture for nonlinear systems. The performance evaluation of the proposed adaptive BSC has been examined on a nonlinear inverted pendulum(IP) and a nonlinear mass–spring–damper(NMSD) system. The acquired results provide a successful and stable BSC control performance for both nonlinear systems. [ABSTRACT FROM AUTHOR]

This paper proposes an inverse-free and model-free control scheme based on gradient neural dynamics (GND), which avoids the calculation of pseudo-inverse, to achieve the tracking control of redundant robot manipulators without knowing their kinematic models. Specifically, two GND models are deployed to solve the inverse kinematics problem and to estimate the unknown Jacobian matrix of manipulators respectively. We prove that the residual tracking error associated with the proposed scheme theoretically converges to an arbitrarily small upper bound in finite-time. Besides, combining GND and zeroing neural dynamics (ZND), this paper also proposes two control schemes based on hybrid neural dynamics to further achieve better performance. Moreover, the proposed continuous-time control schemes are improved to discrete-time algorithms to facilitate the deployment. Finally, the feasibility and merits of the proposed control schemes are revealed by experiments and comparisons. [ABSTRACT FROM AUTHOR]

Systems with a priori unknown and time-varying dynamic behavior pose a significant challenge in the field of Nonlinear Model Predictive Control (NMPC). When both the identification of the nonlinear system and the optimization of control inputs are done robustly and efficiently, NMPC may be applied to control such systems. This paper considers stable systems and presents a novel method for adaptive NMPC, called Adaptive Sampling Based Model Predictive Control (Adaptive SBMPC) , that combines a radial basis function neural network identification algorithm with a nonlinear optimization method based on graph search. Unlike other NMPC methods, it does not rely on linearizing the system or gradient based optimization. Instead, it discretizes the input space to the model via pseudo-random sampling and feeds the sampled inputs through the nonlinear model, producing a searchable graph. For this discretization, an optimal path is found using Lifelong Planning A ⁎ , an efficient graph search method. Adaptive SBMPC is used in simulation to identify and control a simple plant with clearly visualized nonlinear behavior. In these simulations, both fixed and time-varying dynamic systems are considered. Results are compared with an adaptive version of Neural GPC, an existing NMPC algorithm based on Newton–Raphson optimization and a back propagation neural network model. When the cost function exhibits many local minima, Adaptive SBMPC is successful in finding a low-cost solution that appears close globally optimal while Neural GPC converges to a solution that is only locally optimal. This paper presents the method, soundness and completeness theory, and two simulated NMPC examples. The first is a transparent single-input single-output example, and the second considers a more complex power plant combustion process with two inputs and three outputs. [ABSTRACT FROM AUTHOR]

Successful detection and localisation of pedestrians is an important goal in computer vision which is a core area in Artificial Intelligence. State-of-the-art pedestrian detectors proposed in literature have reached impressive performance on certain datasets. However, it has been pointed out that these detectors tend not to perform very well when applied to specific scenes that differ from the training datasets in some ways. Due to this, domain adaptation approaches have recently become popular in order to adapt existing detectors to new domains to improve the performance in those domains. There is a real need to review and analyse critically the state-of-the-art domain adaptation algorithms, especially in the area of object and pedestrian detection. In this paper, we survey the most relevant and important state-of-the-art results for domain adaptation for image and video data, with a particular focus on pedestrian detection. Related areas to domain adaptation are also included in our review and we make observations and draw conclusions from the representative papers and give practical recommendations on which methods should be preferred in different situations that practitioners may encounter in real-life. [ABSTRACT FROM AUTHOR]

This paper evaluates adaptive Q-learning (AQL) and single-partition adaptive Q-learning (SPAQL), two algorithms for efficient model-free episodic reinforcement learning (RL), in two classical control problems (Pendulum and CartPole). AQL adaptively partitions the state–action space of a Markov decision process (MDP), while learning the control policy, i.e. , the mapping from states to actions. The main difference between AQL and SPAQL is that the latter learns time-invariant policies, where the mapping from states to actions does not depend explicitly on the time step. This paper also proposes the SPAQL with terminal state (SPAQL-TS), an improved version of SPAQL tailored for the design of regulators for control problems. The time-invariant policies are shown to result in a better performance than the time-variant ones in both problems studied. These algorithms are particularly fitted to RL problems where the action space is finite, as is the case with the CartPole problem. SPAQL-TS solves the OpenAI GymCartPole problem, while also displaying a higher sample efficiency than trust region policy optimization (TRPO), a standard RL algorithm for solving control tasks. Moreover, the policies learned by SPAQL are interpretable, while TRPO policies are typically encoded as neural networks, and therefore hard to interpret. Yielding interpretable policies while being sample-efficient are the major advantages of SPAQL. The code for the experiments is available at https://github.com/jaraujo98/SinglePartitionAdaptiveQLearning. • Two recent Q-learning algorithms, AQL and SPAQL, are evaluated on two classical control benchmarks. • Based on insights from control theory, a new algorithm, SPAQL-TS, is introduced. • It is shown that both SPAQL and SPAQL-TS outperform TRPO in the Cartpole problem. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks'' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this paper are the determination of Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller and the development of new adaptation strategies based on the tracking error dynamics and Lyapunov stability analysis to improve the closed loop performances. Efficiency of the proposed controller is illustrated with nonlinear system simulations. An application of the developed approaches to a hot-air blower is presented in order to validate simulations results. [Copyright &y& Elsevier]

Abstract: The precision, performance, and robustness of model-based controllers depend, to a large extent, on the accuracy of the inverse dynamic model which is incorporated in the design of the controller. Due to complex nature of the laser cladding process and presence of time-varying uncertainties, derivation of an accurate mathematical inverse dynamic model of the process is very difficult, and involves many unknown parameters. The inverse dynamic model of the complex nonlinear laser cladding process, which is difficult to be described mathematically, can be described by a fuzzy logic-based inverse dynamic model constructed form input–output data. In this paper, the development of an adaptive fuzzy inverse dynamic model of the laser cladding process, using a systematic fuzzy modelling approach is presented. In a closed-loop laser cladding process, the scanning speed of the substrate is required to produce a clad with desired geometry and quality. In this paper, a fuzzy inverse dynamic model that describes the scanning speed as a function of the cladding parameters in particular the clad height is developed. The developed fuzzy model is validated by comparing the model output with experimental data. The results are very promising and show that fuzzy models can accurately describe the process dynamics. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with a new variant of Particle Swarm Optimization (PSO) in which no a priori parameter tuning is necessary. PSO, as an efficient and powerful problem-solving technique, has been widely used, but, as in other Evolutionary Algorithms (EA), appropriate adjustment of its parameters is cumbersome and usually requires a lot of time, effort and luck. Thus, a self-adaptive framework is proposed to improve the robustness of the PSO. In this paper, within a framework that also includes other variants previously introduced by the authors, the algorithm’s parameters are co-evolved with the particles. Its performance results show that the use of this self-adaptive feature averages out the performance of standard PSO and other EA applied to the same problems, namely the design of Water Supply Systems (WSS), while avoiding the process of localizing and fine-tuning suitable parameters values, when using two benchmarking problems presented in the literature, namely the Hanoi Water Supply System and the New York Tunnel Water Supply System. The results provided in the case of a real-world problem demonstrate the scalability of the proposed variant to the realistic water distribution design problems, which are much larger. [Copyright &y& Elsevier]

Abstract: This paper introduces an adaptive reference tracking controller based on the online genetic estimation of the parameters of the system. The main novelty of the paper relies on the fact that the stability of the genetic adaptive scheme is analytically proved and not simply validated by means of simulation as it is customary in the literature. The resulting set-up is flexible enough to be integrated within a great variety of genetic estimation algorithms, which can in many cases outperform traditional estimation procedures. The goal is achieved by using a certain two-degree-of-freedom (2-DOF) based implementation of the control law in which the reference tracking property is separated from the closed-loop stability. Within this framework, the here-presented procedure for the genetic controller synthesis just affects two time-varying pre-filter blocks that do not compromise the closed-loop stability under weak conditions. In this manner, the power and versatility of genetic algorithms can be safely used to achieve tracking performance disregarding stability, which is delegated to a static feedback controller designed on the basis of robust control theory. [Copyright &y& Elsevier]

Abstract: This paper introduces an approach to off-policy Monte Carlo (MC) learning guided by behaviour patterns gleaned from approximation spaces and rough set theory introduced by Zdzisław Pawlak in 1981. During reinforcement learning, an agent makes action selections in an effort to maximize a reward signal obtained from the environment. The problem considered in this paper is how to estimate the expected value of cumulative future discounted rewards in evaluating agent actions during reinforcement learning. The solution to this problem results from a form of weighted sampling using a combination of MC methods and approximation spaces to estimate the expected value of returns on actions. This is made possible by considering behaviour patterns of an agent in the context of approximation spaces. The framework provided by an approximation space makes it possible to measure the degree that agent behaviours are a part of (“covered by”) a set of accepted agent behaviours that serve as a behaviour evaluation norm. Furthermore, this article introduces an adaptive action control strategy called run-and-twiddle (RT) (a form of adaptive learning introduced by Oliver Selfridge in 1984), where approximate spaces are constructed on a “need by need” basis. Finally, a monocular vision system has been selected to facilitate the evaluation of the reinforcement learning methods. The goal of the vision system is to track a moving object, and rewards are based on the proximity of the object to the centre of the camera field of view. The contribution of this article is the introduction of a RT form of off-policy MC learning. [Copyright &y& Elsevier]

This paper presents a quantized-state-based decentralized control strategy for uncertain interconnected nonlinear systems with time-varying delays under a networked control environment. Full state variables quantized via a uniform-hysteretic quantizer are available only for a decentralized adaptive control design. Unlike the existing decentralized adaptive recursive control designs, this study is focused on establishing a quantized-state-based decentralized learning mechanism for neural networks using discontinuously quantized states. Moreover, the stability of a decentralized neural network tracking system in the presence of time delays is analyzed. For each subsystem, a neural-network-based local adaptive tracker using a delay compensator is designed based on the local quantized state feedback. Technical lemmas pertaining to quantization errors and adaptive laws are presented to ensure the boundedness of all closed-loop signals and the convergence of local tracking errors to an adjustable neighborhood of the origin. Finally, illustrative simulations clarify and verify the decentralization strategy of the developed state-quantized adaptive tracking system. The control performance is evaluated using the root mean square control errors, where the values are 0.0293 and 0.0134 for each subsystem in Example 1 and 0.0467 and 0.0384 for each subsystem in Example 2. [ABSTRACT FROM AUTHOR]

Stable operations of control systems play a vital role in mission accomplishments of the second-order nonlinear systems, such as six-axis robots used in intelligent production lines, industrial equipment and control systems. In this paper, a fault-tolerant control scheme is developed for a class of second-order nonlinear control system under actuator bias faults and loss of effectiveness faults via the zero-sum differential game method. Based on the backstepping method, a controller is designed to ensure system tracking performance. Then by the zero-sum differential game method, a fault-tolerant controller is designed for the equivalent error system. Simulation results show the validity of the designed fault-tolerant control scheme. • The stability of the system and the optimality of control consumption are achieved. • Both unknown bias and loss of effectiveness faults are solved. • Uncertainty of the control gain matrix caused by the fault pattern matrix is solved. [ABSTRACT FROM AUTHOR]

As a heuristic algorithm to solve the nonlinear optimal control problem, adaptive dynamic programming is commonly constructed from one-step temporal difference learning. Eligibility trace can effectively speed up controller learning by considering the multi-step information in reinforcement learning. However, eligibility trace will bring in additional computational consumption and increase the learning burden. This paper attempts to take advantage of eligibility trace and avoids the high learning consumption at the same time. Therefore, an event-based neural dynamic programming (λ) [ENDP(λ)] algorithm via the actor–critic framework is constructed to solve the near-optimal control problem of unknown discrete-time systems. First, the modified forward view with eligibility trace is derived, which is suitable for engineering practice. Second, based on the event-triggered mechanism, ENDP(λ) is designed to relieve the pressure of communication consumption. Then, the event-based system is proven to ensure the input-to-state stability under a suitable triggering condition. Moreover, three neural networks are given to approximate the one-step cost function, the n -step cost function, and the control law, respectively. Finally, two typical experimental simulation examples are presented to verify the effectiveness of the ENDP(λ) algorithm. [ABSTRACT FROM AUTHOR]

The paper presents three new autonomous systems for the control of lateral-directional motion of airplane during landing. The geometry of airplane motion in lateral-directional plane takes into account the wind having different velocities and directions. All the three control systems contain an adaptive control subsystem based on the concepts of dynamic inversion and neural networks; the reference models of the adaptive control systems receive the signals provided by Pseudo Control Hedging blocks, as additional signals, to cancel the neural networks’ adapting difficulties in the case of nonlinear actuators with time delay and saturation zones with respect to the velocity and displacement. Two of the three automatic control systems have proportional–derivative/proportional–integral– derivative controllers for the control of the lateral deviation of airplane with respect to the runway. The new designed adaptive architectures have been software implemented and validated by complex numerical simulations; the obtained results prove the new architectures’ stability and small overshoots. [ABSTRACT FROM AUTHOR]

This paper presents a method for re-parameterization based on an arbitrary scalar field named the relaxation field. The relaxation field is applied to re-distribute the control-points of a parametric surface towards the desired areas. The proposed method was developed for possible application in an intelligent shape optimization procedure where a sensitivity field with respect to an objective function (or some other physical field) would be used for constructing the relaxation field. It could hence contribute to the concentrating the control-points at areas where significant changes in the geometry are expected. The method can easily be used in shape optimization since it keeps the number of variables constant during the redistribution of control-points as opposed to adaptive insertion of control points when using T-spline and similar methods. The same method can also be used in surface fitting by choosing the relaxation field based on the geometric error. This leads to an adaptive iterative fitting method. The method was validated by fitting a single patch B-spline surface to triangulated point clouds. The point-clouds were obtained by 3D scanning or from a CAD model. Examples include several complex engineering objects. The proposed method uses a parameterization method based on a combination of harmonic mapping and a mapping method based on a spring mesh. By relaxation using a spring mesh, the method allocates more parametric space to the regions of interest, thus assigning them more control points. The combination of these two mapping methods provides for increased local control while keeping the global smoothness of the parameterization. [ABSTRACT FROM AUTHOR]

The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real-time. For concept demonstration, a trajectory-following optimization problem of a Kinova robotic arm is solved using an integral reinforcement learning approach with guaranteed stability for slowly varying dynamics. The solution is implemented using a model-free value iteration process to solve the integral temporal difference equations of the problem. The performance of the proposed technique is benchmarked against that of another model-free high-order approach and is validated for dynamic payload and disturbances. Unlike its benchmark, the proposed adaptive strategy is capable of handling extreme process variations. This is experimentally demonstrated by introducing static and time-varying payloads close to the rated maximum payload capacity of the manipulator arm. The comparison algorithm exhibited up to a seven-fold percent overshoot compared to the proposed integral reinforcement learning solution. The robustness of the algorithm is further validated by disturbing the real-time adapted strategy gains with a white noise of a standard deviation as high as 5%. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, infringements have become increasingly serious. Digital watermarking is an effective method to protect information. The current watermarking technology still has room for further improvement in imperceptibility and robustness. This paper proposes an improved watermarking technology using meta-heuristic algorithm. Further, Quick Response code (QR code) is used as a carrier to transmit information. The improved Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) is used to hide the watermark into the QR code. Therefore, digital watermarking is realized on the QR code. In the common watermark embedding methods, the digital watermark is related to the embedding strength. How to find a suitable embedding factor and reduce distortion is of great significance to these watermarking algorithms. This paper mainly proposes two novel algorithms based on States of Matter Search (SMS) algorithm to find suitable embedding factors. The first algorithm uses an adaptive parameter to control the movement of particles called the adaptive step States of Matter Search (sSMS). The second algorithm incorporates co-evolutionary matrix to enhance the search capability named Co-evolution States of Matter Search (CSMS). DWT-SVD is updated through two algorithms to acquire optimal embedding strength factors on the QR code watermarking. By adjusting the embedding strength factors, the intensity of the watermark embedded in different frequency domains would be modified. The experimental results have higher PSNR and the QR code can still be decoded by a general decoder. It shows that the proposed approaches are practicable and effective. [ABSTRACT FROM AUTHOR]

Heuristic stochastic optimization techniques such as genetic algorithm perform global search, but they suffer from the problem of slow convergence rate near global optimum. On the other hand deterministic techniques such as gradient descent exhibit a fast convergence rate around global optimum but may get stuck in a local optimum. Motivated by these problems, a hybrid learning algorithm combining genetic algorithm (GA) with gradient descent (GD), called HGAGD, is proposed in this paper. The new algorithm combines the global exploration ability of GA with the accurate local exploitation ability of GD to achieve a faster convergence and also a better accuracy of final solution. The HGAGD is then used to train a qubit neural network (QNN), which is a good candidate for enhancing the computational efficiency of conventional neural networks, for two different applications. Firstly, a benchmark function is chosen to illustrate the potential of the proposed approach in dealing with function approximation problem. Subsequently, the feasibility of the proposed method in designing an indirect adaptive controller for damping of low frequency oscillations in power systems is studied. The results of these studies show that the proposed controller trained by the HGAGD can achieve satisfactory control performance. [ABSTRACT FROM AUTHOR]

The pneumatic muscle actuator (PMA) is one of the most promising actuators especially for the applications that require greater proximity between the humans and the robots. Fast and precise control of PMA, however, is difficult to achieve due to the compressibility of the air and the elasticity of the PMA. In order to achieve accurate and consistent tracking performance of a one axis PMA actuated manipulator over considerably wide ranges of frequency and stroke, an intelligent adaptive control algorithm is developed in this paper. The adaptive learning is enabled by a neural network in which the control gains to a fuzzy sliding mode controller (FSMC) and an integrator are adjusted to minimize the tracking error. Experimental results show that the proposed control strategy achieves fast, accurate and consistent performance tracking sinusoidal reference trajectories up to 1 Hz in frequency and close to the extreme stroke of the PMA actuated manipulator with the compressed air regulated to 4 bar. Results also show that the proposed control strategy, with a more aggressive learning for the control gain to the FSMC, achieves satisfactory performance tracking a trapezoidal reference trajectory. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel object tracking algorithm which combines a tracking-by-detection approach with a recommender system. The proposed algorithm consists of two steps: (1) to match detected features with a three-layer feature dictionary, i.e., target appearance model, which is initially built on the first frame and being adaptive frame-by-frame; (2) to add detected features into the aforementioned dictionary and use a recommender system to optimize the dictionary simultaneously. Among the two steps, while the former determines a new location for the target, the latter updates the target appearance model. Extensive experiments, which are done on 20 benchmark video sequences, demonstrate that the tracking results for the proposed novel method outperforms the state-of-the-art trackers both in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Developing controller for uncertain nonlinear systems in the presence of disturbances is an important and still challenging problem. Adaptive control method asserts to adapt system parameters against uncertainties, if only uncertainties change sufficiently slowly. Alternatively, if uncertainties stay in known bounds, robust control approaches claim to ensure system stability. In this paper, a Proportional–Integral (PI) indirect adaptive Least Squares Support Vectors Regression (LS-SVR) control scheme for a class of uncertain nonlinear system in the presence of large and fast disturbances is proposed. The LS-SVR is used to approximate the nonlinear uncertainty which must be bounded, whereas in comparison to robust control methodologies no requirement needs for bounds to be known. The asymptotic stability of the control scheme is proved by using Lyapunov synthesis. The simulation study is performed on a second-order inverted pendulum system in the presence of fast against slow and large against small disturbances to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

A realistic control paradigm should concurrently account for different sources of uncertainty such as those in modeling parameters, external disturbances and noise, as well as operational cost. Yet, this is a daunting task for which many current control approaches lack in one aspect or the other. In particular, the consumed control energy is an important aspect of controller design that is often ignored. In this paper, we propose a stable robust adaptive interval type-2 fuzzy H 2 /H ∞ controller (RAIT2FH 2 H ∞ C) for a class of uncertain nonlinear systems that aims to address the above concerns through its hybrid robust/adaptive structure. In particular, the H 2 energy and tracking cost function is minimized with respect to a H ∞ disturbance attenuation constraint, while the adaptive interval fuzzy logic system (IT2FLS) handles the uncertainties in approximating the unknown nonlinear dynamics of the system. In principle, the interval fuzzy logic approach aims to manage portions of uncertainty that could not be precisiated, leading to improved error performance. Several simulation studies, with or without disturbance and noisy measurements, as well as actual experimental implementation on a 3-PSP (prismatic-spherical-prismatic) parallel robot confirm this assessment. More specifically, in comparison with a competing methodology as well as its type-1 counterpart, the proposed interval type-2 strategy expends better or comparative control effort while reaching considerably better tracking performance. [ABSTRACT FROM AUTHOR]

The development of autonomous transportation systems has received increasing attention over the last decades. Different from existing automatic train control systems, the decision-making capability in the autonomous driving system enables a train to adapt to the complicated and dynamic circumstances. This paper in particular focuses on the decision-making problem for the autonomous driving of intelligent high-speed trains, and proposes a novel decision-making framework by combining the deep learning and backstepping control methods. By exploiting the deep learning methods, a speed trajectory generator is trained with the actual driving data, and dynamically calculates the reference speed trajectory according to the real-time driving condition. Then, a backstepping controller is designed to track the reference speed trajectory such that the separation, cohesion and alignment requirements for the autonomous driving of high-speed trains are achieved. Simulation experiments are implemented to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

This paper presents an analysis and comparison of neural-adaptive controllers applied in a control structure of an electrical drive with an elastic mechanical coupling between the driving motor and a load machine, using only one state variable used in the feedback loop (a motor speed). However, the presented considerations can be assumed as a general neural speed control of the drive with a fast enough electromagnetic torque control loop of an electrical machine. This is justified by analogy with a design process independent of the parameters of a specific drive system and its electromagnetic torque control loop. Four types of neuro-controllers and training methods are analyzed: Adaptive Linear Neuron with Delta Rule, Multi-Layer Perceptrons Neural Network with the Backpropagation method, Feedforward Network with Adaptive Interaction adaptation and Radial Basis Function Neural Network with gradient algorithm, applied as speed controllers. Two main problematic issues related to neural controllers trained on-line are discussed: initial parameters selection for a neural network and determination of learning factors used in adaptation algorithms. Simulations are confirmed in experiment tests, using dSPACE1103 card. All the tested neurocontrollers are compared to a classical PI solution with one state variable used in the feedback loop of the analyzed drive system. [ABSTRACT FROM AUTHOR]

The study of electromyographic (EMG) signals has gained increased attention in the last decades since the proper analysis and processing of these signals can be instrumental for the diagnosis of neuromuscular diseases and the adaptive control of prosthetic devices. As a consequence, various pattern recognition approaches, consisting of different modules for feature extraction and classification of EMG signals, have been proposed. In this paper, we conduct a systematic empirical study on the use of Fractal Dimension (FD) estimation methods as feature extractors from EMG signals. The usage of FD as feature extraction mechanism is justified by the fact that EMG signals usually show traces of self-similarity and by the ability of FD to characterize and measure the complexity inherent to different types of muscle contraction. In total, eight different methods for calculating the FD of an EMG waveform are considered here, and their performance as feature extractors is comparatively assessed taking into account nine well-known classifiers of different types and complexities. Results of experiments conducted on a dataset involving seven distinct types of limb motions are reported whereby we could observe that the normalized version of the Katz׳s estimation method and the Hurst exponent significantly outperform the others according to a class separability measure and five well-known accuracy measures calculated over the induced classifiers. [ABSTRACT FROM AUTHOR]

This paper proposes an agent-based approach using artificial immune system (AIS) paradigms as a successful mechanism for a distributed intrusion detection system (IDS). The AIS paradigms are negative selection, clonal selection, danger theory, and immune network. These paradigms are very successful for anomaly IDS. The AIS paradigms are inspired by the powerful human immune system (HIS) and are promising candidate for design of an IDS. The proposed AIS-based agents are capable of learning, self-adaption, platform mobility, autonomy and collaboration. The proposed system (MAIS-IDS) was designed using these powerful and collaborative agents. This system has mobile and static agents with detector agents as the main actors in MAIS-IDS. The life cycles of agents are determined using the proposed immune algorithms in specific phases. Essential characteristics of MAIS-IDS are cloning, mutation, migration, collaboration, and randomness. MAIS-IDS was evaluated using a network of virtualized hosts, a kernel-based virtual machine (KVM) hypervisor and management Orchestra. [ABSTRACT FROM AUTHOR]

In this paper, a safe and learning-based control framework for model predictive control (MPC) is proposed to optimize nonlinear systems with a non-differentiable objective function under uncertain environmental disturbances. The control framework integrates a learning-based MPC with an auxiliary controller in a way of minimal intervention. The learning-based MPC augments the prior nominal model with incremental Gaussian Processes to learn the uncertain disturbances. The cross-entropy method (CEM) is utilized as the sampling-based optimizer for the MPC with a non-differentiable objective function. A minimal intervention controller is devised with a control Lyapunov function and a control barrier function to guide the sampling process and endow the system with high probabilistic safety. The proposed algorithm shows a safe and adaptive control performance on a simulated quadrotor in the tasks of trajectory tracking and obstacle avoidance under uncertain wind disturbances. [ABSTRACT FROM AUTHOR]

Abstract: Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme. [Copyright &y& Elsevier]

Abstract: The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications. However, the structure of a CMAC network which will influence the learning performance is difficult to select. This paper proposes a dynamic structure CMAC network (DSCN) which the network structure can grow or prune systematically and their parameters can be adjusted automatically. Then, an adaptive dynamic CMAC neural control (ADCNC) system which is composed of a computation controller and a robust compensator is proposed via second-order sliding-mode approach. The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L 2 tracking performance with a desired attenuation level. Moreover, a proportional–integral (PI)-type adaptation learning algorithm is derived to speed up the convergence of the tracking error in the sense of Lyapunov function and Barbalat’s lemma, thus the system stability can be guaranteed. Finally, the proposed ADCNC system is applied to control a chaotic system. The simulation results are demonstrated that the proposed ADCNC scheme can achieve a favorable control performance even under the variations of system parameters and initial point. [Copyright &y& Elsevier]

Abstract: This paper presents a novel H ∞ tracking-based direct adaptive fuzzy controller (HDAFC) for a class of perturbed uncertain affine nonlinear systems involving external disturbances and measurement noise. A practical interval type-2 (IT2) fuzzy logic system (FLS) is introduced to approximate the ideal control law. To eliminate the tradeoff between H ∞ tracking performance and high gain at the control input, a modified output tracking error is introduced. Based on the proposed fired-rule-determination algorithm, a practical average defuzzifier expressed in parameterized and closed formula is developed for the IT2 FLS. Without the restriction that the control gain function is exactly known, the IT2 HDAFC is constructed and its adaptive law is derived by virtue of the Lyapunov synthesis. To improve control performance under measurement noise, the recursive linear smoothed Newton predictor is further introduced as a delayless output filter. Simulated application of a single-link robot manipulator demonstrates the superiority of the proposed approach over the previous approach in terms of the settling time, tracking accuracy, energy consumption and smoothness of the control input. [Copyright &y& Elsevier]

Abstract: This paper presents a constrained Self-adaptive Differential Evolution (SaDE) algorithm for the design of robust optimal fixed structure controllers with uncertainties and disturbance. Almost all real world optimization problems have constraints which should be satisfied along with the best optimal solution for the problem. In evolutionary algorithms (EAs) the presence of constraints reduces the feasible region and complicates the search process. Therefore, a suitable method to handle the constraints must also be executed. In the SaDE algorithm, four mutation strategies and the control parameter CR are self-adapted. Self-adaptive Penalty (SP) method is introduced into the SaDE algorithm for constraint handling. The performance of SaDE algorithm is demonstrated on the design of robust optimal fixed structure controller of three systems, namely the linearized magnetic levitation system, F-8 aircraft linearized model and a SISO plant. For the comparison purpose, reported results of constrained PSO algorithm and five DE algorithms with different strategies and parameter values are taken into account. Statistical performance in 20 independent runs is considered to compare the performance of algorithms. From the obtained results, it is observed that SaDE algorithm is able to self-adapt the mutation strategy and the crossover rate and hence performs better than the other variants of DE and the constrained PSO algorithm. Better performance of SaDE is achieved by sustained maintenance of diversity throughout the evolutionary process thus producing better individuals consistently. This also aids the algorithm to escape from local optima thereby avoiding premature convergence. [Copyright &y& Elsevier]

Abstract: In this paper, a Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization (MSCPSO) based on four sub-swarms is presented. In the proposed algorithm, several strategies are employed to avoid falling into local optimum, improve the diversity and achieve better solution. Particles in each sub-swarms share the only global historical best optimum to enhance the cooperative capability. Besides, the inertia weight of a particle in each sub-swarms is modified, which is subject to the fitness information of all particles, and the adaptive strategy is employed to control the influence of the historical information to create a more potential search ability. To effectively keep the balance between the global exploration and the local exploitation, the particle in each takes advantage of the shared information to maintain cooperation with each other and guides its own evaluation. On the other hand, in order to increase the diversity of the particles and avoid falling into a local optimum, a various diversity operation is adopted to guide the particles to jump out of the local optimum and achieve the global best position smoothly. The proposed method was applied to some well-known benchmarks; the results demonstrated good performances of MSCPSO in solving the complex multimodal functions. [Copyright &y& Elsevier]

Abstract: A novel direct adaptive interval type-2 fuzzy neural network (FNN) controller in which linguistic fuzzy control rules can be directly incorporated into the controller is developed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances, in this paper. By incorporating direct adaptive interval type-2 FNN control scheme and sliding mode approach, two non-identical chaotic systems can be synchronized based on Lyapunov stability criterion. Moreover, the chattering phenomena of the control efforts can be reduced and the external disturbance on the synchronization error can be attenuated. The stability of the proposed overall adaptive control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded. From the simulation example, to synchronize two non-identical Chua’s chaotic circuits, it has been shown that type-2 FNN controllers have the potential to overcome the limitations of tpe-1 FNN controllers when training data is corrupted by high levels of uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: One of the very important way to save the electrical energy in distribution system is network reconfiguration for loss reduction. This paper proposes a new hybrid evolutionary algorithm for solving the distribution feeder reconfiguration (DFR) problem. The proposed hybrid evolutionary algorithm is the combination of SAPSO (self-adaptive particle swarm optimization) and MSFLA (modified shuffled frog leaping algorithm), called SAPSO–MSFLA, which can find optimal configuration of distribution network. In the PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Therefore, a self-adaptive framework is proposed to improve the robustness of the PSO, also in the modified shuffled frog leaping algorithm (MSFLA) to improve the performance of algorithm a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea of integrating SAPSO and MSFLA is to use their advantages and avoid their disadvantages. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization in minimum time. [ABSTRACT FROM AUTHOR]

Abstract: The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments. [Copyright &y& Elsevier]

Abstract: Many metaheuristics are either based on neighborhood search or the construction of solutions. Examples for the latter ones include ant colony optimization and greedy randomized adaptive search procedures. These techniques generally construct solutions probabilistically by sampling a probability distribution over the search space. Solution constructions are generally independent from each other. Recent algorithmic variants include two important features that are inspired by deterministic branch and bound derivatives such as beam search: the use of bounds for evaluating partial solutions, and the parallel and non-independent construction of solutions. In this paper we give a theoretical reason of why these algorithms generally work very well in practice. Second, we confirm our theoretical findings by means of practical examples. After the application to artificial problems, we present experimental results concerning the well-known open shop scheduling problem. [Copyright &y& Elsevier]

Abstract: System identification is a challenging and complex optimization problem due to nonlinearity of the systems and even more in a dynamic environment. Adaptive infinite impulse response (IIR) systems are preferably used in modeling real world systems because of their reduced number of coefficients and better performance over the finite impulse response filters. Particle swarm optimization (PSO) and its other variants has been a subject of research for the past few decades for solving complex optimization problems. In this paper, PSO with quantum infusion (PSO–QI) is used in identification of benchmark IIR systems and a real world problem in power systems. PSO–QI’s performance is compared with PSO and differential evolution PSO (DEPSO) algorithms. The results show that PSO–QI has better performance over these algorithms in identifying dynamical systems. [Copyright &y& Elsevier]

Abstract: In this paper a new color space, called the RGB color ratio space, is proposed and defined according to a reference color such that an image can be transformed from a conventional color space to the RGB color ratio space. Because a color in the RGB color ratio space is represented as three color ratios and intensity, the chrominance can be completely reserved (three color ratios) and the luminance can be de-correlated with the chrominance. Different from traditional distance measurement, a road color model is determined by an ellipse area in the RGB ratio space enclosed by the estimated boundaries. A proposed adaptive fuzzy logic in which fuzzy membership functions are defined according to estimated boundaries is introduced to implement clustering rules. Therefore, each pixel will have its own fuzzy membership function corresponding to its intensity. A basic neural network is trained and used to achieve parameters optimization. The low computation cost of the proposed segmentation method shows the feasibility for real time application. Experimental results for road detection demonstrate the robustness to intensity variation of the proposed approach. [Copyright &y& Elsevier]

Abstract: In this paper, in order to deal with training data corrupted by noise or rule uncertainties, a new observer-based indirect adaptive interval type-2 fuzzy controller is developed for nonlinear MIMO systems involving external disturbances using fuzzy descriptions to model the plant. Based on the universal approximation theorem, a fuzzy logic controller equipped with a training algorithm is proposed such that the tracking error, because of the matching error and external disturbance, is attenuated to an arbitrary desired level using the H ∞ tracking design technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbances—data uncertainties, very well, but the adaptive type-1 fuzzy controller must expend more control effort in order to handle noisy training data. In the meantime, the adaptive fuzzy controller can perform successful control and guarantee that the global stability of the resulting closed-loop system and the tracking performance can be achieved. [Copyright &y& Elsevier]

Abstract: The paper presents an algorithm of idle speed stabilization in the spark ignition automotive engine by means of spark advance control. The algorithm is based on a well-known approach of a model-based adaptive control and uses artificial neural networks. The control algorithm is based on a neural network model observer of the additional effective torque. The additional load is estimated as difference between effective torque, estimated by the neural network observer, and brake torque, estimated on the basis of a linear quadratic model. In that case the additional load is understood as the sum of the alternator brake torque (additional load form electric car equipments) and the momentary and/or permanent changes of the engine’s characteristics. On the basis of estimated values of the additional load, the required value of angular acceleration is determined to make the engine return to the specified speed. This acceleration is achieved by adjusting the spark advance. The required value of spark advance is estimated by means of a neural network model converse to that of the effective torque. The algorithm was experimentally compared with PID and adaptive algorithms in the same test bed. The tests were conducted under sudden change of external load. The proposed algorithm proved to be more effective in terms of control error. [Copyright &y& Elsevier]

Abstract: The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring. [Copyright &y& Elsevier]

Abstract: This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which is developed using recurrent high-order neural networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well-known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum. [Copyright &y& Elsevier]