Ye‐Hwa Chen

Abstract Trajectory tracking is one of the most essential tasks in autonomous driving. The driving environment and vehicle dynamics involve numerous complex uncertainties. For four‐wheel independent drive vehicles powered by in‐wheel motors, the torque output capability of each in‐wheel motor is constrained by its rotational speed, resulting in limited control inputs. To address this challenge, a robust control scheme for nonlinear vehicle systems is presented. This scheme aims to achieve satisfactory trajectory tracking performance in complex driving environments while maintaining in‐wheel motor torque output within an expected range. Initially, diffeomorphism theory is employed to handle bounded input constraints in vehicle speed control, addressing the mismatch between control inputs and the operational constraints of in‐wheel motors. A robust control approach based on an input diffeomorphism scheme is developed for longitudinal dynamics. Subsequently, the trajectory tracking problem is elegantly reformulated as an approximate constraint‐following control problem by converting trajectory tracking tasks into equality constraints. A constraint‐following‐based robust control methodology is proposed for vehicle lateral dynamics to address trajectory tracking challenges. Finally, CarSim‐Simulink co‐simulations verify that the proposed strategy achieves accurate trajectory tracking while maintaining in‐wheel motor torque within the maximum execution capacity, despite nonlinear and time‐varying uncertainties.

Motion planning for autonomous vehicles in dense, complex traffic scenarios remains a significant challenge. The key issues lie in the trajectory safety and the path-speed coupling. To address these problems, this paper proposes a Safety-Guaranteed Trajectory Planning algorithm (SGTP) using a gap-oriented spatio-temporal corridor. Firstly, the candidate lanes for the ego vehicle are determined based on road topology and navigation information. Then, considering the uncertainty of surrounding traffic, the risk of each surrounding vehicle is assessed, and their occupied areas are safety-guaranteed according to a calculated risk field, enabling the construction of a dynamic spatio-temporal gap distribution map. By evaluating the cost of gap transitions, a tree-search strategy determines the optimal gap sequence. Subsequently, a spatio-temporal safety corridor is generated around the target gap sequence. Within this corridor, a reference trajectory is initialized using an Intelligent Driver Model integrated with Dynamic Programming (IDM-DP), and a safe, feasible, and smooth trajectory is optimized under the corridor constraints. Finally, SGTP is extensively validated on the nuPlan benchmark dataset. Experimental results demonstrate its outstanding performance in collision avoidance and driving efficiency. Compared to existing methods, SGTP achieves significant improvements in overall performance and safety metrics, highlighting its effectiveness for reliable motion planning in complex, interaction-intensive traffic environments.

This paper addresses the performance regulation of Unmanned Ground Vehicle (UGV) swarm systems, where multiple vehicles travel together to complete a task. The system must satisfy two key performance requirements: maintaining vehicle coalition and avoiding collisions. These requirements are inherently conflicting, as staying close together increases the risk of collisions. Additionally, the system must contend with modeling uncertainties and disturbances. We propose a novel differential homeomorphic approach, introducing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta _{i}$</tex-math> </inline-formula>-performance measure that harmoniously combines these conflicting requirements. Our approach ensures that vehicles remain close to each other without collisions, even under uncertainty. To achieve this, we develop an adaptive robust control scheme that guarantees desirable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta _{i}$</tex-math> </inline-formula>-performance. Furthermore, we optimize the control design parameters using a cooperative game-theoretic framework, establishing the existence, uniqueness, and closed-form solutions of Pareto optimality. Consequently, our approach meets four critical performance criteria for UGV swarm systems: coalition, collision avoidance, robustness, and optimality.

This paper presents a robust constrained control strategy aimed at enhancing the trajectory tracking performance of wheel hub motors, based on their dynamic model. We introduce an innovative state transformation method that utilises the monotonic and unbounded properties of the tangent function to convert constrained output variables of the motors into unconstrained ones. This transformation ensures that the motor's angular displacement output consistently remains within specified limits. By reconfiguring the control variables, we derive a new dynamic model, leading to the proposal of a model-based and error-based control method. The theoretical analysis, using the Lyapunov method, demonstrates that the controller guarantees both uniform boundedness and uniform ultimate boundedness of the system. The effectiveness of the robust controller is validated through simulations and experiments, showing that this strategy effectively mitigates uncertainties and significantly enhances the servo capability and reliability of wheel hub motors.

An unmanned ground swarm (UGS) system consists of multiple vehicles exhibiting intelligent and coordinated behavior through mutual cooperation and information exchange. The system is expected to simultaneously achieve four key objectives: swarm tracking, compact formation, collision avoidance, and obstacle evasion. However, these objectives are inherently conflicting—for instance, maintaining compact formation and ac curate tracking may increase the risk of inter-agent collisions or obstacle encounters. To resolve these conflicts, we propose a novel integrated control framework that unifies the four objectives into a consistent set of constraints. This framework systematically ad dresses the performance trade-offs through a robust and adaptive control scheme capable of handling dynamic agent behaviors and system uncertainties. Robustness is ensured without prior knowledge of the uncertainties, while an adaptive mechanism further reduces the control effort required. Moreover, fuzzy-set theoretic formulation is employed to quantify uncertainty bound, enabling the expression of performance indices that link control parameters to system behavior. These indices are then used in a Pareto-game framework to achieve parameter optimality. The effectiveness of the proposed control design—characterized by its robustness, adaptability, and optimality—is validated through simulations of a UGS team scenario.

ABSTRACT Robotic systems in practical applications are subject to various uncertainties, including sensor measurement errors, parameter variations, and external disturbances, which can compromise system performance and stability. Additionally, ensuring operational safety is crucial to prevent harm to both personnel and the surrounding environment. To address these challenges, this paper proposes a novel robust constraint‐following control method incorporating inequality constraints, based on the Udwadia–Kalaba (U‐K) equation, to enhance the reliability and safety of complex mechanical systems. First, the dynamic equation of selective compliance assembly robot arm (SCARA) robots is derived using the U‐K equation. Then, employing a diffeomorphism approach, the finite state variables are mapped to infinite state variables via a tangent function, ensuring that the joint displacement of the SCARA robot remains within a bounded range. Based on this transformed mathematical model, a robust constraint‐following controller is designed. The Lyapunov method is then utilized to establish the uniform boundedness and uniform ultimate boundedness of the controlled system. Finally, the proposed control algorithm is validated through numerical simulations and experimental testing on a SCARA robot platform.

Abstract This paper presents a novel robust control method for a hip-assist exoskeleton robot’s joint module, addressing dynamic performance under variable loads. The proposed approach integrates traditional PID control with robust, model-based strategies, utilizing the system’s dynamic model and a Lyapunov-based robust controller to handle uncertainties. This method not only enhances traditional PID control but also offers practical advantages in implementation. Theoretical analysis confirms the system’s uniform boundedness and ultimate boundedness. A Matlab prototype was developed for simulation, demonstrating the control scheme’s feasibility and effectiveness. Numerical simulations show that the proposed fractional-order hybrid PD (FHPD) controller significantly reduces tracking error by 58.70% compared to the traditional PID controller, 55.41% compared to the MPD controller, and 32.32% compared to ADRC, highlighting its superior tracking performance and stability.

Dyadic control is a new control frontier, which is to address two (often conflicting) objectives simultaneously. We consider rendering both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">compact formation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collision avoidance</i> in uncertain cooperative road transportation systems. These two tasks, however, present conflicting objectives, where excessive stress on formation tightness may lead to an increased risk of collisions. The tasks are creatively formulated as equality constraints and inequality constraints. Based on the generalized Udwadia-Kalaba (GUK) equation, two independent controllers are developed to handle these constraints, with orthogonality between the control components ensuring no mutual interference. The proposed method guarantees the uniform boundedness and uniform ultimate boundedness of the system, even in the presence of unknown uncertainties. The effectiveness of the control strategy is demonstrated through simulations of a four-vehicle fleet system.

Trajectory tracking in bimanual humanoid robots, whose closed-chain kinematic structures inherently amplify the effects of modeling errors, external disturbances, and time-varying parameters, is a challenging task. To address this, we reformulate the dual-arm tracking task as a servo constraint-following problem and derive the system dynamics under approximate constraints using the Udwadia-Kalaba method. The humanoid system is modeled as a constrained mechanical structure subjected to fast-varying, bounded uncertainties with unknown limits. On this basis, we propose a robust control framework that guarantees both uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the tracking error, ensuring stability and performance even under severe parametric and dynamic uncertainties. To reconcile the trade-off between transient dynamics and steady-state accuracy—essential for service-oriented tasks such as door opening or coffee pouring—we integrate a Nash equilibrium-based optimization mechanism into the controller design. By formulating a two-player non-cooperative game over the controller's key tuning parameters, we analytically derive the existence, uniqueness, and closed-form solutions of the game, achieving an optimal balance between competing objectives. Comprehensive simulations on a reduced-order bimanual humanoid model validate the proposed approach, demonstrating superior tracking accuracy, disturbance rejection, and energy efficiency compared to benchmark methods. The proposed strategy offers a theoretically grounded and practically implementable solution for robust, constraint-compliant humanoid manipulation.

Abstract In various fields such as military, agriculture, and transportation, unmanned ground vehicle (UGV) has brought significant economic and social benefits. This study proposes a ternary control design for the UGV swarm system to accomplish formation and trajectory tracking tasks. First, the tunable kinematic model of the UGV swarm system is established using two improved artificial potential functions (APFs). Second, based on the Udwadia–Kalaba (U‐K) bridging approach, the analytical expression for the control input of the UGV swarm system's dynamic model is obtained. The control design is skillfully transformed into a constraint‐following problem. Then, considering the system's uncertainties, the ternary control input is derived by integrating the adaptive robust controller. Finally, a swarm system consisting of six heterogeneous UGVs is used as an example to validate the control algorithm's effectiveness and flexibility through simulations. In contrast to the robust control, the ternary control outperforms in terms of both convergence time and magnitude of the control error. Furthermore, the real‐time performance and robustness of the ternary control are validated on an embedded controller (MicroAutoBox II, DS1401) with limited resources. This paper offers a practical control design method for swarm system with predefined constraints and uncertainties.

A More Electric Aircraft (MEA) is an innovative aircraft design that integrates secondary onboard energy sources into an electric system, offering significant advantages in fuel efficiency, reliability, and maintainability. A key enabler of the MEA concept is the development of advanced electric actuation systems, which are critical for optimizing aircraft performance. However, these systems frequently face challenges related to uncertainties and actuator failures in complex operational environments. To address these issues, this paper proposes an integrated fault-tolerant control approach that combines dynamic modeling, uncertainty analysis, fault compensation, and nonlinear robust control design. Specifically, a robust fault-tolerant control strategy is developed to mitigate actuator faults and system uncertainties. The proposed approach is validated through theoretical analysis and numerical-hydraulic co-simulations, demonstrating its effectiveness in improving system stability and fault resilience. Co-simulation results confirm that the method significantly enhances control performance under multiple uncertainties, providing a practical solution for MEA actuation systems.

In this paper, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is proposed to address the challenge of parameter tuning in traditional PID controllers for artillery stabilization systems. The proposed algorithm introduces chaotic mapping to optimize the initial population, enhancing the global search capability; additionally, a sigmoid function and Lévy flight-based dynamic adjustment strategy regulate the selection factor and step size, improving both convergence speed and optimization accuracy. Comparative experiments using five benchmark test functions demonstrate that the IMRFO algorithm outperforms five commonly used heuristic algorithms in four cases. The proposed algorithm is validated through co-simulation and physical platform experiments. Experimental results show that the proposed approach significantly improves control accuracy and response speed, offering an effective solution for optimizing complex nonlinear control systems. By introducing heuristic optimization algorithms for self-tuning artillery stabilization system parameters, this work provides a new approach to enhancing the intelligence and adaptability of modern artillery control.

Abstract The intelligent vehicle corner module system, which integrates four-wheel independent drive, independent steering, independent braking and active suspension, can accurately and efficiently perform vehicle driving tasks and is the best carrier of intelligent vehicles. Nevertheless, too many angle/torque control inputs make control difficult and non-real-time. In this paper, a hierarchical real-time motion control framework for corner module configuration intelligent electric vehicles is proposed. In the trajectory planning module, an improved driving risk field is designed to describe the surrounding environment's driving risk. Combined with the kinematic vehicle-road model, model predictive control (MPC) method, spline curve method, the local reference trajectory of safety, comfort and smoothness is planned in real time. The optimal steering angle is determined using MPC method in path tracking module. In the motion control module, a feedforward-feedback controller assigns the optimal steering angle to the front/rear axles, and an angle allocation controller distributes the target angles of the front/rear axles to four steered wheels. Finally, the PreScan-Simulink-CarSim joint simulation environment is established for conducting the human-in-the-loop emergency obstacle avoidance experiment. It took only 0.005 s for the hierarchical motion control system to determine its average solution time. This proves the effectiveness of the hierarchical motion control system.

Cyber-physical system (CPS) is a complex system that integrates cyber, computer system, and physical system. Due to the large amount of information transmitted by CPS in real time, there are physical uncertainty and serious security risks, so how to accurately and effectively realize the accurate control of the CPS becomes a challenging task. In this paper, we comprehensively consider the physical uncertainty and cyber interference that the CPS may face, and then design a Servo Robust Control (SRC). The control design is divided into two phases. In the first phase, a novel control scheme is proposed to ensure that the system can maintain stable performance in the face of physical uncertainty and cyber interference. The second phase is the optimal design of control parameters. Since the selection of control parameters seriously affects the performance of the system, multi-objective parameter optimization methods (non-cooperative game and Stackelberg strategy) are used to study the optimal selection of control parameters. Finally, the proposed SRC is applied to a typical CPS (i.e., autonomous vehicle) for verification. The effectiveness and superiority of this method are verified by comparing with other control methods.

ABSTRACT A robust approximate constraint‐following control (RACC) approach is proposed in this article for collaborative robots with inequality constraints. The trajectory‐following control and boundary control of the robot are investigated. First, an explicit constraint equation for the collaborative robot system is established based on the Udwadia–Kalaba (U‐K) theory. Second, due to the monotone unbounded property of the tangent function, a special function is constructed to transform the joint output angles of the constrained robot into unconstrained state variables, and a new form of the robot constraint equation is obtained. Through this transformation, the joint motion of the robot will always be confined to specified angles and follow the desired trajectory. The constraint equation ensures the safety of the robot at the algorithmic level and innovatively solves the control problem of the equality and inequality of the robot's motion. According to theoretical analysis, the control approach can deal with uncertainty and satisfy both uniform boundedness (UB) and uniform ultimate boundedness (UUB) requirements. Finally, based on the rapid controller prototype CSPACE and a two‐degree‐of‐freedom collaborative robot platform, experimental verification is carried out. Numerical simulation and experimental results demonstrate that the proposed RACC approach with state transformation exhibits significant advantages in trajectory tracking performance and safety for collaborative robots.

A novel robust control strategy is proposed in this work to address the dynamic control problem of permanent magnet synchronous motors (PMSM) position tracking and lessen the effect of system parameter and load fluctuations on the dynamic performance of PMSM. The tracking performance is improved by a robust control element built with the Lyapunov method to reduce the impact of uncertain factors such as parameter uncertainty, nonlinear friction, and external interference; the nominal control element is stabilized by the dynamics model. The uniformly bounded and uniformly final bounded systems are proven, and the associated conclusions are provided using the Lyapunov minimax approach. In this work, modeling and experimental investigation are conducted using the cSPACE fast controller, based on the permanent magnet synchronous motor test platform. The results of the testing and simulation show that the developed controller can effectively regulate the permanent magnet synchronous motor and achieve more accurate position tracking even in the face of ambiguity.

This work addresses the position tracking control design of the stator vane driven by electro-hydrostatic actuators facing uncertain aerodynamic disturbances. Rapidly changing aerodynamic conditions impose complex disturbance torques on the guide vanes. Consequently, a challenging task is to enhance control precision in complex uncertain environments. Inspired by the principles of mammalian muscle movement, a novel robust control strategy based on the backstepping method has been proposed. Using backstepping, virtual rotational speed and virtual pressure difference force are designed, which decompose the high-order position closed-loop control problem into three lower-order parts, eliminating the need for matching conditions. Subsequently, robust controllers were designed, and stability proofs and performance analyses of the controllers were provided. This control strategy was tested through numerical hydraulic simulation. The results show that compared to other control methods, this approach significantly improves tracking accuracy and robustness. Therefore, it is believed that this method has the potential to become a new generation solution for such problems.

This paper introduces an adaptive robust control approach tailored for underactuated mechanical systems encountering matched and mismatched uncertainty, employing a constraint-following methodology. The control strategy unfolds in two phases: initially, a nominal control scheme is devised neglecting uncertainty and deviations in initial conditions from constraints. Subsequently, uncertainty is categorized into matched and mismatched components, ensuring that mismatched uncertainties remain unobservable. Leveraging the structural characteristics of the uncertainty bound, a novel segmented adaptive law is proposed and seamlessly integrated into the adaptive robust control framework. By employing the Lyapunov minimax approach, the method ensures uniform boundedness and uniform ultimate boundedness simultaneously, thereby ensuring approximate adherence to constraints for underactuated mechanical systems facing both matched and mismatched uncertainties alongside initial condition deviations.

Human-machine systems (HMSs) are dedicated to integrating intelligent human decisions with machine operations to achieve synergistic operational functionality. We focus on constraint-following control within the HMS, considering potential uncertainties, environmental disturbances, and limited operational space. A hierarchical hybrid control scheme is proposed, consisting of a preemption algorithm and a human decision algorithm. Specifically, the preemption algorithm relies on online state feedback from mechanical system signals, such as position and velocity, which can be implemented in hardware or software; the human decision algorithm takes inputs from electrophysiological signals or language commands. In this development, a Lagrangian density function is constructed that integrates optimal decision making with a uniformly bounded threshold. The intelligent decision-making problem in the HMS is creatively analyzed and mathematically solved leveraging variational calculus, resulting in the analytical expression of the optimal membership function associated with human decisions. Furthermore, a series of numerical simulation experiments are conducted using a bionic upper-limb prosthetic system as an example, and the comparison results demonstrate the superiority and effectiveness of the proposed method.

Abstract The paper presents a novel control method aimed at enhancing the trajectory tracking accuracy of two-link mechanical systems, particularly nonlinear systems that incorporate uncertainties such as time-varying parameters and external disturbances. Leveraging the Udwadia–Kalaba equation, the algorithm employs the desired system trajectory as a servo constraint. First, the system’s constraints to construct its dynamic equation and apply generalized constraints from the constraint equation to an unconstrained system. Second, we design a robust approximate constraint tracking controller for manipulator control and establish its stability using Lyapunov’s law. Finally, we numerically simulate and experimentally validate the controller on a collaborative platform using model-based design methods.

This paper explores the application of non-cooperative game theory in achieving optimal prescribed performance control for uncertain underactuated mechanical systems. The study addresses scenarios characterized by time-varying (potentially rapid) and bounded uncertainties. Utilizing a constraint-following control design approach., we apply the prescribed transient and steady-state performance (TSSP) criteria to the constraint-following error. Additionally., we employ state transformation techniques to implement the prescribed performance control. We introduce a prescribed performance control methodology with two adjustable parameters and demonstrate the process of determining the optimal design for these parameters through Nash games. Our findings indicate a significant enhancement in the overall system performance., thus confirming the efficacy of the optimization method grounded in non-cooperative game theory.

In this article, the trajectory generation and motion control of autonomous driving oversized cargo cooperative transportation systems (CTS) in static but bounded environment is investigated. Different from common vehicle systems, the challenges lie on the safety-guaranteed cooperation of independently controlled carriers with inherent connections brought by the rigid payload, which results in complex system dynamics and multiple time-variant uncertainties. A constraint-oriented “leader-follower” modeling and control framework is introduced, and a trajectory generation method based on the diffeomorphism is creatively proposed to generate closed-form collision-free trajectory for the payload in the bounded environment. To achieve safety-guaranteed trajectory following under uncertainties, a transformed adaptive robust control strategy (TARC) is designed through constraint relaxation, and the coordination of the carriers is realized. An implementation with comprehensive ablation studies demonstrates the effectiveness of our trajectory generation and tracking control framework. The collision-free trajectory set is efficiently generated, and the CTS can be kept strictly inside the safe corridor with high tracking accuracy, which is extremely hard for the baseline methods.

A Stackelberg game-based design for an adaptive robust control for the fuzzy uncertain underactuated mechanical systems (UMSs) is proposed. The emphasis is on fuzzy-based uncertainty and inequality constraint. The uncertainty is time varying and bounded within a prescribed fuzzy set. For the inequality constraint, we creatively have it merge into constraint-following performance by a diffeomorphism technique. An adaptive robust control strategy is then proposed. Deterministic performance is guaranteed provided the control design parameters are within feasible regions. To further enhance the performance, we introduce a two-player Stackelberg game setting. The optimal choice of design parameters can be solved. The feasibility of this design is demonstrated on an autonomous wheeled mobile robot (AWMR), which is confined in a bounded space.

In this paper, derived from the proportional-derivative control and robust control, a novel practical robust control method based on a dynamic feedforward model is established by taking six-axis motion cooperative industrial robots as the research object. The nonlinear friction, parameter uncertainty, and external disturbance are taken into consideration while establishing the dynamic model of the cooperative industrial robot. The method includes a proportional-derivative control and a robust control. Lyapunov theory is used to analyze the proposed controller, and it is shown that this method can guarantee uniformly bounded and uniformly final bounded systems. Simulation and experiment results show that the proposed controller is better than proportional-integral-derivative control and mode-based proportional-derivative control in stability tracking performance and robustness. In addition, the CSPACE platform for rapid controller prototyping may reduce the arduous programming effort and offer a lot of ease for the trials.

The revolution of physical structure is highly significant for future software defined vehicles (SDV). Active structural transformation is a promising feature of the next generation of vehicle physical structure. It can enhance the dynamic performance of vehicles, thus providing safer and more comfortable ride experiences, such as the ability to avoid rollover in critical situations. Based on the active structural transformation technology, this study proposes a novel approach to improve the dynamic performance of a vehicle. The first analytical motion model of a vehicle with active structural transformation capability is established. Then, a multi-objective optimization problem with the adjustable parameters as design variables is abstracted and solved with an innovative scenario specific optimization method. Simulation results under different driving scenarios revealed that the active transformable vehicle applying the proposed method could significantly improve the handling stability without sacrificing the ride comfort, compared with a conventional vehicle with a fixed structure. The proposed method pipeline is defined by the software and supported by the hardware. It fully embodies the characteristics of SDV, and inspires the improvement of multiple types of vehicle performance based on the concept of "being defined by software" and the revolution of the physical structure.

Many robotic mechanical systems exhibit nonlinearity and are frequently subject to uncertainties. This paper introduces a distinctive and practical robust control algorithm, developed using the Udwadia-Kalaba (UK) equation. By defining the trajectory of the ideal system, the algorithm incorporates the servo constraints. The controller proposed in this paper is composed of three primary components. The first part of the controller provides an analytical expression for the binding force of the system under servo constraints. The second part of the controller addresses the issue of initial condition incompatibility within the system. The third part serves as a robust component to effectively compensate for the impact of uncertainties. Finally, the proposed robust control method is validated through MATLAB simulation on a SCARA robot, incorporating the system’s dynamics model. The simulation results demonstrate the controller’s superior dynamic performance when compared to alternative control algorithms.

Cyber-physical systems (CPSs) are a kind of systems integrating cyber and physical components, which may be subject to physical uncertainty and cyber interference in the process of operation. In order to solve this problem, a new control design method is proposed by studying the constrained following problem of controlled systems under physical uncertainty and cyber interference. The remarkable feature of this control is that both physical uncertainty and cyber interference are taken into account, which ensures that the controlled system can resist cyber interference while still meeting the expected constraints. In addition, the effectiveness and superiority of the proposed control method are verified by the simulation comparison with sliding mode control (SMC) in the autonomous vehicle scenario.

In order to achieve the stability and safety of the autonomous vehicle while reducing the control cost, a driving strategy for uncertain autonomous vehicle is presented. There are many uncertainties in the autonomous vehicle driving, and the boundary of the uncertainty maybe unknown. To solve this problem, a gated leakage type adaptive robust control based on the tracking deviation is developed. The salient feature of the control lies in the novel leakage mechanism designs. The leakage mechanism is designed to provide gated value for the leakage to prevent excessive control effort. The control system ensures the performance of the autonomous vehicle in terms of lateral and yaw displacement, which in turn prevents the vehicle from sideslip even in uncertainty. Compared with the constant leakage type adaptive robust control and Linear Quadratic Regulator (LQR) control, the effectiveness and superiority of the proposed control method are verified.

In order to improve the control performance of the Cyber-Physical Systems (CPSs), an integrated modelling-control-design trio framework is established in this paper. In the modelling part, CPS has two components, cyber component and physical component, so the system may be subject to (possibly fast) time-varying cyber interference and (possibly fast) time-varying physical uncertainty. A dynamic model encompassing these two phases of the CPS is established. In the control part, a novel control design is proposed based on the dynamic model. The problem of constraint-following for CPS operating under cyber interference and physical uncertainty is considered. In the design part, the choice of control parameters is investigated. This procedure consists of two stages. The first stage is to design a control scheme based on feasible control design parameters, so that it can guarantee the performance in the case of both cyber interference and physical uncertainty. The second stage is to seek the optimal design among the feasible control design parameters, which is resolved by an intelligent multi-agent game-theoretic approach. We invoke both Nash game and Stackelberg strategy to choose the optimal parameters. Interestingly, the optimal parameters obtained from different game settings are the same. This shows the conception of optimality we established spans in a broader context. The robustness and superiority of the system performance are demonstrated in the intelligent transportation system.

Agile formation (AF) is a new frontier for intelligent swarm system formation. The AF pertains to perform various tasks in short phases of work and frequent reassessment and adaptation of plans. This greatly increases the applicability of swarm systems. There are however two major challenges for the control design: smooth task transitions and guaranteed collision avoidance. We adopt the constraint-following approach to address these. First, for agile formation, a plateau activation function is proposed to generate a sequence of consecutive and disjoint formations. For collision avoidance, a distance-gauge function is proposed. Second, by taking the objectives of agile formation control and collision avoidance as desirable constraints, the agile formation together with collision avoidance are both cast into a constraint following control problem. Third, to evaluate the constraint-following error, a performance measure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> is introduced and then an agile formation control is designed to render the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-measure to be asymptotically convergent to zero. By this, the swarm system can follow the agile formation constraint and collision avoidance constraint. Therefore, agile formation and collision avoidance are both accomplished.

Abstract A morphing quadrotor unmanned aerial vehicle (QUAV) possesses the remarkable ability to alter its shape, enabling it to navigate through gaps smaller than its wingspan. However, these deformations result in changes to the system's center of gravity and moment of inertia, necessitating real-time computation of each state's variations. To address this challenge, we propose a dynamic modeling approach based on the Udwadia−Kalaba (U-K) method. The morphing QUAV is divided into three separate subsystems, with the dynamic modeling for each subsystem conducted independently. Subsequently, the QUAV's deformation states and inherent structure are introduced in the form of constraints, and the constrained forces are derived using the U-K equation. By combining these analytical solutions, the model of the QUAV under continuous deformation is obtained. This approach effectively simplifies the modeling computations caused by changes in the system's center of gravity and moment of inertia during deformation. A control approach is proposed to achieve attitude stabilization and altitude control for the morphing QUAV. Ultimately, the stable motion of the morphing QUAV is validated through numerical simulations.

Since the actual environment of mechanical systems is not ideal, there will be some unstable factors, namely uncertainty. Such uncertainty is changeable and bounded, but its boundary is usually uncertain. To describe the uncertain boundary, the fuzzy set theory is used in this paper, which is one of the innovations of this paper. On this basis, an adaptive robust control method based on two control parameters is proposed, which is for the fuzzy mechanical systems. It is proved by Lyapunov function that this control approach can ensure the global uniform boundedness (GUB) and global uniform ultimate boundedness (GUUB) performance of the controlled mechanical system. This is the first level of the control design. Afterwards, the second level is to ensure the control performance while reducing the control cost by optimizing two control parameters. In order to optimize two control parameters, the cooperative game theory is adopted, which is another one of the innovations of this paper. In the optimization process, two control parameters are regarded as two players, and the optimal control parameters are obtained by minimizing the cost function designed based on these two players. Finally, a serial robot model is adopted to verify the feasibility and superiority of the proposed control approach.