Robotic systems operating in dynamic and uncertain environments require motion planners that can rapidly adapt to environmental changes while maintaining safety and efficiency. Frequent replanning is inevitable in such scenarios as obstacle configurations evolve and previously feasible trajectories become invalid. However, real-time replanning remains challenging for many applications due to the high computational cost of collision checking and graph maintenance, especially when edge evaluations are expensive or when the environment changes continuously. The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. The algorithm maintains an incremental search graph which is reused throughout the entire navigation process. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Theoretical analysis indicates that the proposed algorithm converges to the optimal solution as long as sufficient planning time is given. Planning results on robotic systems with SE(3) and R7state spaces in challenging environments highlight the superior performance of the proposed algorithm over various state-of-the-art sampling-based planners in both static and dynamic motion planning tasks. The experiment of planning for a Turtlebot 4 operating in a dynamic environment with several moving pedestrians further verifies the feasibility and advantages of the proposed algorithm.

Drawing inspiration from the ability of fish to maintain efficient swimming over a wide range of speeds by tuning the stiffness of their tails, researchers have explored stiffness adjustment mechanisms in fish-like robots. Typically, existing mechanisms require extra actuators or power sources only for tuning stiffness, resulting in additional energy consumption and more complex structures. To address this, our study introduces an innovative fishtail featuring an online stiffness modulation mechanism that does not require additional actuators or power sources solely for stiffness adjustment. Through model-based simulations and experimental testing, we evaluated the effectiveness of the proposed method. The results demonstrate that the designed mechanism enables efficient swimming across a broader frequency range (0–4 Hz) compared to most servo-actuated platforms with adjustable stiffness reported in existing studies. The robot achieves a maximum average speed of 1.4 BL/s and a minimum cost of transport of 9.5 J/(m•kg).

Sampling-based algorithms are widely used for motion planning in high-dimensional configuration spaces. However, due to low sampling efficiency, their performance often diminishes in complex configuration spaces with narrow corridors. Existing approaches address this issue using handcrafted or learned heuristics to guide sampling toward useful regions. Unfortunately, these strategies often lack generalizability to various problems or require extensive prior training. In this paper, we propose a simple yet efficient sampling-based planning framework along with its bidirectional version that overcomes these issues by integrating different levels of planning granularity. Our approach probes configuration spaces with uniform random samples at varying resolutions and explores these multi-resolution samples online with a bias towards sparse samples when traveling large free configuration spaces. By seamlessly transitioning between sparse and dense samples, our approach can navigate complex configuration spaces while maintaining planning speed and completeness. The simulation results demonstrate that our approach outperforms several state-of-the-art sampling-based planners in SE(2), SE(3), and R14with challenging terrains. Furthermore, experiments conducted with the Franka Emika Panda robot operating in a constrained workspace provide additional evidence of the superiority of the proposed method.

The exploration of energy-efficient active suspension control strategies for high-performance vibration suppression remains a critical challenge, particularly under partial-state measurements, uncertain dynamics, and external disturbances. This article proposes an innovative predefined-time output feedback control scheme for active vehicle suspension systems that achieves superior vibration mitigation with reduced energy consumption. By employing the time-varying scaling function technique, a predetermined-time extended state observer is developed to estimate unmeasurable velocities and lumped disturbance, while a second-order predefined-time filter is designed to avoid the explosion of computational complexity. Furthermore, using the effect characterization method and theX-mechanism reference dynamics, beneficial couplings/disturbances and nonlinearities can be reserved instead of direct cancellation, which leads to significant energy conservation up to 58% compared to other methods. Then, the predefined-time output feedback control is proposed to ensure that settling time can be arbitrarily user-specified using only one parameter, which is independent of initial conditions and control gains. Comparative experiments are performed to present the effectiveness and robustness of the proposed control method.

In nature, fish are excellent swimmers due to their flexible and precise control of tail, which allows them to freely transform between the smooth flapping and the motion of rapid response so that they can move with dexterity. Here, inspired by the versatile motion abilities of fish, a novel robotic fish has been developed, featuring the capability of adaptable bistability. Through tuning the bistability, the robot can acquire two locomotion modes, namely monostable and bistable modes, and it can also swim at different energy barrier that needs to be overcome to realize the bistable motion. The theoretical models are derived to facilitate the control of the robot and the understanding of its nonlinear behavior. The impact of the tunable bistability on the swimming and turning performance is investigated through extensive experiments. The study effectively demonstrates the robotic fish’s capability to swiftly and efficiently navigate through mode switches, enabled by its tunable bistability. This feature is essential for underwater robots to perform tasks in intricate environments.

The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Simulation results demonstrate that the algorithm outperforms various state-of-the-art sampling-based planners in addressing both static and dynamic motion planning problems.

This work is devoted to solving the control problem of vehicle active suspension systems (ASSs) subject to time-varying dynamic constraints. An adaptive control scheme based on nonlinear state-dependent function (NSDF) is proposed to stabilize the vertical displacement of the vehicle body. It provides a reliable guarantee of driving safety, ride comfort, and operational stability. It is commonly known that in the existing work, either the state constraints are ignored which may reduce the stability and safety of the system, or the virtual controller is subjected to some feasibility conditions affecting real system implementation. In this work, it is the first attempt to directly deal with the time-varying displacement and velocity of the vehicle constraints in ASSs without involving any specific feasibility conditions. A novel coordinate transformation based on the NSDF is introduced and integrated into each step of the backstepping design. Thus, the proposed control scheme not only adapts to the time-varying motion (time-varying vertical displacement and velocity) constraints, but also eliminates the feasibility conditions of the virtual controller without the difficulty of obtaining system parameters. Finally, the control scheme for ASSs used in this work is compared with existing control schemes in order to demonstrate its superiority and rationality.

In this paper, a novel periodic sliding mode control method is designed for 4-DOF tower crane systems with unmatched disturbances as well as unknown/time-varying control directions. Specifically, the nonlinear disturbance observer is constructed to solve the unmatched uncertainties. The problems arising from unknown/time-varying control directions are tackled through the period sliding mode technique. Unlike most previous unknown control direction-related studies, the control coefficient is allowed to cross 0 in a continuous way. In addition, to improve the payload swing suppression and elimination performance, nonlinear terms involving payload swing information are elaborately injected into the control design. As far as we know, the designed periodic sliding mode control scheme provides the first control method for crane systems to successfully guarantee positioning as well as anti-swing performance in spite of unmatched disturbances and unknown control directions. The rigorous theoretical analysis is presented through Lyapunov techniques. Several simulations and experimental results are carried out to illustrate the merits of the designed periodic sliding mode control method.

Active vehicle suspension systems exhibit substantial vibration isolation capabilities, however, suffer from external disturbances, high energy consumption, risks of fault signals, limited transient performance, etc. In this paper, a predefined-time fault-tolerant control scheme is proposed for active suspensions to improve ride comfort and reliability, and enhance energy conservation. The reference X-dynamics together with a conditional disturbance cancellation scheme are developed to avoid the cancellation of beneficial nonlinearities and beneficial disturbances, respectively, which can reduce energy consumption without any optimization calculation or hardware alteration. Importantly, the error signals can converge to a predefined bound within the predefined time interval. Both the settling time and the residual bound can be arbitrarily user-defined, which are independent of initial states and control gains. Especially, to avoid singularity and alleviate chattering, a continuous piecewise function and a quadratic fraction inequality are constructed. The utilization of the proposed predefined-time fault-tolerant control facilitates satisfactory ride comfort with low energy cost. Experimental results are presented to validate the superior control performance of the designed control scheme.

This paper presents Fast Adaptive Tree (FAT), an asymptotically-optimal sampling-based path planner for dynamic and uncertain scenarios. Namely, the solution extracted converges to the optimal solution given the sensor information as the number of samples approaches infinity. The planner maintains an underlying graph, which increasingly approximates the search domain, and a dynamic spanning tree of the graph, which contains the shortest path from the start to the goal state. The planner quickly responds to the availability of new information about the environments or the robot movements by minimally repairing the spanning tree over the navigation. The simulation results show that the proposed path planner achieves higher efficiency of replanning than several state-of-the-art path planners without sacrificing solution quality.

A novel control method of addressing coupling and disturbance influences for finite-time energy-saving robust control of active suspension systems (ASSs) is investigated. By elaborately constructing coupling and disturbance effect indicators, the pros and cons of coupling and disturbance influences on ASSs are discussed, and then a finite-time coupling and disturbance effects-triggered control method is designed via a second-order sliding mode control technique. Importantly, the good/bad coupling effects are assessed through a well-designed nonlinear function. By means of determining if the sign of disturbances conforms to the expected motion or not, the addition of beneficial disturbance effects or removal of detrimental disturbance effects is implemented. Noticeably, by employing a bioinspired nonlinear reference model, beneficial nonlinear stiffness and damping effects are thus utilized, leading to the possibility of energy-saving performance. As a result, the proposed control method exhibits a unique feature, i.e., fully employing potential contribution from the coupling and disturbance effects, and presents a totally new coupling and disturbance effects-triggered control framework, leading to obvious performance improvement. Benchmark experimental conclusions are devoted to distinguishing the advantages and effectiveness of the designed tracking method.

Developing a good control strategy for biomimetic robots is challenging. Robust control methods require an accurate model of the robot. Nowadays, model-free methods are being extensively explored for the control and navigation of terrestrial robots. In this paper, we consider a novel deep reinforcement learning-based model-free swimming control for our bio-inspired robotic tadpole. To realize this, we utilize dynamic motion primitives, which can represent a large range of motion behaviors, and combine them with a decoupled reinforcement learning framework. The proposed architecture optimizes the motion primitives first to develop a travelling wave undulation pattern in the tail and then to navigate the robot along different predefined paths. Through this framework, effective swimming gait emerges, and the robot is able to navigate well on the surface of water. This framework combines the optimization potential of deep reinforcement learning with stability and generalization properties of dynamic motion primitives. We train and test our method on a simulated model of the robot to demonstrate the effectiveness of the method and also conduct experimental testing on the real robot to verify the results.

This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously. The asymptotic stability is theoretically proved by a rigorous Lyapunov-based analysis. To the best of our knowledge, this is a unique control scheme for active suspension systems which can technically take several critical control practice issues into account with guaranteed excellent performance simultaneously, including energy savings, actuator saturation, unexpected disturbances, etc. The superior performance is well validated with a series of experiments, and carefully compared to several existing control methods. The results of this study would definitely present a unique insight and an alternative approach to active controller designs via exploiting beneficial nonlinear and disturbance effects for better control performance and lower energy cost simultaneously.

A novel adaptive neural network tracking control method is systematically investigated for a unique double-pendulum tower crane system model in this article. Several critical and practical application-oriented control issues, including robustness, tracking error limitation, double-pendulum effects, and input dead zone nonlinearity, are considered simultaneously, which have never been well addressed in the existing literature. Technically, neural networks are employed to approximate the functions with uncertain/unknown dynamics and nonideal inputs. Several barrier Lyapunov functions are proposed that can circumvent the violation of tracking error limitations in the proposed control method. Importantly, based on the designed adaptive neural network tracking control method, the jib and trolley can track their desired trajectories very fast, and the hook and payload sway can be completely eliminated. The Lyapunov stability theory and Babalat’s lemma are utilized to theoretically prove the convergence and stability of the proposed control system. Finally, well-designed simulation studies are carried out to verify the excellent performance and strong robustness of the control method. This article should be the first work considering a double-pendulum tower crane system with guaranteed convergence and performance without any linearization for the original nonlinear dynamic model.

Active suspension systems are widely used in vehicles to improve ride comfort and handling performance. However, existing control strategies may be limited by various factors, including insufficient consideration of different operation conditions, such as changing in vehicle mass, defects in strategy design leading to incapability for guaranteeing finite-time stability, lack of considering input effects of dead zone and saturation, excessive energy cost, etc. Importantly, very few results considered the energy-saving performance of active suspension systems although a well-perceived issue in practice. An adaptive fuzzy SMC method based on a bioinspired reference model is established in this article, which is to purposely address these problems and be able to provide finite-time convergence and energy-saving performance simultaneously. The proposed control method effectively utilizes beneficial nonlinear stiffness and nonlinear damping properties that the bioinspired reference model could provide. Therefore, superior vibration suppression performance with less energy consumption and improved ride comfort can all be obtained readily. By using a fuzzy-logic system (FLS), the proposed method is beneficial in compensating for system parameter uncertainties, external disturbances, input dead zones, and saturations. Furthermore, based on the adaptive PD-SMC method, the tracking errors can converge to zeros in finite time. The stability of the equilibrium point of all the states in active suspension systems is theoretically proven by Lyapunov techniques. Finally, simulation results are provided to verify the correctness and effectiveness of the proposed control scheme.

Online learning methods are designed to establish timely predictive models for machine learning problems. The methods for online learning of nonlinear systems are usually developed in the reproducing kernel Hilbert space (RKHS) associated with Gaussian kernel in which the kernel bandwidth is manually selected and remains steady during the entire modeling process in most cases. This setting may make the learning model rigid and inappropriate for complex data streams. Since the bandwidth appears in a nonlinear term of the kernel model, it raises substantial challenges in the development of learning methods with an adaptive bandwidth. In this article, we propose a novel approach to address this important open issue. By a carefully casted linearization scheme, the nonlinear learning problem is reasonably transformed into a state feedback control problem for a series of controllable systems. Then, by employing optimal control techniques, an effective algorithm is developed, and the parameters in the learning model including kernel bandwidth can be efficiently updated in a real-time manner. By taking advantage of the particular structure of the Gaussian kernel model, a theoretical analysis on the convergence and rationality of the proposed method is also provided. Compared with the kernel algorithms with a fixed bandwidth, our novel learning framework can not only achieve adaptive learning results with a better prediction accuracy but also show performance that is more robust with a faster convergence speed. Encouraging numerical results are provided to demonstrate the advantages of our new method.

This paper investigates the parameter optimization problem of membership functions for fuzzy-model-based control under imperfect premise matching. A novel frequency domain algorithm which can clearly relate the membership function parameters to the targeted control performance is developed, and consequently an optimization approach to the membership function parameters is then established based on the resulting nonlinear characteristic output spectrum function (nCOS). Compared to traditional search-based optimization approach, this method can give a more detailed result with less time consuming and an in-depth understanding of nonlinear influence rather than just optimal results. With this novel method, performance of the fuzzy-model-based controller is further enhanced. Finally, the fuzzy membership functions optimization method is applied to nonlinear systems to obtain improved system performance.

In this paper, we propose a novel kernel method for the online identification of stochastic nonlinear spatiotemporal dynamical systems using the robust control approach. By the difference method, the stochastic spatiotemporal (SST) systems driven by multiplicative noise are first transformed into a class of multi-input-multi-output-partially linear kernel models (PLKMs) with heterogeneous random terms. With the help of techniques established for reproducing kernel Hilbert space, the online learning problem is reasonably considered as an output feedback control problem for a group of time varying linear dynamical systems. We develop an effective algorithm to address the learning problem of PLKM and SST systems by employing the model predictive control theory. Compared with the existing learning methods, the new one can achieve adaptive, robust, and fast convergent online modeling performance for the spatiotemporal dynamics with multiplicative noise, which greatly facilitates the characterization of physical characteristics of the system. Moreover, this investigation for the first time addresses the learning problems for SST systems with novel robust control techniques, which can provide some novel insights into the design of kernel machine learning methods from the perspective of optimal control theory. Numerical studies for benchmark systems are presented to illustrate the effectiveness and efficiency of our new method.

This paper studies the observer-based tracking control problem for discrete-time nonlinear networked control systems with parameter uncertainties and unmeasurable state variables. A network-induced constraint, i.e., the intermittent measurement loss, is considered in the controller design. The uncertain nonlinear system is described by an interval type-2 (IT2) fuzzy Takagi-Sugeno model, in which the lower and the upper membership functions with corresponding coefficients are used to capture and express uncertainties existing in the system. A premise-variables-independent IT2 fuzzy observer is constructed to estimate the unmeasurable state variables, and then a novel IT2 fuzzy tracking controller is designed. Furthermore, sufficient criteria are established to guarantee the resulting closed-loop system to be stochastically stable. Finally, two examples are provided to show the effectiveness of the proposed approach.

This paper studies the problem of adaptive fuzzy control for a category of single-input single-output nonlinear networked control systems with network-induced delay and data loss based on adaptive backstepping control approach. Fuzzy logic systems are used to approximate the unknown nonlinear characteristics existing in the system, while Pade approximation is introduced to handle network-induced delay. Data loss occurs intermittently and stochastically in the data transmitting process, which is regarded as the delay in the controller design. In the framework of adaptive fuzzy backstepping technique, a novel state-feedback adaptive controller is constructed to ensure all signals in the resulting closed-loop system to be bounded and the state variables can be regulated to the origin. Finally, two examples are given to show the validity of the proposed results.

Feature selection is an important preprocessing technique for data analysis and data mining. One of main challenge for feature selection is to overcome the curse of dimensionality. Bacterial algorithms, like Bacterial Foraging Optimization (BFO), have been well-exploited as the metaheuristics for addressing the optimization problems. In this paper, an extended bacterial algorithm named as Bacterial-Inspired Feature Selection Algorithm (BIFS) is proposed. In BIFS, the searching process of bacteria consists of two main mechanisms: interactive swimming (or running) strategy used in Bacterial Colony Optimization (BCO), and random tumbling strategy embedded in Bacterial Foraging Optimization (BFO). The rule controlled foraging mode in BCO has been used in BIFS to overcome the high computational cost problem in most BFOs. Meanwhile, the `roulette wheel weighting' strategy is employed to weight the influence of features on the fitness functions and evaluate the distribution of the features within the large search space. Experiments on six benchmark datasets show that the proposed algorithm (i.e. BIFS) achieves higher classification accuracy rate in comparison to the four bacterial based algorithms and other three evolutionary algorithms. Furthermore, an additional real application of the proposed bacterial-inspired feature selection algorithm for fault diagnosis of complex structures in engineering has been developed. The results show that the proposed bacterial-inspired algorithm is capable of selecting the most sensitive sensors to detect and isolate the fault of complex structures.

This paper is concerned with the problem of adaptive tracking control for a class of nonlinear systems with parametric uncertainty, bounded external disturbance, and actuator saturation. In order to achieve robust output tracking for the saturated uncertain nonlinear systems, a combination of adaptive robust control (ARC) and a novel terminal sliding-mode-based nonlinear disturbance observer (TSDO) is proposed, where the modeling inaccuracy and disturbance are integrated as a lumped disturbance. Specifically, the observer errors of estimating the lump disturbances converge to zero in finite-time for improving the precision of estimation. The estimated disturbances are then used in the controller to compensate for the system's lumped disturbances. The analytical results show that the proposed scheme is stable and can guarantee the asymptotic tracking with the tracking error converging to zero even in the presence of disturbances. Finally, the developed method is illustrated the effectiveness by the application to control of a quarter-car model with active suspension system.

The identification of nonlinear spatiotemporal dynamical systems given by partial differential equations has attracted a lot of attention in the past decades. Several methods, such as searching principle-based algorithms, partially linear kernel methods, and coupled lattice methods, have been developed to address the identification problems. However, most existing methods have some restrictions on sampling processes in that the sampling intervals should usually be very small and uniformly distributed in spatiotemporal domains. These are actually not applicable for some practical applications. In this paper, to tackle this issue, a novel kernel-based learning algorithm named integral least square regularization regression (ILSRR) is proposed, which can be used to effectively achieve accurate derivative estimation for nonlinear functions in the time domain. With this technique, a discretization method named inverse meshless collocation is then developed to realize the dimensional reduction of the system to be identified. Thereafter, with this novel inverse meshless collocation model, the ILSRR, and a multiple-kernel-based learning algorithm, a multistep identification method is systematically proposed to address the identification problem of spatiotemporal systems with pointwise nonuniform observations. Numerical studies for benchmark systems with necessary discussions are presented to illustrate the effectiveness and the advantages of the proposed method.

This paper investigates the problem of sampled-data H∞ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy H∞ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method.

The identification of nonlinear spatiotemporal systems is of significance to engineering practice, since it can always provide useful insight into the underlying nonlinear mechanism and physical characteristics under study. In this paper, nonlinear spatiotemporal system models are transformed into a class of multi-input-multi-output (MIMO) partially linear systems (PLSs), and an effective online identification algorithm is therefore proposed by using a pruning error minimization principle and least square support vector machines. It is shown that many benchmark physical and engineering systems can be transformed into MIMO-PLSs which keep some important physical spatiotemporal relationships and are very helpful in the identification and analysis of the underlying system. Compared with several existing methods, the advantages of the proposed method are that it can make full use of some prior structural information about system physical models, can realize online estimation of the system dynamics, and achieve accurate characterization of some important nonlinear physical characteristics of the system. This would provide an important basis for state estimation, control, optimal analysis, and design of nonlinear distributed parameter systems. The proposed algorithm can also be applied to identification problems of stochastic spatiotemporal dynamical systems. Numeral examples and comparisons are given to demonstrate our results.

This paper describes the motion planning problems of mobile robots in uncertain dynamic environments with no restrictions on the shape of obstacles. A new approach based on the behavior dynamics of collision-avoidance is proposed to deal with the real-time collision-free motion planning using the local coordinates of the mobile robot. The behavior dynamics of a dynamic collision-avoidance process was modeled, and then the motion-planning problem was transformed into a simple optimization problem with some inequality constraints based on control of the behavior dynamics. The decision-making space of this optimization problem is right the acceleration space of the mobile robot. By solving this optimization problem, the optimal behavior of the mobile robot to avoid obstacles and go to the desired target can be obtained easily. Simulations are given to illustrate the main ideas.